Name: Master Business Networking in Just 1 Day - Join our Workshop in York
Price: 595 GBP
Availability: InStock

Corby

<p id="isPasted" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course Overview:</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">According to a 1973 survey by the Sunday Times of London, 41% of people list public speaking as their biggest fear. Forget small spaces, darkness, and spiders, standing up in front of a crowd and talking is far more terrifying for most people. Through this workshop you will become more confident and relaxed in front of an audience which will translate into a successful speaking event.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">However, mastering this fear and getting comfortable speaking in public can be a great ego booster, not to mention a huge benefit to your career. The Public Speaking workshop will give participants some public speaking skills, including in-depth information on developing an engaging program and delivering their presentation with power.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Learning Objectives:</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Inside This Training, You&#39;ll Learn</p><ul type="disc" fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Completely remove your stage fear and become a dynamic speaker</li><li fr-original-style="" style="box-sizing: border-box;">Raise your confidence up to a level you never thought to be possible!</li><li fr-original-style="" style="box-sizing: border-box;">Deeper insights into group dynamics</li><li fr-original-style="" style="box-sizing: border-box;">Presentation design, skills &amp; styles</li><li fr-original-style="" style="box-sizing: border-box;">Tools to get your audience to say yes to you</li><li fr-original-style="" style="box-sizing: border-box;">Advanced new behavior patterns required for public speaking</li><li fr-original-style="" style="box-sizing: border-box;">Control time for effective training</li><li fr-original-style="" style="box-sizing: border-box;">Secrets of mind control techniques</li><li fr-original-style="" style="box-sizing: border-box;">Develop your personal winning style</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Prerequisites:</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">None</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course Materials:</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Attendees will receive a course manual with presentation slides and reference materials.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Agenda:</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Module One: Identifying Your Audience</strong></p><ul type="disc" fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Performing a Needs Analysis</li><li fr-original-style="" style="box-sizing: border-box;">Creating an Audience Profile</li><li fr-original-style="" style="box-sizing: border-box;">Identifying Key Questions and Concerns</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Module Two: Creating a Basic Outline</strong></p><ul type="disc" fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Outlining the Situation</li><li fr-original-style="" style="box-sizing: border-box;">Identifying the Task That Had to Be Performed</li><li fr-original-style="" style="box-sizing: border-box;">Listing the Actions You Took</li><li fr-original-style="" style="box-sizing: border-box;">Revealing the Results</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Module Three: Organizing the Program</strong></p><ul type="disc" fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Making Organization Easy</li><li fr-original-style="" style="box-sizing: border-box;">Organizational Methods</li><li fr-original-style="" style="box-sizing: border-box;">Classifying and Categorizing</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Module Four: Fleshing It Out</strong></p><ul type="disc" fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Identifying Appropriate Sources</li><li fr-original-style="" style="box-sizing: border-box;">Establishing Credibility</li><li fr-original-style="" style="box-sizing: border-box;">The Importance of Citations</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Module Five: Putting It All Together</strong></p><ul type="disc" fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">&nbsp;Writing Your Presentation</li><li fr-original-style="" style="box-sizing: border-box;">Adding a Plan B</li><li fr-original-style="" style="box-sizing: border-box;">Reviewing, Editing, and Rewriting</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Module Six: Being Prepared</strong></p><ul type="disc" fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Checking Out the Venue</li><li fr-original-style="" style="box-sizing: border-box;">Gathering Materials</li><li fr-original-style="" style="box-sizing: border-box;">A 24 Hour Checklist</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Module Seven: Overcoming Nervousness</strong></p><ul type="disc" fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">A Visit from the Boss</li><li fr-original-style="" style="box-sizing: border-box;">Preparing Mentally</li><li fr-original-style="" style="box-sizing: border-box;">Physical Relaxation Techniques</li><li fr-original-style="" style="box-sizing: border-box;">Appearing Confident in Front of the Crowd (Even If You Don&acirc;&euro;&trade;t Feel That Way)</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Module Eight: Delivering Your Speech (I)</strong></p><ul type="disc" fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Starting Off on the Right Foot</li><li fr-original-style="" style="box-sizing: border-box;">Using Visual Aids</li><li fr-original-style="" style="box-sizing: border-box;">Checking the Volume of Your Voice</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Module Nine: Delivering Your Speech (II)</strong></p><ul type="disc" fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Adjusting on the Fly</li><li fr-original-style="" style="box-sizing: border-box;">Gauging Whether Breaks Are Required</li><li fr-original-style="" style="box-sizing: border-box;">Wrapping Up and Winding Down</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Module Ten: Questions and Answers</strong></p><ul type="disc" fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Ground Rules</li><li fr-original-style="" style="box-sizing: border-box;">Answering Questions That Sound Like an Attack</li><li fr-original-style="" style="box-sizing: border-box;">Dealing with Complex Questions</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Module Eleven: Wrapping Up</strong></p><ul type="disc" fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Words from the Wise</li><li fr-original-style="" style="box-sizing: border-box;">Review of Parking Lot</li><li fr-original-style="" style="box-sizing: border-box;">Lessons Learned</li><li fr-original-style="" style="box-sizing: border-box;">Completion of Action Plans and Evaluations</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Target Audience:</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Anybody who is interested in Public Speaking Skills.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Technical Requirements:</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">For eBooks:</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Internet for downloading the eBook</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Laptop, tablet, Smartphone, eReader (No Kindle)</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Adobe DRM supported software (e.g. Digital Editions, Bluefire Reader)</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">eBook download and activation instructions</p>

Certificate: Course Completion Certificate | Language: English
Duration: 1 Day | Credits: 8 PDUs
Course Delivery: Classroom | Virtual Live
Lunch and Beverages are included in a classroom session
Course Overview:
This networking skills training will help you to build a professional reputation and develop a network of connections through the two key methods available to you; face-to-face and online.
You will discover how to effectively combine these two approaches and create a strategy for networking success that will generate your own community and actively grow your personal network. This will result in an improved performance in your approach to networking and consequently provide opportunities and prospects that will increase your business success.
The training provides you with the tools and techniques to both plan a successful, strategic approach to networking and develop your personal communication skills, which will ensure you build effective business relationships.
Course Contents:
Effective Networking Strategies and Techniques – Business Networking Training
Networking has become a crucial factor in the modern workplace. It is the most effective method of developing your business or career and is a key component of your personal brand. You need to be able to build strong business relationships, develop a level of personal credibility and increase your network of contacts in order to enhance your visibility and create opportunities for yourself and your organisation.
Perhaps you’re looking to improve the networking performance of a management or sales team. Or you want to include networking skills training as part of management and leadership training programmes. Whatever your need, We provide you with a comprehensive networking skills training course to make your life easy.
This business networking training is aimed at helping you develop your business or career. You will help ensure you have the skills, knowledge and techniques to develop a joined-up networking strategy that gets you the outcomes you want.
You will also be encouraged to develop the correct behaviours and attitudes to help build your visibility and credibility through networking so that you develop strong working relationships that last.
Course Topics:
Why Network? – Establishing the importance of networking with some shocking statistics and an activity that defines the importance of networking to the individual. This is followed by some insight into the key aims of a networking strategy, what puts people off as well as some personal revelations about networking
Increase Your Business Presence – Looking at the components of a strong business presence, including; credibility, personal brand, visibility and social capital.
Developing a Networkers Attitude – An exercise that clarifies the perfect attitudes and behaviours of great networkers and comparing the participants against this. Thus identifying areas for development.
Face to Face Networking – An overview and insight into the main types of face to face networking opportunities available with the benefits and pitfalls of each.
Online Networking – An overview of the different social media and online networking tools and how to get the best out of them. Includes; LinkedIn, Twitter, Facebook and online forums.
Communication Skills for Networking Success – Ensuring a good first impression, creating an opening sound bite and writing an online profile that promotes and engages.
Developing a Conversation – Building on the opening sound bite and developing a conversation through good questioning techniques and following a selection of key networking rules. Including an activity to test and practice the skills.
Working a Room – A fun activity that embeds and develops learning while encouraging practice and communication. This activity covers 10 of the main skills required to work a room.
Breaking into a Group – Identifying body language to spot when a group or pair is open to new people entering the conversation or would rather not encourage participation. Knowing how to break into a group, what to say and how to remain professional.
Exiting a Conversation – Developing some techniques to extricate oneself from a conversation without causing embarrassment or rudeness.
The Follow Up – Using a structure to identify the most important contacts created and establishing when and how to follow up with each group. Includes example follow up email and a chance to develop their own.
Creating a Network Plan – Participants are provided with a networking action plan to review and complete. This sets them on the journey of developing their networking skills and ensures they have a strategy and goals to target.
This training is highly interactive and includes lots of activities and exercises to stimulate discussion and develop learning opportunities.
Networking has become a crucial factor in the modern workplace. It is the most effective method of developing your business or career and is a key component of your personal brand. You need to be able to build strong business relationships, develop a level of personal credibility and increase your network of contacts in order to enhance your visibility and create opportunities for yourself and your organisation.
Perhaps you’re looking to improve the networking performance of a management or sales team. Or you want to include networking skills training as part of management and leadership training programmes. Whatever your need, We will provide you with a comprehensive networking skills training course to make your life easy.
Course Feature:
At the end of this business networking skills training, you will be able to:
Develop a successful networking strategy
Maximise their effectiveness with face-to-face and online networking opportunities
Improve their approach, attitude and behaviours when networking
Create a plan for networking that will help them meet their goals
Certification:
Once after the training you receive course completion certificate from Mangates
Who can Attend?
Anybody who is interested in learning Business Networking Skills.

Mangates

Networking Skills

Business & Marketing

Business Networking 1 Day Training in Corby

Master Business Networking in Just 1 Day - Join our Workshop in Corby

The sale ends 5 days before the event starts.

Early Bird Fee

The sale ends 10 days before the event starts.

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Business Networking 1 Day Training in Chichester

Master Business Networking in Just 1 Day - Join our Workshop in Chichester

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Master Business Networking in Just 1 Day - Join our Workshop in Burton Upon Trent

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Harrogate

Business Networking 1 Day Training in Harrogate

Master Business Networking in Just 1 Day - Join our Workshop in Harrogate

Glasgow

Business Networking 1 Day Training in Glasgow

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Business Networking 1 Day Training in Wokingham

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Business Networking 1 Day Training in Warrington

Master Business Networking in Just 1 Day - Join our Workshop in Warrington

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			<h2>Overview</h2>		</div>
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					<div><p><span>Join our Statistics course and discover your hidden skills, setting you on a path to success in this area. Get ready to improve your skills and achieve your biggest goals. The Statistics course has everything you need to get a great start in this sector.</span></p><p><span>Improving and moving forward is key to getting ahead personally. The Statistics course is designed to teach you the important stuff quickly and well, helping you to get off to a great start in the field. So, what are you looking for? Enrol now!</span></p></div>
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					<div><p><span>This course covers everything you must know to stand against the tough competition. The future is truly yours to seize with this Statistics. Enrol today and complete the course to achieve a certificate that can change your career forever.</span></p></div>
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					<div><p><span>This course covers everything you must know to stand against the tough competition. The future is truly yours to seize with this Statistics. Enrol today and complete the course to achieve a certificate that can change your career forever.</span></p></div>
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			<h4>Process of Evaluation</h4>  		</div>
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					<div><p>After studying the course, your skills and knowledge will be tested with an MCQ exam or assignment. You have to get a score of 60% to pass the test and get your certificate.</p></div>
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					<div><p><span>After completing the </span><b>Statistics course</b><span>, you will receive your</span><b> CPD-accredited Digital/PDF Certificate for £5.99. </b><span>To get the </span><b>hardcopy certificate for £12.99, </b><span>you must also pay the shipping charge of just</span><b> £3.99 (UK) and £10.99 (International).</b></p></div>
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					<div><p><span>This Statistics is suitable for anyone aspiring to start a career in relevant field; even if you are new to this and have no prior knowledge, this course is going to be very easy for you to understand. </span></p><p><span>On the other hand, if you are already working in this sector, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level. </span></p><p><span>This course has been developed with maximum flexibility and accessibility, making it ideal for people who don't have the time to devote to traditional education.</span></p></div>
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					<div><p><span>There is no prerequisite to enrol in this course. You don't need any educational qualification or experience to enrol in the Statistics course.</span></p><p><span>Do note: you must be at least 16 years old to enrol.</span></p><p><span>Any internet-connected device, such as a computer, tablet, or smartphone, can access this online course.</span></p></div>
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					<div><p><span>The certification and skills you get from this </span><span>Statistics Course</span><span> can help you advance your career and gain expertise in several fields, allowing you to apply for high-paying jobs in related sectors.</span></p></div>
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					<td>Sampling</td>
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					<td><span><i></i> 00:19:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 07: Charts and Graphs</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Charts and Graphs</td>
					<td> </td>
					<td><span><i></i> 00:21:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 08: Hypothesis Testing </td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Hypothesis Testing</td>
					<td> </td>
					<td><span><i></i> 00:25:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 09: Ten Common Statistical Mistakes </td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Ten Common Statistical Mistakes</td>
					<td> </td>
					<td><span><i></i> 00:30:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Assignment</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Assignment - Statistics</td>
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
				</tr>
				</tbody></table></div>

			</div>
									</div>
						</div>
						<div id="menu2">
							
						</div>

Overview
Join our Statistics course and discover your hidden skills, setting you on a path to success in this area. Get ready to improve your skills and achieve your biggest goals. The Statistics course has everything you need to get a great start in this sector.
Improving and moving forward is key to getting ahead personally. The Statistics course is designed to teach you the important stuff quickly and well, helping you to get off to a great start in the field. So, what are you looking for? Enrol now!
You Will Learn Following Things:
Learn strategies to boost your workplace efficiency.
Hone your skills to help you advance your career.
Acquire a comprehensive understanding of various topics and tips.
Learn in-demand skills that are in high demand among UK employers
This course covers everything you must know to stand against the tough competition. The future is truly yours to seize with this Statistics. Enrol today and complete the course to achieve a certificate that can change your career forever.
Details

Perks of Learning with IOMH
One-to-one support from a dedicated tutor throughout your course.
Study online - whenever and wherever you want.
Instant Digital/ PDF certificate
100% money back guarantee
12 months access
This course covers everything you must know to stand against the tough competition. The future is truly yours to seize with this Statistics. Enrol today and complete the course to achieve a certificate that can change your career forever.
Process of Evaluation
After studying the course, your skills and knowledge will be tested with an MCQ exam or assignment. You have to get a score of 60% to pass the test and get your certificate.
Certificate of Achievement
After completing the Statistics course, you will receive your CPD-accredited Digital/PDF Certificate for £5.99. To get the hardcopy certificate for £12.99, you must also pay the shipping charge of just £3.99 (UK) and £10.99 (International).
Who Is This Course for?
This Statistics is suitable for anyone aspiring to start a career in relevant field; even if you are new to this and have no prior knowledge, this course is going to be very easy for you to understand. 
On the other hand, if you are already working in this sector, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level. 
This course has been developed with maximum flexibility and accessibility, making it ideal for people who don't have the time to devote to traditional education.
Requirements
There is no prerequisite to enrol in this course. You don't need any educational qualification or experience to enrol in the Statistics course.
Do note: you must be at least 16 years old to enrol.
Any internet-connected device, such as a computer, tablet, or smartphone, can access this online course.
Career Path
The certification and skills you get from this Statistics Course can help you advance your career and gain expertise in several fields, allowing you to apply for high-paying jobs in related sectors.
 Course Curriculum

Module 01: Introduction to Statistics			
	Introduction to Statistics		00:18:00
Module 02: Measuring Central Tendency			
	Measuring Central Tendency		00:18:00
Module 03: Measures of Dispersion			
	Measures of Dispersion		00:11:00
Module 04: Correlation and Regression Analysis			
	Correlation and Regression Analysis		00:35:00
Module 05: Probability			
	Probability		00:16:00
Module 06: Sampling			
	Sampling		00:19:00
Module 07: Charts and Graphs			
	Charts and Graphs		00:21:00
Module 08: Hypothesis Testing			
	Hypothesis Testing		00:25:00
Module 09: Ten Common Statistical Mistakes			
	Ten Common Statistical Mistakes		00:30:00
Assignment			
	Assignment - Statistics		00:00:00

IOMH - Institute of Mental Health

Statistics

Maths

Teacher

Professional Development

General Admission

<p>Did you know that the demand for statisticians in the UK is projected to increase by 34% by 2026? In fact, statistics is one of the fastest-growing fields in the country, with an estimated 58,000 new jobs expected to be created in the next decade. This makes it a great time to invest in your future by enrolling in the Statistics QLS Level 3 &amp; 4 course.</p> <p>Through this course, you will gain a comprehensive understanding of statistical concepts and methods, including probability theory, regression analysis, hypothesis testing, and more. With a curriculum that spans from basic to higher knowledge, the course is designed to meet the needs of learners at all levels, from beginners to advanced users.</p> <p>The Statistics QLS Level 3 &amp; 4 course is endorsed by Quality Licence Scheme, which means that you can be assured of the highest quality of education and training. Upon completion of the course, you will receive a Level 4 Endorsed Certificate hardcopy free of charge, which can greatly enhance your career prospects in fields such as finance, healthcare, marketing, and more.</p> <p>In addition to the Level 4 Endorsed Certificate, learners who enrol in the Statistics QLS Level 3 &amp; 4 course will receive <strong>5 career-guided courses free</strong> with the QLS course, including career development plan fundamentals, networking skills for personal success, boost your confidence and self-esteem, CV writing and job searching, and learn to reduce and manage stress.</p> <p>So why wait? Take the first step towards a brighter future and enrol in the Statistics QLS Level 3 &amp; 4 course today. Whether you're looking to enhance your career prospects or simply want to gain a deeper understanding of statistics, this course has everything you need to succeed.</p> <p><strong>Learning Outcomes:</strong></p> <ul> <li>Develop an understanding of the fundamental principles of statistics, including the measurement of central tendency and dispersion.</li><li>Gain knowledge of correlation and regression analysis, including how to interpret statistical relationships between variables.</li><li>Understand probability and its application in statistical analysis.</li><li>Learn about sampling methods and their use in statistical analysis.</li><li>Develop the ability to create and interpret charts and graphs for data analysis.</li><li>Gain knowledge of hypothesis testing and how to apply statistical tests to analyse data.</li><li>Identify and avoid common statistical mistakes in data analysis.</li></ul><p><strong>Diploma in Statistics - QLS Level 3 &amp; 4's Description:</strong></p> <p><strong>Level 3</strong> curriculum provides an introduction to statistics, covering the fundamental principles of measurement of central tendency, dispersion, correlation, and regression analysis. Students will learn how to apply statistical analysis to a range of data sets. They will also gain knowledge of how to create meaningful conclusions and interpretations from statistical data.</p> <p><strong>Level 4</strong> curriculum covers more advanced statistical methods, including probability theory, sampling methods, charts and graphs, and hypothesis testing. Students will learn how to apply these methods to real-world scenarios and develop their skills in data analysis. They will also gain knowledge of common statistical mistakes and how to avoid them.</p> <p>This course is designed for individuals who are interested in pursuing a career in data analysis or research. It is suitable for individuals who are looking to gain knowledge of the fundamental principles of statistics or who want to expand their knowledge in statistical methods.</p> <p><strong>This bundle comprises the following courses:</strong></p> <p><strong>QLS Endorsed Course:</strong></p> <ul> <li><strong>Course 01: </strong>Diploma in Statistics at QLS Level 4</li></ul> <p><strong>CPD QS Accredited Courses:</strong></p> <ul> <li><strong>Course 01:</strong> Career Development Plan Fundamentals</li><li><strong>Course 02:</strong> Networking Skills for Personal Success</li><li><strong>Course 03:</strong> Boost Your Confidence and Self-Esteem</li><li><strong>Course 04:</strong> CV Writing and Job Searching</li><li><strong>Course 05:</strong> Learn to Reduce and Manage Stress</li></ul> <p><strong>Certificate</strong></p> <p>In pursuing this QLS-endorsed course, learners attain a profound understanding and amplify their professional prowess, enriching their curriculum vitae.</p> <p>The Quality Licence Scheme stands as a reputable and distinguished endorsement, emblematic of exceptional standards and excellence throughout the UK.</p> <p>Upon completion, learners will receive an Diploma in Statistics at QLS Level 4 Endorsed hard copy certificate, making it easy for them to showcase their achievements.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        15 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                             <ul> <li>Individuals interested in pursuing a career in data analysis or research.</li><li>Those looking to gain knowledge of the fundamental principles of statistics.</li><li>People who want to enhance their existing skills in statistical analysis.</li><li>Individuals who want to learn how to apply statistical analysis to real-world scenarios.</li><li>Those looking to expand their knowledge in statistical methods.</li></ul> 
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                             <ul> <li>Data Analyst - £25,000 to £50,000 per year</li><li>Research Analyst - £20,000 to £45,000 per year</li><li>Statistician - £30,000 to £60,000 per year</li><li>Business Intelligence Analyst - £25,000 to £55,000 per year</li><li>Market Research Analyst - £20,000 to £45,000 per year</li><li>Financial Analyst - £25,000 to £60,000 per year.</li></ul> 
                        </div>

Did you know that the demand for statisticians in the UK is projected to increase by 34% by 2026? In fact, statistics is one of the fastest-growing fields in the country, with an estimated 58,000 new jobs expected to be created in the next decade. This makes it a great time to invest in your future by enrolling in the Statistics QLS Level 3 & 4 course.
Through this course, you will gain a comprehensive understanding of statistical concepts and methods, including probability theory, regression analysis, hypothesis testing, and more. With a curriculum that spans from basic to higher knowledge, the course is designed to meet the needs of learners at all levels, from beginners to advanced users.
The Statistics QLS Level 3 & 4 course is endorsed by Quality Licence Scheme, which means that you can be assured of the highest quality of education and training. Upon completion of the course, you will receive a Level 4 Endorsed Certificate hardcopy free of charge, which can greatly enhance your career prospects in fields such as finance, healthcare, marketing, and more.
In addition to the Level 4 Endorsed Certificate, learners who enrol in the Statistics QLS Level 3 & 4 course will receive 5 career-guided courses free with the QLS course, including career development plan fundamentals, networking skills for personal success, boost your confidence and self-esteem, CV writing and job searching, and learn to reduce and manage stress.
So why wait? Take the first step towards a brighter future and enrol in the Statistics QLS Level 3 & 4 course today. Whether you're looking to enhance your career prospects or simply want to gain a deeper understanding of statistics, this course has everything you need to succeed.
Learning Outcomes:
Develop an understanding of the fundamental principles of statistics, including the measurement of central tendency and dispersion.
Gain knowledge of correlation and regression analysis, including how to interpret statistical relationships between variables.
Understand probability and its application in statistical analysis.
Learn about sampling methods and their use in statistical analysis.
Develop the ability to create and interpret charts and graphs for data analysis.
Gain knowledge of hypothesis testing and how to apply statistical tests to analyse data.
Identify and avoid common statistical mistakes in data analysis.
Diploma in Statistics - QLS Level 3 & 4's Description:
Level 3 curriculum provides an introduction to statistics, covering the fundamental principles of measurement of central tendency, dispersion, correlation, and regression analysis. Students will learn how to apply statistical analysis to a range of data sets. They will also gain knowledge of how to create meaningful conclusions and interpretations from statistical data.
Level 4 curriculum covers more advanced statistical methods, including probability theory, sampling methods, charts and graphs, and hypothesis testing. Students will learn how to apply these methods to real-world scenarios and develop their skills in data analysis. They will also gain knowledge of common statistical mistakes and how to avoid them.
This course is designed for individuals who are interested in pursuing a career in data analysis or research. It is suitable for individuals who are looking to gain knowledge of the fundamental principles of statistics or who want to expand their knowledge in statistical methods.
This bundle comprises the following courses:
QLS Endorsed Course:
Course 01: Diploma in Statistics at QLS Level 4
CPD QS Accredited Courses:
Course 01: Career Development Plan Fundamentals
Course 02: Networking Skills for Personal Success
Course 03: Boost Your Confidence and Self-Esteem
Course 04: CV Writing and Job Searching
Course 05: Learn to Reduce and Manage Stress
Certificate
In pursuing this QLS-endorsed course, learners attain a profound understanding and amplify their professional prowess, enriching their curriculum vitae.
The Quality Licence Scheme stands as a reputable and distinguished endorsement, emblematic of exceptional standards and excellence throughout the UK.
Upon completion, learners will receive an Diploma in Statistics at QLS Level 4 Endorsed hard copy certificate, making it easy for them to showcase their achievements.
 CPD 
15 CPD hours / points  Accredited by CPD Quality Standards
 Who is this course for? 
Individuals interested in pursuing a career in data analysis or research.
Those looking to gain knowledge of the fundamental principles of statistics.
People who want to enhance their existing skills in statistical analysis.
Individuals who want to learn how to apply statistical analysis to real-world scenarios.
Those looking to expand their knowledge in statistical methods.
 Career path 
Data Analyst - £25,000 to £50,000 per year
Research Analyst - £20,000 to £45,000 per year
Statistician - £30,000 to £60,000 per year
Business Intelligence Analyst - £25,000 to £55,000 per year
Market Research Analyst - £20,000 to £45,000 per year
Financial Analyst - £25,000 to £60,000 per year.

Imperial Academy

Level 4 QLS Endorsed Course | Endorsed Certificate Included | Plus 5 Career Guided Courses | CPD Accredited

Statistics - QLS Level 3 & 4

Description
Our comprehensive course combines " Accidents, Incidents, and Breakdowns and Vehicle Marshal and Banksman Training combines 2 aspects: Road safety measures to handle accidents, incidents, and breakdowns and essentials of critical health and safety responsibilities.
Accidents, Incidents, and Breakdowns Training Content:
Recognizing risks and potential incidents, including breakdowns, collisions, and other scenarios.
Duty of care, taking preventative measures, and handling incidents.
Understanding health and safety regulations and related statistics.
Prevention strategies and appropriate responses in case of an incident.
Personal safety and actions to be taken during a vehicle collision.
Steps to follow when facing a breakdown, including monitoring gauges and risk assessments.
Ensuring the safety of other road users and cooperating with emergency services and recovery operators.
Handling bridge strikes, including prevention and actions in case of an incident.
Vehicle Marshal and Banksman
Learn critical health and safety responsibilities
Maneuvering vehicles safely during loading and unloading operations
The safe use of work equipment
Best practices for guiding vehicles as a Banksman



Join us to enhance your knowledge of road safety preparedness. Register today to ensure your drivers are well-versed in the rules of the road and equipped to handle unexpected challenges on their journeys.



Please review our Terms and Conditions for more information.

Total Compliance 

Driver CPC - Accidents, Incidents and Breakdowns, Vehicle Marshal & Banksman - August 2025

<strong>This course covers the fundamental topics of statistical methodology, linear regression, and ordinary least squares that every statistician needs to know.</strong>
        <p>To work with statistics and quantitative reports, you need to have a good understanding of the fundamentals and techniques of statistics. However, learning and applying new statistical techniques can often be a daunting experience and this is where this video course comes in handy. This course is designed to guide you through the basic principles of statistical methodology in a step-by-step way.
You'll start with an introduction to the course along with an overview of the learning objectives. Next, you'll explore the types of regression analysis that exist, and find out how ordinary least squares (OLS) work. To get a deeper understanding of linear regression and OLS, you'll interpret and analyze complicated regression output from OLS. Finally, you'll discover essential tips and techniques that'll help you to confidently perform regression analysis.
By the end of this video, you'll be well-versed with linear regression techniques and the basic principles of statistical methodology.
The code and resources for this course is available at https://github.com/PacktPublishing/Easy-Statistics-Linear-Regression</p>
        <h2>What You Will Learn</h2><p>Understand the basic concept of statistical regression analysis<br>Become familiar with linear regression terminologies<br>Distinguish between different kinds of regression methods<br>Find out the difference between correlation and causation<br>Analyze and integrate complicated regression output from OLS<br>Explore tips and tricks relating to regression analysis</p>
        <h2>Audience</h2><p>If you are a student at any level, an experienced professional, a manager, or a government worker who wants to delve into statistical regression or work with quantitative reports and policy analysis, then this course is for you. As the course is for beginners, it doesn't require you to have any knowledge of mathematics or statistics.</p>
        <h2>Approach</h2><p>With the help of animated graphics and interesting examples, this course will teach you the fundamentals of statistical regression terminologies.</p>
        <h2>Key Features</h2><p>Explore the basic statistical fundamentals of ordinary least squares (OLS) * Understand linear regression and its application * Gain the confidence to interpret complicated regression output</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Easy-Statistics-Linear-Regression</p>
        <h2>About the Author</h2><h3>Franz Buscha</h3><p>Franz Buscha is a professor of economics at the University of Westminster, which he joined after completing his Ph.D. in economics at Lancaster University. He has been involved in numerous funded research projects from research councils and government departments. He has also contributed to a wide range of projects, including policy evaluation and bespoke econometric advice to UK government departments. Franz has published in leading journals and contributed to numerous policy reports. His research has even been covered by various media outlets. He is an experienced online educator and has published several online courses, including LinkedIn Learning. Franz also has a monthly radio program called Policy Matters on Share Radio.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. What is Regression?</h4><p></p><br><h4>3. What is Ordinary Least Squares?</h4><p></p><br><h4>4. Ordinary Least Squares Regression in Practice</h4><p></p><br><h4>5. Tips and Final Thoughts </h4><p></p>

This course covers the fundamental topics of statistical methodology, linear regression, and ordinary least squares that every statistician needs to know.
To work with statistics and quantitative reports, you need to have a good understanding of the fundamentals and techniques of statistics. However, learning and applying new statistical techniques can often be a daunting experience and this is where this video course comes in handy. This course is designed to guide you through the basic principles of statistical methodology in a step-by-step way.
You'll start with an introduction to the course along with an overview of the learning objectives. Next, you'll explore the types of regression analysis that exist, and find out how ordinary least squares (OLS) work. To get a deeper understanding of linear regression and OLS, you'll interpret and analyze complicated regression output from OLS. Finally, you'll discover essential tips and techniques that'll help you to confidently perform regression analysis.
By the end of this video, you'll be well-versed with linear regression techniques and the basic principles of statistical methodology.
The code and resources for this course is available at https://github.com/PacktPublishing/Easy-Statistics-Linear-Regression
What You Will Learn
Understand the basic concept of statistical regression analysis
Become familiar with linear regression terminologies
Distinguish between different kinds of regression methods
Find out the difference between correlation and causation
Analyze and integrate complicated regression output from OLS
Explore tips and tricks relating to regression analysis
Audience
If you are a student at any level, an experienced professional, a manager, or a government worker who wants to delve into statistical regression or work with quantitative reports and policy analysis, then this course is for you. As the course is for beginners, it doesn't require you to have any knowledge of mathematics or statistics.
Approach
With the help of animated graphics and interesting examples, this course will teach you the fundamentals of statistical regression terminologies.
Key Features
Explore the basic statistical fundamentals of ordinary least squares (OLS) * Understand linear regression and its application * Gain the confidence to interpret complicated regression output
Github Repo
https://github.com/PacktPublishing/Easy-Statistics-Linear-Regression
About the Author
Franz Buscha
Franz Buscha is a professor of economics at the University of Westminster, which he joined after completing his Ph.D. in economics at Lancaster University. He has been involved in numerous funded research projects from research councils and government departments. He has also contributed to a wide range of projects, including policy evaluation and bespoke econometric advice to UK government departments. Franz has published in leading journals and contributed to numerous policy reports. His research has even been covered by various media outlets. He is an experienced online educator and has published several online courses, including LinkedIn Learning. Franz also has a monthly radio program called Policy Matters on Share Radio.
Course Outline
1. Introduction

2. What is Regression?

3. What is Ordinary Least Squares?

4. Ordinary Least Squares Regression in Practice

5. Tips and Final Thoughts


Packt

Stata

This course covers the fundamental topics of statistical methodology, linear regression, and ordinary least squares that every statistician needs to know.

Easy Statistics: Linear Regression

<strong>This course covers the fundamental topics of statistical methodology, enabling you to understand the application and interpretation of linear and non-linear regression modeling.</strong>
        <p>Working with statistics and quantitative reports requires a good understanding of statistics fundamentals and techniques. However, learning and applying new statistical techniques can often be a daunting experience. This is where this course comes into play.
To make your experience with statistics a pleasant one, this course gives you comprehensive knowledge of basic principles of the statistical methodology, focusing on linear regression and non-linear regression.
The course starts with an introduction to easy statistics and gives you an overview of the course objectives. Next, you will explore the types of regression analysis that exist and find out how ordinary least squares (OLS) works. To gain a deeper understanding of linear regression and OLS, you will learn to interpret and analyze complicated regression output from OLS. You will also focus on Gauss-Markov assumptions and zero conditional mean. Moving ahead, you will cover non-linear regression, exploring how it works, what the different non-linear regression models are, and the major uses. Towards the end, you will learn to work around with regression modeling with the help of practical examples.
By the end of this video, you will be well-versed with linear and non-linear regression and the basic principles of the statistical methodology.

All the resource files are added on GitHub at https://github.com/PacktPublishing/-Easy-Statistics-Linear-and-Non-Linear-Regression</p>
        <h2>What You Will Learn</h2><p>Understand the basic concept of statistical regression analysis<br>Become familiar with linear and non-linear regression terminologies<br>Distinguish between different types of regression methods<br>Analyze and integrate complicated regression output from ordinary least squares (OLS)<br>Find out the difference between logit and probit transformation<br>Model non-linear relationships in a linear regression</p>
        <h2>Audience</h2><p>If you're a student, an experienced professional, a manager, or a government worker who wants to learn linear and non-linear regression, regression modeling, and ordinary least squares, then this course is for you. This is a beginner-level course and does not require any prior knowledge of mathematics or statistics.</p>
        <h2>Approach</h2><p>With the help of eye-catching animated graphics and interesting examples, this course will teach you linear and non-linear regression and regression modeling to ensure that you become well-versed with the fundamentals of statistics and quantitative analysis.</p>
        <h2>Key Features</h2><p>Understand the statistical fundamentals of ordinary least squares (OLS) * Gain the confidence to comfortably interpret complicated regression output from OLS * Explore regression modeling and its application</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/-Easy-Statistics-Linear-and-Non-Linear-Regression</p>
        <h2>About the Author</h2><h3>Franz Buscha</h3><p>Franz Buscha is a professor of economics at the University of Westminster, which he joined after completing his Ph.D. in economics at Lancaster University. He has been involved in numerous funded research projects from research councils and government departments. He has also contributed to a wide range of projects, including policy evaluation and bespoke econometric advice to UK government departments. Franz has published in leading journals and contributed to numerous policy reports. His research has even been covered by various media outlets. He is an experienced online educator and has published several online courses, including LinkedIn Learning. Franz also has a monthly radio program called Policy Matters on Share Radio.</p>
        <h2>Course Outline</h2><h4>1. Linear Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What is Easy Statistics: Linear Regression?</p><p style='margin: 0'>This video explains the concept of easy statistics with linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Linear Regression?</p><p style='margin: 0'>This video explains the concept of linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>3. Learning Outcomes</p><p style='margin: 0'>This video explains the various learning outcomes.</p></td></tr><tr><td style='padding: 15px;'><p>4. Whom is this Course for?</p><p style='margin: 0'>This video discusses the target audience for this course.</p></td></tr><tr><td style='padding: 15px;'><p>5. Prerequisites</p><p style='margin: 0'>This video explains the various prerequisites of the course.</p></td></tr><tr><td style='padding: 15px;'><p>6. Using Stata</p><p style='margin: 0'>This video shows how to use Stata for linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>7. What is Regression Analysis?</p><p style='margin: 0'>This video explains the concept of regression analysis.</p></td></tr><tr><td style='padding: 15px;'><p>8. Get to Know About Linear Regression</p><p style='margin: 0'>This video explains the concept of linear regression in detail.</p></td></tr><tr><td style='padding: 15px;'><p>9. Why is Regression Analysis Useful?</p><p style='margin: 0'>This video explains the importance and uses of regression analysis.</p></td></tr><tr><td style='padding: 15px;'><p>10. What Types of Regression Analysis Exist?</p><p style='margin: 0'>This video explains the various types of regression analysis.</p></td></tr><tr><td style='padding: 15px;'><p>11. Explaining Regression</p><p style='margin: 0'>This video explains the concept of regression.</p></td></tr><tr><td style='padding: 15px;'><p>12. Lines of Best Fit</p><p style='margin: 0'>This video explains the lines of best fit concept.</p></td></tr><tr><td style='padding: 15px;'><p>13. Causality vs. Correlation</p><p style='margin: 0'>This video illustrates the difference between causality and correlation.</p></td></tr><tr><td style='padding: 15px;'><p>14. What is Ordinary Least Squares (OLS)?</p><p style='margin: 0'>This video explains the concept of OLS.</p></td></tr><tr><td style='padding: 15px;'><p>15. Ordinary Least Squares (OLS) Visual - Part 1</p><p style='margin: 0'>This video is the first part of the two-part video that explains OLS visually.</p></td></tr><tr><td style='padding: 15px;'><p>16. Ordinary Least Squares (OLS) Visual - Part 2</p><p style='margin: 0'>This video is the second part of the two-part video that explains OLS visually.</p></td></tr><tr><td style='padding: 15px;'><p>17. Sum of Squares</p><p style='margin: 0'>This video explains how to calculate the sum of squares using linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>18. Best Linear Unbiased Estimator</p><p style='margin: 0'>This video explains the best linear unbiased estimator.</p></td></tr><tr><td style='padding: 15px;'><p>19. The Gauss-Markov Assumptions</p><p style='margin: 0'>This video discusses the Gauss-Markov assumptions.</p></td></tr><tr><td style='padding: 15px;'><p>20. Homoskedasticity</p><p style='margin: 0'>This video explains the concept of homoskedasticity.</p></td></tr><tr><td style='padding: 15px;'><p>21. No Perfect Collinearity</p><p style='margin: 0'>This video explains the concept of no perfect collinearity.</p></td></tr><tr><td style='padding: 15px;'><p>22. Linear in Parameters</p><p style='margin: 0'>This video focuses on linearity in parameters.</p></td></tr><tr><td style='padding: 15px;'><p>23. Zero Conditional Mean</p><p style='margin: 0'>This video explains what is zero conditional mean.</p></td></tr><tr><td style='padding: 15px;'><p>24. How to Test and Correct for Endogeneity?</p><p style='margin: 0'>This video explains how to test and correct for endogeneity.</p></td></tr><tr><td style='padding: 15px;'><p>25. The Gauss-Markov Assumptions - Recap</p><p style='margin: 0'>This video presents a recap of the Gauss-Markov assumptions.</p></td></tr><tr><td style='padding: 15px;'><p>26. Stata - Applied Examples</p><p style='margin: 0'>This video explains with an example how to use Stata.</p></td></tr><tr><td style='padding: 15px;'><p>27. Final Thoughts and Tips</p><p style='margin: 0'>This video provides some final thoughts and tips with respect to linear regression.</p></td></tr></tbody></table><br><h4>2. Non-Linear Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What is Easy Statistics: Non-Linear Regression?</p><p style='margin: 0'>This video explains the concept of easy statistics with non-linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Non-Linear Regression?</p><p style='margin: 0'>This video explains the concept of non-linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>3. What are the Main Learning Outcomes?</p><p style='margin: 0'>This video presents the learning outcome of this course.</p></td></tr><tr><td style='padding: 15px;'><p>4. Whom is this Course for?</p><p style='margin: 0'>This video discusses the target audience for this course.</p></td></tr><tr><td style='padding: 15px;'><p>5. Prerequisites</p><p style='margin: 0'>This video discusses the various prerequisites for this course.</p></td></tr><tr><td style='padding: 15px;'><p>6. Using Stata</p><p style='margin: 0'>This video explains how to use Stata.</p></td></tr><tr><td style='padding: 15px;'><p>7. What is Non-Linear Regression Analysis?</p><p style='margin: 0'>This video explains the concept of non-linear regression analysis.</p></td></tr><tr><td style='padding: 15px;'><p>8. How does Non-Linear Regression Work?</p><p style='margin: 0'>This video explains the working of non-linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>9. Why is Non-Linear Regression analysis Useful?</p><p style='margin: 0'>This video explains the importance and uses of non-linear regression analysis.</p></td></tr><tr><td style='padding: 15px;'><p>10. Types of Non-Linear Regression models</p><p style='margin: 0'>This video explains the various types of non-linear regression models.</p></td></tr><tr><td style='padding: 15px;'><p>11. Maximum Likelihood</p><p style='margin: 0'>This video explains the maximum likelihood estimation technique.</p></td></tr><tr><td style='padding: 15px;'><p>12. Linear Probability Model</p><p style='margin: 0'>This video explains the linear probability model.</p></td></tr><tr><td style='padding: 15px;'><p>13. The Logit and Probit Transformation</p><p style='margin: 0'>This video explains the logit and probit transformation model.</p></td></tr><tr><td style='padding: 15px;'><p>14. Latent Variables</p><p style='margin: 0'>This video focuses on latent variables.</p></td></tr><tr><td style='padding: 15px;'><p>15. What are Marginal Effects?</p><p style='margin: 0'>This video explains the concept of marginal effects.</p></td></tr><tr><td style='padding: 15px;'><p>16. Dummy Explanatory Variables</p><p style='margin: 0'>This video focuses on the dummy explanatory variables.</p></td></tr><tr><td style='padding: 15px;'><p>17. Multiple Non-Linear Regression</p><p style='margin: 0'>This video explains multiple non-linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>18. Goodness-of-Fit</p><p style='margin: 0'>This video discusses the concept of goodness-of-fit.</p></td></tr><tr><td style='padding: 15px;'><p>19. A Note about Logit Coefficients</p><p style='margin: 0'>This video provides a note about logit coefficients.</p></td></tr><tr><td style='padding: 15px;'><p>20. Tips for Logit and Probit Regression</p><p style='margin: 0'>This video provides some tips for logit and probit regression.</p></td></tr><tr><td style='padding: 15px;'><p>21. Back to the Linear Probability Model</p><p style='margin: 0'>This video explains the linear probability model.</p></td></tr><tr><td style='padding: 15px;'><p>22. Stata - Applied Logit and Probit Examples</p><p style='margin: 0'>This video provides examples of applied logit and probit using Stata.</p></td></tr></tbody></table><br><h4>3. Regression Modelling</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to regression modelling.</p></td></tr><tr><td style='padding: 15px;'><p>2. Regression Modelling - Don't Rush it</p><p style='margin: 0'>This video explains the concept of regression modelling in detail.</p></td></tr><tr><td style='padding: 15px;'><p>3. Non-Linear Shapes in Regression</p><p style='margin: 0'>This video explains the concept of non-linear shapes in regression.</p></td></tr><tr><td style='padding: 15px;'><p>4. Non-Linear Shapes in Regression - Practical Examples</p><p style='margin: 0'>This video explains the concept of non-linear shapes in regression with the help of examples.</p></td></tr><tr><td style='padding: 15px;'><p>5. How to use and Interpret Interaction Effects?</p><p style='margin: 0'>This video explains how to use and interpret interaction effects.</p></td></tr><tr><td style='padding: 15px;'><p>6. How to use and Interpret Interaction Effects? - Practical Examples</p><p style='margin: 0'>This video explains how to use and interpret interaction effects with the help of examples.</p></td></tr><tr><td style='padding: 15px;'><p>7. Using Time in Regression</p><p style='margin: 0'>This video explains how to use time in regression.</p></td></tr><tr><td style='padding: 15px;'><p>8. Using Time in Regression - Practical Examples</p><p style='margin: 0'>This video explains how to use time in regression with the help of examples.</p></td></tr><tr><td style='padding: 15px;'><p>9. Categorical Explanatory Variables in Regression</p><p style='margin: 0'>This video explains the categorical explanatory variables in regression.</p></td></tr><tr><td style='padding: 15px;'><p>10. Categorical Explanatory Variables in Regression - Practical Examples</p><p style='margin: 0'>This video explains the categorical explanatory variables in regression with the help of examples.</p></td></tr><tr><td style='padding: 15px;'><p>11. Dealing with Multicollinearity in Regression</p><p style='margin: 0'>This video explains how to deal with multicollinearity in regression.</p></td></tr><tr><td style='padding: 15px;'><p>12. Dealing with Multicollinearity in Regression - Practical Examples</p><p style='margin: 0'>This video explains how to deal with multicollinearity in regression with the help of examples.</p></td></tr><tr><td style='padding: 15px;'><p>13. Dealing with Missing Data in Regression</p><p style='margin: 0'>This video explains how to deal with missing data in regression.</p></td></tr><tr><td style='padding: 15px;'><p>14. Dealing with Missing Data in Regression - Practical Examples</p><p style='margin: 0'>This video explains how to deal with missing data in regression with the help of examples.</p></td></tr></tbody></table>

This course covers the fundamental topics of statistical methodology, enabling you to understand the application and interpretation of linear and non-linear regression modeling.
Working with statistics and quantitative reports requires a good understanding of statistics fundamentals and techniques. However, learning and applying new statistical techniques can often be a daunting experience. This is where this course comes into play.
To make your experience with statistics a pleasant one, this course gives you comprehensive knowledge of basic principles of the statistical methodology, focusing on linear regression and non-linear regression.
The course starts with an introduction to easy statistics and gives you an overview of the course objectives. Next, you will explore the types of regression analysis that exist and find out how ordinary least squares (OLS) works. To gain a deeper understanding of linear regression and OLS, you will learn to interpret and analyze complicated regression output from OLS. You will also focus on Gauss-Markov assumptions and zero conditional mean. Moving ahead, you will cover non-linear regression, exploring how it works, what the different non-linear regression models are, and the major uses. Towards the end, you will learn to work around with regression modeling with the help of practical examples.
By the end of this video, you will be well-versed with linear and non-linear regression and the basic principles of the statistical methodology. All the resource files are added on GitHub at https://github.com/PacktPublishing/-Easy-Statistics-Linear-and-Non-Linear-Regression
What You Will Learn
Understand the basic concept of statistical regression analysis
Become familiar with linear and non-linear regression terminologies
Distinguish between different types of regression methods
Analyze and integrate complicated regression output from ordinary least squares (OLS)
Find out the difference between logit and probit transformation
Model non-linear relationships in a linear regression
Audience
If you're a student, an experienced professional, a manager, or a government worker who wants to learn linear and non-linear regression, regression modeling, and ordinary least squares, then this course is for you. This is a beginner-level course and does not require any prior knowledge of mathematics or statistics.
Approach
With the help of eye-catching animated graphics and interesting examples, this course will teach you linear and non-linear regression and regression modeling to ensure that you become well-versed with the fundamentals of statistics and quantitative analysis.
Key Features
Understand the statistical fundamentals of ordinary least squares (OLS) * Gain the confidence to comfortably interpret complicated regression output from OLS * Explore regression modeling and its application
Github Repo
https://github.com/PacktPublishing/-Easy-Statistics-Linear-and-Non-Linear-Regression
About the Author
Franz Buscha
Franz Buscha is a professor of economics at the University of Westminster, which he joined after completing his Ph.D. in economics at Lancaster University. He has been involved in numerous funded research projects from research councils and government departments. He has also contributed to a wide range of projects, including policy evaluation and bespoke econometric advice to UK government departments. Franz has published in leading journals and contributed to numerous policy reports. His research has even been covered by various media outlets. He is an experienced online educator and has published several online courses, including LinkedIn Learning. Franz also has a monthly radio program called Policy Matters on Share Radio.
Course Outline
1. Linear Regression

1. What is Easy Statistics: Linear Regression?
This video explains the concept of easy statistics with linear regression.
2. What is Linear Regression?
This video explains the concept of linear regression.
3. Learning Outcomes
This video explains the various learning outcomes.
4. Whom is this Course for?
This video discusses the target audience for this course.
5. Prerequisites
This video explains the various prerequisites of the course.
6. Using Stata
This video shows how to use Stata for linear regression.
7. What is Regression Analysis?
This video explains the concept of regression analysis.
8. Get to Know About Linear Regression
This video explains the concept of linear regression in detail.
9. Why is Regression Analysis Useful?
This video explains the importance and uses of regression analysis.
10. What Types of Regression Analysis Exist?
This video explains the various types of regression analysis.
11. Explaining Regression
This video explains the concept of regression.
12. Lines of Best Fit
This video explains the lines of best fit concept.
13. Causality vs. Correlation
This video illustrates the difference between causality and correlation.
14. What is Ordinary Least Squares (OLS)?
This video explains the concept of OLS.
15. Ordinary Least Squares (OLS) Visual - Part 1
This video is the first part of the two-part video that explains OLS visually.
16. Ordinary Least Squares (OLS) Visual - Part 2
This video is the second part of the two-part video that explains OLS visually.
17. Sum of Squares
This video explains how to calculate the sum of squares using linear regression.
18. Best Linear Unbiased Estimator
This video explains the best linear unbiased estimator.
19. The Gauss-Markov Assumptions
This video discusses the Gauss-Markov assumptions.
20. Homoskedasticity
This video explains the concept of homoskedasticity.
21. No Perfect Collinearity
This video explains the concept of no perfect collinearity.
22. Linear in Parameters
This video focuses on linearity in parameters.
23. Zero Conditional Mean
This video explains what is zero conditional mean.
24. How to Test and Correct for Endogeneity?
This video explains how to test and correct for endogeneity.
25. The Gauss-Markov Assumptions - Recap
This video presents a recap of the Gauss-Markov assumptions.
26. Stata - Applied Examples
This video explains with an example how to use Stata.
27. Final Thoughts and Tips
This video provides some final thoughts and tips with respect to linear regression.
2. Non-Linear Regression

1. What is Easy Statistics: Non-Linear Regression?
This video explains the concept of easy statistics with non-linear regression.
2. What is Non-Linear Regression?
This video explains the concept of non-linear regression.
3. What are the Main Learning Outcomes?
This video presents the learning outcome of this course.
4. Whom is this Course for?
This video discusses the target audience for this course.
5. Prerequisites
This video discusses the various prerequisites for this course.
6. Using Stata
This video explains how to use Stata.
7. What is Non-Linear Regression Analysis?
This video explains the concept of non-linear regression analysis.
8. How does Non-Linear Regression Work?
This video explains the working of non-linear regression.
9. Why is Non-Linear Regression analysis Useful?
This video explains the importance and uses of non-linear regression analysis.
10. Types of Non-Linear Regression models
This video explains the various types of non-linear regression models.
11. Maximum Likelihood
This video explains the maximum likelihood estimation technique.
12. Linear Probability Model
This video explains the linear probability model.
13. The Logit and Probit Transformation
This video explains the logit and probit transformation model.
14. Latent Variables
This video focuses on latent variables.
15. What are Marginal Effects?
This video explains the concept of marginal effects.
16. Dummy Explanatory Variables
This video focuses on the dummy explanatory variables.
17. Multiple Non-Linear Regression
This video explains multiple non-linear regression.
18. Goodness-of-Fit
This video discusses the concept of goodness-of-fit.
19. A Note about Logit Coefficients
This video provides a note about logit coefficients.
20. Tips for Logit and Probit Regression
This video provides some tips for logit and probit regression.
21. Back to the Linear Probability Model
This video explains the linear probability model.
22. Stata - Applied Logit and Probit Examples
This video provides examples of applied logit and probit using Stata.
3. Regression Modelling

1. Introduction
This video provides an introduction to regression modelling.
2. Regression Modelling - Don't Rush it
This video explains the concept of regression modelling in detail.
3. Non-Linear Shapes in Regression
This video explains the concept of non-linear shapes in regression.
4. Non-Linear Shapes in Regression - Practical Examples
This video explains the concept of non-linear shapes in regression with the help of examples.
5. How to use and Interpret Interaction Effects?
This video explains how to use and interpret interaction effects.
6. How to use and Interpret Interaction Effects? - Practical Examples
This video explains how to use and interpret interaction effects with the help of examples.
7. Using Time in Regression
This video explains how to use time in regression.
8. Using Time in Regression - Practical Examples
This video explains how to use time in regression with the help of examples.
9. Categorical Explanatory Variables in Regression
This video explains the categorical explanatory variables in regression.
10. Categorical Explanatory Variables in Regression - Practical Examples
This video explains the categorical explanatory variables in regression with the help of examples.
11. Dealing with Multicollinearity in Regression
This video explains how to deal with multicollinearity in regression.
12. Dealing with Multicollinearity in Regression - Practical Examples
This video explains how to deal with multicollinearity in regression with the help of examples.
13. Dealing with Missing Data in Regression
This video explains how to deal with missing data in regression.
14. Dealing with Missing Data in Regression - Practical Examples
This video explains how to deal with missing data in regression with the help of examples.

This course covers the fundamental topics of statistical methodology, enabling you to understand the application and interpretation of linear and non-linear regression modeling.

Easy Statistics: Linear and Non-Linear Regression

<strong>This course is designed for beginners, although we will go deep gradually, and is a highly focused course designed to master your Python skills in probability and statistics, which covers the major part of machine learning or data science-related career opportunities.</strong>
        <p>In today's ultra-competitive business universe, probability and statistics are the most important fields of study. That is because statistical research presents businesses with the data they need to make informed decisions in every business area, whether it is market research, product development, product launch timing, customer data analysis, sales forecast, or employee performance.

But why do you need to master probability and statistics in Python?

The answer is that an expert grip on the concepts of statistics and probability with data science will enable you to take your career to the next level. This course is designed carefully to reflect the most in-demand skills that will help you in understanding the concepts and methodology with regard to Python.

The course is as follows:

Easy to understand
Expressive
Comprehensive
Practical with live coding
About establishing links between probability and machine learning

By the end of this course, you will be able to relate the concepts and theories in machine learning with probabilistic reasoning and understand the methodology of statistics and probability with data science, using real datasets.

The code files and all related files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Mastering-Probability-and-Statistics-in-Python</p>
        <h2>What You Will Learn</h2><p>The importance of statistics and probability in data science<br>The foundations for machine learning and its roots in probability theory<br>The concepts of absolute beginning in-depth with examples in Python<br>Practical explanation and live coding with Python<br>Probabilistic view of modern machine learning<br>Implementation of Bayes' classifier on a real dataset</p>
        <h2>Audience</h2><p>This course is for individuals who want to learn statistics and probability along with its implementation in realistic projects. Data scientists and business analysts and those who want to upgrade their data analysis skills will also get the benefit. People who want to learn statistics and probability with real datasets in data science and are passionate about numbers and programming will get the most out of this course.<br><br>No prior knowledge is needed. You start from the basics and gradually build your knowledge of the subject. A basic understanding of Python will be a plus but not mandatory.</p>
        <h2>Approach</h2><p>This course is designed for beginners, although we will go deep gradually. You will learn theoretical concepts first, followed by its practical implementation in Python. At the end of each module, you will work on the homework/tasks, which will evaluate/further build your learning based on the previous concepts and methods.</p>
        <h2>Key Features</h2><p>Easy explanations, yet complete and comprehensive course * Fundamental, pythonic, and a complete course to master the important concepts used in data science * Practical with live coding of the implementation of the concepts learned theoretically</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Mastering-Probability-and-Statistics-in-Python</p>
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.

Their courses have successfully helped more than 100,000 students master AI and data science.</p>
        <h2>Course Outline</h2><h4>1. Introduction to the Course</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to the Instructor</p><p style='margin: 0'>This video gives you an introduction to AI sciences and to the instructor of this course.</p></td></tr><tr><td style='padding: 15px;'><p>2. Focus of the Course</p><p style='margin: 0'>This video shows you the objective of the course.</p></td></tr></tbody></table><br><h4>2. Probability and Statistics</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Probability Versus Statistics</p><p style='margin: 0'>This video focuses on probability versus statistics.</p></td></tr></tbody></table><br><h4>3. Sets</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Definition of Set</p><p style='margin: 0'>This video demonstrates the definition of a set.</p></td></tr><tr><td style='padding: 15px;'><p>2. Cardinality of a Set</p><p style='margin: 0'>This video explains the cardinality of a set.</p></td></tr><tr><td style='padding: 15px;'><p>3. Subsets, Power Set, and Universal Set</p><p style='margin: 0'>This video explains subsets, power set, and universal set.</p></td></tr><tr><td style='padding: 15px;'><p>4. Python Practice Subsets</p><p style='margin: 0'>This video lets you practice on the subsets that you have learned so far.</p></td></tr><tr><td style='padding: 15px;'><p>5. Power Sets Solution</p><p style='margin: 0'>This video answers the problem statement to generate the power set.</p></td></tr><tr><td style='padding: 15px;'><p>6. Operations</p><p style='margin: 0'>This video explores the different types of operations that can be performed on the sets.</p></td></tr><tr><td style='padding: 15px;'><p>7. Python Practice Operations</p><p style='margin: 0'>This video lets you practice on the types of operations that you have learned so far.</p></td></tr><tr><td style='padding: 15px;'><p>8. Venn Diagrams Operations</p><p style='margin: 0'>This video shows the types of operations that can be performed on the Venn diagrams.</p></td></tr><tr><td style='padding: 15px;'><p>9. Homework</p><p style='margin: 0'>This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.</p></td></tr></tbody></table><br><h4>4. Experiment</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Random Experiment</p><p style='margin: 0'>This video explains about a random experiment.</p></td></tr><tr><td style='padding: 15px;'><p>2. Outcome and Sample Space</p><p style='margin: 0'>This video explains what is meant by the outcome and sample space of any experiment.</p></td></tr><tr><td style='padding: 15px;'><p>3. Event</p><p style='margin: 0'>This video explains about the event in an experiment.</p></td></tr><tr><td style='padding: 15px;'><p>4. Recap and Homework</p><p style='margin: 0'>This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.</p></td></tr></tbody></table><br><h4>5. Probability Model</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Probability model</p><p style='margin: 0'>This video talks about probability models.</p></td></tr><tr><td style='padding: 15px;'><p>2. Probability Axioms</p><p style='margin: 0'>This video explains axioms of probability.</p></td></tr><tr><td style='padding: 15px;'><p>3. Probability Axioms Derivations</p><p style='margin: 0'>This video covers derivation from axioms of probability.</p></td></tr><tr><td style='padding: 15px;'><p>4. Probability Models Example</p><p style='margin: 0'>This video shows examples of probability models.</p></td></tr><tr><td style='padding: 15px;'><p>5. More Examples of Probability Models</p><p style='margin: 0'>This video shows a few more examples of probability models.</p></td></tr><tr><td style='padding: 15px;'><p>6. Probability Models Continuous</p><p style='margin: 0'>This video talks about continuous probability models.</p></td></tr><tr><td style='padding: 15px;'><p>7. Conditional Probability</p><p style='margin: 0'>This video explains the concept of conditional probability.</p></td></tr><tr><td style='padding: 15px;'><p>8. Conditional Probability Example</p><p style='margin: 0'>This video shows an example of conditional probability.</p></td></tr><tr><td style='padding: 15px;'><p>9. Conditional Probability Formula</p><p style='margin: 0'>This video covers the formula for conditional probability.</p></td></tr><tr><td style='padding: 15px;'><p>10. Conditional Probability in Machine Learning</p><p style='margin: 0'>This video demonstrates the importance of conditional probability in Machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>11. Conditional Probability Total Probability Theorem</p><p style='margin: 0'>This video covers the law of total probability or total probability theorem.</p></td></tr><tr><td style='padding: 15px;'><p>12. Probability Models Independence</p><p style='margin: 0'>This video explains one of the important probability theories, that is, "Independence".</p></td></tr><tr><td style='padding: 15px;'><p>13. Probability Models Conditional Independence</p><p style='margin: 0'>This video continues with the concept of Independence in the probability theory.</p></td></tr><tr><td style='padding: 15px;'><p>14. Probability Models Bayes' Rule</p><p style='margin: 0'>This video explains the Bayes' rule or theorem in probability theory.</p></td></tr><tr><td style='padding: 15px;'><p>15. Probability Models towards Random Variables</p><p style='margin: 0'>This video talks about random variables.</p></td></tr><tr><td style='padding: 15px;'><p>16. Homework</p><p style='margin: 0'>This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.</p></td></tr></tbody></table><br><h4>6. Random Variables</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to random variables.</p></td></tr><tr><td style='padding: 15px;'><p>2. Random Variables Examples</p><p style='margin: 0'>This video shows examples of random variables.</p></td></tr><tr><td style='padding: 15px;'><p>3. Bernoulli Random Variables</p><p style='margin: 0'>This video explains discrete random variables and you will also learn about the Bernoulli random variable.</p></td></tr><tr><td style='padding: 15px;'><p>4. Bernoulli Trail Python Practice</p><p style='margin: 0'>This video builds simulation for the Bernoulli random variable.</p></td></tr><tr><td style='padding: 15px;'><p>5. Geometric Random Variable</p><p style='margin: 0'>This video explains the concept of geometric random variable.</p></td></tr><tr><td style='padding: 15px;'><p>6. Geometric Random Variable Normalization Proof Optional</p><p style='margin: 0'>This video will continue to discuss the geometric random variable.</p></td></tr><tr><td style='padding: 15px;'><p>7. Geometric Random Variable Python Practice</p><p style='margin: 0'>This video shows the implementation of geometric random variable in Python.</p></td></tr><tr><td style='padding: 15px;'><p>8. Binomial Random Variables</p><p style='margin: 0'>This video explains the concept of binomial random variables.</p></td></tr><tr><td style='padding: 15px;'><p>9. Binomial Python Practice</p><p style='margin: 0'>This video shows the implementation of binomial random variable in Python.</p></td></tr><tr><td style='padding: 15px;'><p>10. Random Variables in Real Datasets</p><p style='margin: 0'>This video shows the real data sets and helps you build a relation of random variables on real datasets.</p></td></tr><tr><td style='padding: 15px;'><p>11. Homework</p><p style='margin: 0'>This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.</p></td></tr></tbody></table><br><h4>7. Continuous Random Variables</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Zero Probability to Individual Values</p><p style='margin: 0'>This video talks about continuous random variables.</p></td></tr><tr><td style='padding: 15px;'><p>2. Probability Density Functions</p><p style='margin: 0'>This video explains probability density functions.</p></td></tr><tr><td style='padding: 15px;'><p>3. Uniform Distribution</p><p style='margin: 0'>This video explains uniform distribution.</p></td></tr><tr><td style='padding: 15px;'><p>4. Uniform Distribution Python</p><p style='margin: 0'>This video shows the implementation of uniform distribution in Python.</p></td></tr><tr><td style='padding: 15px;'><p>5. Exponential</p><p style='margin: 0'>This video explains about the exponential random variables.</p></td></tr><tr><td style='padding: 15px;'><p>6. Exponential Python</p><p style='margin: 0'>This video shows the implementation of exponential random variables in Python.</p></td></tr><tr><td style='padding: 15px;'><p>7. Gaussian Random Variables</p><p style='margin: 0'>This video explains about Gaussian random variables.</p></td></tr><tr><td style='padding: 15px;'><p>8. Gaussian Python</p><p style='margin: 0'>This video shows the implementation of Gaussian random variables in Python.</p></td></tr><tr><td style='padding: 15px;'><p>9. Transformation of Random Variables</p><p style='margin: 0'>This video explains about the transformation of random variables.</p></td></tr><tr><td style='padding: 15px;'><p>10. Homework</p><p style='margin: 0'>This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.</p></td></tr></tbody></table><br><h4>8. Expectations</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Definition</p><p style='margin: 0'>This video defines expectations in detail.</p></td></tr><tr><td style='padding: 15px;'><p>2. Sample Mean</p><p style='margin: 0'>This video helps you compute the mean or average of the data in Python.</p></td></tr><tr><td style='padding: 15px;'><p>3. Law of Large Numbers</p><p style='margin: 0'>This video talks about the law of large numbers.</p></td></tr><tr><td style='padding: 15px;'><p>4. Law of Large Numbers Famous Distributions</p><p style='margin: 0'>This video continues with the concept of law of large numbers.</p></td></tr><tr><td style='padding: 15px;'><p>5. Law of Large Numbers Famous Distributions Python</p><p style='margin: 0'>This video shows the implementation of law of large numbers concept in Python.</p></td></tr><tr><td style='padding: 15px;'><p>6. Variance</p><p style='margin: 0'>In this video, you will learn about the variance concept.</p></td></tr><tr><td style='padding: 15px;'><p>7. Homework</p><p style='margin: 0'>This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.</p></td></tr></tbody></table><br><h4>9. Project Bayes' Classifier</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Project Bayes' Classifier from Scratch</p><p style='margin: 0'>In this video, you will build the Bayes' classifier in Python from scratch.</p></td></tr></tbody></table><br><h4>10. Multiple Random Variables</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Joint Distributions</p><p style='margin: 0'>This video talks about joint distributions.</p></td></tr><tr><td style='padding: 15px;'><p>2. Multivariate Gaussian</p><p style='margin: 0'>This video explains about multivariate Gaussian.</p></td></tr><tr><td style='padding: 15px;'><p>3. Conditioning Independence</p><p style='margin: 0'>This video demonstrates conditioning in random variables.</p></td></tr><tr><td style='padding: 15px;'><p>4. Classification</p><p style='margin: 0'>This video shows you the objective of the course.</p></td></tr><tr><td style='padding: 15px;'><p>5. Naive Bayes' Classification</p><p style='margin: 0'>This video explains the concepts of Naive Bayes' classifier.</p></td></tr><tr><td style='padding: 15px;'><p>6. Regression</p><p style='margin: 0'>This video talks about the regression model.</p></td></tr><tr><td style='padding: 15px;'><p>7. Curse of Dimensionality</p><p style='margin: 0'>This video talks about curse of dimensionality.</p></td></tr><tr><td style='padding: 15px;'><p>8. Homework</p><p style='margin: 0'>This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.</p></td></tr></tbody></table><br><h4>11. Optional Estimation</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Parametric Distributions</p><p style='margin: 0'>This video explains about parametric distributions.</p></td></tr><tr><td style='padding: 15px;'><p>2. Maximum Likelihood Estimate (MLE)</p><p style='margin: 0'>This video explains about the Maximum Likelihood Estimate (MLE).</p></td></tr><tr><td style='padding: 15px;'><p>3. Log Likelihood</p><p style='margin: 0'>This video helps you learn about Log Likelihood.</p></td></tr><tr><td style='padding: 15px;'><p>4. Maximum A Posterior Estimate (MAP)</p><p style='margin: 0'>This video explains Maximum A Posterior Estimate (MAP).</p></td></tr><tr><td style='padding: 15px;'><p>5. Logistic Regression</p><p style='margin: 0'>This video helps you understand logistic regression in detail.</p></td></tr><tr><td style='padding: 15px;'><p>6. Ridge Regression</p><p style='margin: 0'>This video helps you understand ridge regression in detail.</p></td></tr><tr><td style='padding: 15px;'><p>7. Deep Neural Network (DNN)</p><p style='margin: 0'>This video focuses on Deep Neural Network (DNN) concept.</p></td></tr></tbody></table><br><h4>12. Mathematical Derivations for Math Lovers</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Permutations</p><p style='margin: 0'>This video showcases and explains the concept of permutations in detail.</p></td></tr><tr><td style='padding: 15px;'><p>2. Combinations</p><p style='margin: 0'>This video helps you learn about combination in detail.</p></td></tr><tr><td style='padding: 15px;'><p>3. Binomial Random Variable</p><p style='margin: 0'>This video explains about the binomial random variable.</p></td></tr><tr><td style='padding: 15px;'><p>4. Logistic Regression Formulation</p><p style='margin: 0'>This video explains logistic regression formulation.</p></td></tr><tr><td style='padding: 15px;'><p>5. Logistic Regression Derivation</p><p style='margin: 0'>This video explains logistic regression derivation.</p></td></tr></tbody></table>

This course is designed for beginners, although we will go deep gradually, and is a highly focused course designed to master your Python skills in probability and statistics, which covers the major part of machine learning or data science-related career opportunities.
In today's ultra-competitive business universe, probability and statistics are the most important fields of study. That is because statistical research presents businesses with the data they need to make informed decisions in every business area, whether it is market research, product development, product launch timing, customer data analysis, sales forecast, or employee performance. But why do you need to master probability and statistics in Python? The answer is that an expert grip on the concepts of statistics and probability with data science will enable you to take your career to the next level. This course is designed carefully to reflect the most in-demand skills that will help you in understanding the concepts and methodology with regard to Python. The course is as follows: Easy to understand
Expressive
Comprehensive
Practical with live coding
About establishing links between probability and machine learning By the end of this course, you will be able to relate the concepts and theories in machine learning with probabilistic reasoning and understand the methodology of statistics and probability with data science, using real datasets. The code files and all related files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Mastering-Probability-and-Statistics-in-Python
What You Will Learn
The importance of statistics and probability in data science
The foundations for machine learning and its roots in probability theory
The concepts of absolute beginning in-depth with examples in Python
Practical explanation and live coding with Python
Probabilistic view of modern machine learning
Implementation of Bayes' classifier on a real dataset
Audience
This course is for individuals who want to learn statistics and probability along with its implementation in realistic projects. Data scientists and business analysts and those who want to upgrade their data analysis skills will also get the benefit. People who want to learn statistics and probability with real datasets in data science and are passionate about numbers and programming will get the most out of this course.

No prior knowledge is needed. You start from the basics and gradually build your knowledge of the subject. A basic understanding of Python will be a plus but not mandatory.
Approach
This course is designed for beginners, although we will go deep gradually. You will learn theoretical concepts first, followed by its practical implementation in Python. At the end of each module, you will work on the homework/tasks, which will evaluate/further build your learning based on the previous concepts and methods.
Key Features
Easy explanations, yet complete and comprehensive course * Fundamental, pythonic, and a complete course to master the important concepts used in data science * Practical with live coding of the implementation of the concepts learned theoretically
Github Repo
https://github.com/PacktPublishing/Mastering-Probability-and-Statistics-in-Python
About the Author
AI Sciences
AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences. Their courses have successfully helped more than 100,000 students master AI and data science.
Course Outline
1. Introduction to the Course

1. Introduction to the Instructor
This video gives you an introduction to AI sciences and to the instructor of this course.
2. Focus of the Course
This video shows you the objective of the course.
2. Probability and Statistics

1. Probability Versus Statistics
This video focuses on probability versus statistics.
3. Sets

1. Definition of Set
This video demonstrates the definition of a set.
2. Cardinality of a Set
This video explains the cardinality of a set.
3. Subsets, Power Set, and Universal Set
This video explains subsets, power set, and universal set.
4. Python Practice Subsets
This video lets you practice on the subsets that you have learned so far.
5. Power Sets Solution
This video answers the problem statement to generate the power set.
6. Operations
This video explores the different types of operations that can be performed on the sets.
7. Python Practice Operations
This video lets you practice on the types of operations that you have learned so far.
8. Venn Diagrams Operations
This video shows the types of operations that can be performed on the Venn diagrams.
9. Homework
This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.
4. Experiment

1. Random Experiment
This video explains about a random experiment.
2. Outcome and Sample Space
This video explains what is meant by the outcome and sample space of any experiment.
3. Event
This video explains about the event in an experiment.
4. Recap and Homework
This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.
5. Probability Model

1. Probability model
This video talks about probability models.
2. Probability Axioms
This video explains axioms of probability.
3. Probability Axioms Derivations
This video covers derivation from axioms of probability.
4. Probability Models Example
This video shows examples of probability models.
5. More Examples of Probability Models
This video shows a few more examples of probability models.
6. Probability Models Continuous
This video talks about continuous probability models.
7. Conditional Probability
This video explains the concept of conditional probability.
8. Conditional Probability Example
This video shows an example of conditional probability.
9. Conditional Probability Formula
This video covers the formula for conditional probability.
10. Conditional Probability in Machine Learning
This video demonstrates the importance of conditional probability in Machine learning.
11. Conditional Probability Total Probability Theorem
This video covers the law of total probability or total probability theorem.
12. Probability Models Independence
This video explains one of the important probability theories, that is, "Independence".
13. Probability Models Conditional Independence
This video continues with the concept of Independence in the probability theory.
14. Probability Models Bayes' Rule
This video explains the Bayes' rule or theorem in probability theory.
15. Probability Models towards Random Variables
This video talks about random variables.
16. Homework
This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.
6. Random Variables

1. Introduction
This video provides an introduction to random variables.
2. Random Variables Examples
This video shows examples of random variables.
3. Bernoulli Random Variables
This video explains discrete random variables and you will also learn about the Bernoulli random variable.
4. Bernoulli Trail Python Practice
This video builds simulation for the Bernoulli random variable.
5. Geometric Random Variable
This video explains the concept of geometric random variable.
6. Geometric Random Variable Normalization Proof Optional
This video will continue to discuss the geometric random variable.
7. Geometric Random Variable Python Practice
This video shows the implementation of geometric random variable in Python.
8. Binomial Random Variables
This video explains the concept of binomial random variables.
9. Binomial Python Practice
This video shows the implementation of binomial random variable in Python.
10. Random Variables in Real Datasets
This video shows the real data sets and helps you build a relation of random variables on real datasets.
11. Homework
This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.
7. Continuous Random Variables

1. Zero Probability to Individual Values
This video talks about continuous random variables.
2. Probability Density Functions
This video explains probability density functions.
3. Uniform Distribution
This video explains uniform distribution.
4. Uniform Distribution Python
This video shows the implementation of uniform distribution in Python.
5. Exponential
This video explains about the exponential random variables.
6. Exponential Python
This video shows the implementation of exponential random variables in Python.
7. Gaussian Random Variables
This video explains about Gaussian random variables.
8. Gaussian Python
This video shows the implementation of Gaussian random variables in Python.
9. Transformation of Random Variables
This video explains about the transformation of random variables.
10. Homework
This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.
8. Expectations

1. Definition
This video defines expectations in detail.
2. Sample Mean
This video helps you compute the mean or average of the data in Python.
3. Law of Large Numbers
This video talks about the law of large numbers.
4. Law of Large Numbers Famous Distributions
This video continues with the concept of law of large numbers.
5. Law of Large Numbers Famous Distributions Python
This video shows the implementation of law of large numbers concept in Python.
6. Variance
In this video, you will learn about the variance concept.
7. Homework
This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.
9. Project Bayes' Classifier

1. Project Bayes' Classifier from Scratch
In this video, you will build the Bayes' classifier in Python from scratch.
10. Multiple Random Variables

1. Joint Distributions
This video talks about joint distributions.
2. Multivariate Gaussian
This video explains about multivariate Gaussian.
3. Conditioning Independence
This video demonstrates conditioning in random variables.
4. Classification
This video shows you the objective of the course.
5. Naive Bayes' Classification
This video explains the concepts of Naive Bayes' classifier.
6. Regression
This video talks about the regression model.
7. Curse of Dimensionality
This video talks about curse of dimensionality.
8. Homework
This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.
11. Optional Estimation

1. Parametric Distributions
This video explains about parametric distributions.
2. Maximum Likelihood Estimate (MLE)
This video explains about the Maximum Likelihood Estimate (MLE).
3. Log Likelihood
This video helps you learn about Log Likelihood.
4. Maximum A Posterior Estimate (MAP)
This video explains Maximum A Posterior Estimate (MAP).
5. Logistic Regression
This video helps you understand logistic regression in detail.
6. Ridge Regression
This video helps you understand ridge regression in detail.
7. Deep Neural Network (DNN)
This video focuses on Deep Neural Network (DNN) concept.
12. Mathematical Derivations for Math Lovers

1. Permutations
This video showcases and explains the concept of permutations in detail.
2. Combinations
This video helps you learn about combination in detail.
3. Binomial Random Variable
This video explains about the binomial random variable.
4. Logistic Regression Formulation
This video explains logistic regression formulation.
5. Logistic Regression Derivation
This video explains logistic regression derivation.

Data Science

Python

This course is designed for beginners, although we will go deep gradually, and is a highly focused course designed to master your Python skills in probability and statistics, which covers the major part of machine learning or data science-related career opportunities.

Mastering Probability and Statistics in Python

<strong>Master Data Science skills using Python and real time project and go from Beginner to Super Advance level</strong>
        <p>Want to become a good Data Scientist?  Then this is a right course for you.
This course was designed by IT professionals with a Master's in Mathematics and Data Science.  We cover complex theories, algorithms, and coding libraries in a very simple way so they can be easily grasped by any beginner.
We walk you step-by-step through the World of Data science. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science from beginner to advanced level.
All the codes and supporting files for this course are available at: https://github.com/PacktPublishing/Data-Statistics-with-Full-Stack-Python</p>
        <h2>What You Will Learn</h2><p>Master Data Science on Python<br>Learn to use Numpy and Pandas for Data Analysis<br>Learn all the math you need to understand Machine Learning algorithms<br>Real-world case studies<br>Learn to use Matplotlib for Python plotting<br>Learn to use Seaborn for statistical plots<br>Master end-to-end data science solutions<br>Learn all statistical concepts you need to become a Machine Learning ninja </p>
        <h2>Audience</h2><p>This course is for anyone who wants to become a Data Scientist.</p>
        <h2>Approach</h2><p>Exhaustive and packed with step-by-step instructions, working examples, and helpful advice, this course is divided into clear chunks so you can learn at your own pace and focus on your own area of interest.</p>
        <h2>Key Features</h2><p>Master complex theories, algorithms, and coding libraries in a very simple way * Master Data Science in Python</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/data-statistics-with-full-stack-python</p>
        <h2>About the Author</h2><h3>Geekshub Pvt. Ltd. </h3><p>Geekshub is an online education company in the field of big data and analytics. Their aim as a team is to provide the best skill-set to their customers to make them job-ready and prepare them to crack any challenge. They have the best trainers for cutting-edge technologies such as machine learning, deep learning, Natural Language Processing (NLP), reinforcement learning, and data science. Their instructors are people who graduated from IIT, MIT and Standford. They are passionate about teaching the topics using curated real-world case studies that calibrate the learning experience of students.</p>
        <h2>Course Outline</h2><h4>1. Python Fundamentals</h4><p></p><br><h4>2. Numpy</h4><p></p><br><h4>3. Pandas</h4><p></p><br><h4>4. Some Fun With Maths</h4><p></p><br><h4>5. Inferential Statistics</h4><p></p><br><h4>6. Hypothesis Testing</h4><p></p><br><h4>7. Data Visualization</h4><p></p><br><h4>8. Exploratory Data Analysis</h4><p></p><br><h4>9. Simple Linear Regression</h4><p></p><br><h4>10. Real World Problem - Investment Requirement Analysis for a Company</h4><p></p><br><h4>11. Loan Analysis Project</h4><p></p>

Master Data Science skills using Python and real time project and go from Beginner to Super Advance level
Want to become a good Data Scientist? Then this is a right course for you.
This course was designed by IT professionals with a Master's in Mathematics and Data Science. We cover complex theories, algorithms, and coding libraries in a very simple way so they can be easily grasped by any beginner.
We walk you step-by-step through the World of Data science. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science from beginner to advanced level.
All the codes and supporting files for this course are available at: https://github.com/PacktPublishing/Data-Statistics-with-Full-Stack-Python
What You Will Learn
Master Data Science on Python
Learn to use Numpy and Pandas for Data Analysis
Learn all the math you need to understand Machine Learning algorithms
Real-world case studies
Learn to use Matplotlib for Python plotting
Learn to use Seaborn for statistical plots
Master end-to-end data science solutions
Learn all statistical concepts you need to become a Machine Learning ninja 
Audience
This course is for anyone who wants to become a Data Scientist.
Approach
Exhaustive and packed with step-by-step instructions, working examples, and helpful advice, this course is divided into clear chunks so you can learn at your own pace and focus on your own area of interest.
Key Features
Master complex theories, algorithms, and coding libraries in a very simple way * Master Data Science in Python
Github Repo
https://github.com/packtpublishing/data-statistics-with-full-stack-python
About the Author
Geekshub Pvt. Ltd. 
Geekshub is an online education company in the field of big data and analytics. Their aim as a team is to provide the best skill-set to their customers to make them job-ready and prepare them to crack any challenge. They have the best trainers for cutting-edge technologies such as machine learning, deep learning, Natural Language Processing (NLP), reinforcement learning, and data science. Their instructors are people who graduated from IIT, MIT and Standford. They are passionate about teaching the topics using curated real-world case studies that calibrate the learning experience of students.
Course Outline
1. Python Fundamentals

2. Numpy

3. Pandas

4. Some Fun With Maths

5. Inferential Statistics

6. Hypothesis Testing

7. Data Visualization

8. Exploratory Data Analysis

9. Simple Linear Regression

10. Real World Problem - Investment Requirement Analysis for a Company

11. Loan Analysis Project


Master Data Science skills using Python and real time project and go from Beginner to Super Advance level

Data Statistics with Full Stack Python

<strong>Is statistics a driving force in the industry you want to enter? Do you want to work as a marketing analyst, a business intelligence analyst, a data analyst, or a data scientist?

Well then, you've come to the right place!</strong>
        <p>This course will teach you fundamental skills that will enable you to understand complicated statistical analysis directly applicable to real-life situations. Modern software packages and programming languages are now automating most of these activities, but this course gives you something more valuable-critical thinking abilities. This course will help you understand the fundamentals of statistics, learn how to work with different types of data, calculate correlation and covariance, and more.

Careers in the field of data science are some of the most popular in the corporate world today. And, given that most businesses are starting to realize the advantages of working with the data at their disposal, this trend will only continue to grow.

The course has been designed as follows:

Easy to understand
Comprehensive
Practical
To the point
Packed with plenty of exercises and resources
Data-driven
Introduces you to the statistical scientific lingo
Teaches you about data visualization
Shows you the main pillars of quant research

By the end of this course, you will acquire the fundamental skills that will enable you to understand complicated statistical analysis directly applicable to real-life situations. With this course, you will develop a habit of critical thinking that will take you miles ahead in your career.

The code files and all related files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Statistics-for-Data-Science-and-Business-Analysis-V-</p>
        <h2>What You Will Learn</h2><p>Understand the fundamentals of statistics<br>Work and plot with different types of data<br>Calculate the measures of central tendency, asymmetry, and variability<br>Calculate correlation and covariance<br>Perform hypothesis testing and make data-driven decisions<br>Carry out regression analysis along with using dummy variables</p>
        <h2>Audience</h2><p>This course targets anyone who wants a career in data science or business intelligence; individuals who are passionate about numbers and quant analysis; anyone who wants to learn the subtleties of statistics and how it is used in the business world; people who want to learn the fundamentals of statistics; business analysts; and business executives.<br><br>Absolutely no prior experience is required for this course. We will start from the basics and gradually build up your knowledge. Everything is in the course.</p>
        <h2>Approach</h2><p>A complete course (packed with extensive case studies, complete training, superb course materials, quiz questions, handouts, course notes, an HD video, and animations) that covers major statistical topics and helps you become a marketing analyst, a business intelligence analyst, a data analyst, or a data scientist.</p>
        <h2>Key Features</h2><p>Learn and understand the fundamentals of statistics for data science and business analysis * Practical tutorial with extensive case studies that will help to reinforce the learned concepts * High-quality production - HD video and animations along with a knowledgeable instructor</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Statistics-for-Data-Science-and-Business-Analysis-V-</p>
        <h2>About the Author</h2><h3>365 Careers Ltd.</h3><p>365 Careers' courses have been taken by more than 203,000 students in 204 countries. People working at world-class firms such as Apple, PayPal, and Citibank have completed 365 Careers trainings. By choosing 365 Careers, you make sure you will learn from proven experts who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.

If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers' courses are the perfect place to start.</p>
        <h2>Course Outline</h2><h4>1. Introduction to the Course</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Does the Course Cover?</p><p style='margin: 0'>This video describes about what the course is about and other details.</p></td></tr><tr><td style='padding: 15px;'><p>2. Understanding the Difference Between a Population and a Sample</p><p style='margin: 0'>This video helps in understanding the difference between a population and a sample. Furthermore, you will know the difference between a random sample and a representative sample.</p></td></tr></tbody></table><br><h4>2. Descriptive Statistics Fundamentals</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. The Various Types of Data We can Work With</p><p style='margin: 0'>This video explains about the various types of data to work with.</p></td></tr><tr><td style='padding: 15px;'><p>2. Levels of Measurement</p><p style='margin: 0'>In this video, the other classification of variables is shown - levels of measurement.</p></td></tr><tr><td style='padding: 15px;'><p>3. Categorical Variables and Visualization Techniques for Categorical Variables</p><p style='margin: 0'>This video looks into techniques for visualizing categorical variables; namely frequency distribution tables, bar charts, pie charts, and Pareto diagrams.</p></td></tr><tr><td style='padding: 15px;'><p>4. Numerical Variables and Using a Frequency Distribution Table</p><p style='margin: 0'>In this video, the frequency distribution table is explained.</p></td></tr><tr><td style='padding: 15px;'><p>5. Histogram Charts</p><p style='margin: 0'>Learn how to illustrate data with histograms.</p></td></tr><tr><td style='padding: 15px;'><p>6. Cross Tables and Scatter Plots</p><p style='margin: 0'>In this video, you will explore the different ways to demonstrate relationship between variables.</p></td></tr><tr><td style='padding: 15px;'><p>7. The Main Measures of Central Tendency: Mean, Median, Mode</p><p style='margin: 0'>This video will introduce you to the three measures of central tendency - mean, median, and mode.</p></td></tr><tr><td style='padding: 15px;'><p>8. Measuring Skewness</p><p style='margin: 0'>This video explains the most commonly used tool to measure asymmetry - skewness, and its relationship with the mean, median, and mode.</p></td></tr><tr><td style='padding: 15px;'><p>9. Measuring How Data is Spread Out: Calculating Variance</p><p style='margin: 0'>In this video, you will learn how to calculate variance.</p></td></tr><tr><td style='padding: 15px;'><p>10. Standard Deviation and Coefficient of Variation</p><p style='margin: 0'>This video explains standard deviation and coefficient of variation.</p></td></tr><tr><td style='padding: 15px;'><p>11. Calculating and Understanding Covariance</p><p style='margin: 0'>This video continues with the most common measure of interconnection between variables: covariance.</p></td></tr><tr><td style='padding: 15px;'><p>12. The Correlation Coefficient</p><p style='margin: 0'>This video explains correlation coefficient - the quantitative representation of correlation between variables.</p></td></tr><tr><td style='padding: 15px;'><p>13. Practical Example</p><p style='margin: 0'>This video contains a practical example on descriptive statistics.</p></td></tr></tbody></table><br><h4>3. Inferential Statistics Fundamentals</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Inferential Statistics</p><p style='margin: 0'>This video provides an introduction to inferential statistics.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is a Distribution?</p><p style='margin: 0'>This video explains what a distribution is, what types of distributions are there, and how this helps us better understand statistics.</p></td></tr><tr><td style='padding: 15px;'><p>3. The Normal Distribution</p><p style='margin: 0'>This video introduces the normal distribution and its great importance to statistics as a field.</p></td></tr><tr><td style='padding: 15px;'><p>4. The Standard Normal Distribution</p><p style='margin: 0'>This video explains the standard normal distribution by deriving it from the normal distribution through the method of standardization. You will also be elaborated on its use for testing.</p></td></tr><tr><td style='padding: 15px;'><p>5. Understanding the central limit theorem</p><p style='margin: 0'>This video is about the central limit theorem - one of the most important statistical concepts.</p></td></tr><tr><td style='padding: 15px;'><p>6. Standard Error</p><p style='margin: 0'>This video introduces the standard error - an important ingredient for making predictions.</p></td></tr><tr><td style='padding: 15px;'><p>7. Working with Estimators and Estimates</p><p style='margin: 0'>This video explores the estimators and estimates, and differentiates between the two concepts.</p></td></tr></tbody></table><br><h4>4. Confidence Intervals</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Confidence Intervals - an Invaluable Tool for Decision Making</p><p style='margin: 0'>This video explains confidence intervals in detail.</p></td></tr><tr><td style='padding: 15px;'><p>2. Calculating Confidence Intervals Within a Population with a Known Variance</p><p style='margin: 0'>In this video, you will see the first example of the use of confidence intervals and introduce the concept of the z-score.</p></td></tr><tr><td style='padding: 15px;'><p>3. Confidence Interval Clarifications</p><p style='margin: 0'>This video deep dives into the explanation of confidence intervals.</p></td></tr><tr><td style='padding: 15px;'><p>4. Student's T Distribution</p><p style='margin: 0'>This video is about the inception of the student's T distribution - a valuable tool when working with small samples.</p></td></tr><tr><td style='padding: 15px;'><p>5. Calculating Confidence Intervals Within a Population with an Unknown Variance</p><p style='margin: 0'>This video is about calculating confidence intervals within a population with an unknown variance.</p></td></tr><tr><td style='padding: 15px;'><p>6. What is a Margin of Error and Why is it Important in Statistics?</p><p style='margin: 0'>This video explains about margin of error and its importance in statistics.</p></td></tr><tr><td style='padding: 15px;'><p>7. Calculating Confidence Intervals for Two Means with Dependent Samples</p><p style='margin: 0'>This video shows real life examples of confidence intervals on dependent samples.</p></td></tr><tr><td style='padding: 15px;'><p>8. Calculating Confidence Intervals for Two Means with Independent Samples (Part 1)</p><p style='margin: 0'>This is the first of the three-part video, where you'll calculate confidence intervals for two means with independent samples.</p></td></tr><tr><td style='padding: 15px;'><p>9. Calculating Confidence Intervals for Two Means with Independent Samples (Part 2)</p><p style='margin: 0'>This is the second of the three-part video, where you'll calculate confidence intervals for two means with independent samples.</p></td></tr><tr><td style='padding: 15px;'><p>10. Calculating Confidence Intervals for Two Means with Independent Samples (Part 3)</p><p style='margin: 0'>This is the third of the three-part video, where you'll calculate confidence intervals for two means with independent samples.</p></td></tr><tr><td style='padding: 15px;'><p>11. Practical Example: Inferential Statistics</p><p style='margin: 0'>This video contains a practical example on inferential statistics.</p></td></tr></tbody></table><br><h4>5. Hypothesis Testing</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. The Null and the Alternative Hypothesis</p><p style='margin: 0'>This video explains null and alternative hypotheses. You will be shown different examples and explained how to form hypotheses that are later to be tested.</p></td></tr><tr><td style='padding: 15px;'><p>2. Establishing a Rejection Region and a Significance Level</p><p style='margin: 0'>This video explains the rationale behind testing. You will establish a rejection region and a significance level.</p></td></tr><tr><td style='padding: 15px;'><p>3. Type I Error Versus Type II Error</p><p style='margin: 0'>In this video, you'll learn about Type I error versus Type II error.</p></td></tr><tr><td style='padding: 15px;'><p>4. Test for the Mean; Population Variance Known</p><p style='margin: 0'>This video is about test for the mean when population variance is known.</p></td></tr><tr><td style='padding: 15px;'><p>5. What is P-Value and Why is it One of the Most Useful Tools for Statisticians?</p><p style='margin: 0'>In this video, you'll learn about p-value and understand why it is one of the most useful tools for statisticians.</p></td></tr><tr><td style='padding: 15px;'><p>6. Test for the Mean; Population Variance Unknown</p><p style='margin: 0'>In this video, using the new p-value notion, some t-tests are performed.</p></td></tr><tr><td style='padding: 15px;'><p>7. Test for the Mean; Dependent Samples</p><p style='margin: 0'>In this video, the mean for two dependent samples are tested.</p></td></tr><tr><td style='padding: 15px;'><p>8. Test for the Mean; Independent Samples (Part 1)</p><p style='margin: 0'>This is the first of the two-part video, where the means for two dependent samples are tested.</p></td></tr><tr><td style='padding: 15px;'><p>9. Test for the Mean; Independent Samples (Part 2)</p><p style='margin: 0'>This is the second of the two-part video, where the means for two dependent samples are tested.</p></td></tr><tr><td style='padding: 15px;'><p>10. Practical Example: Hypothesis Testing</p><p style='margin: 0'>This video is a practical example on hypothesis testing.</p></td></tr></tbody></table><br><h4>6. The Fundamentals of Regression Analysis</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Regression Analysis</p><p style='margin: 0'>In this video, you will learn the theoretical foundations of simple regression analysis.</p></td></tr><tr><td style='padding: 15px;'><p>2. Correlation and Causation</p><p style='margin: 0'>In this video, you will learn about correlation and causation.</p></td></tr><tr><td style='padding: 15px;'><p>3. The Linear Regression Model Made Easy</p><p style='margin: 0'>In this video, you will learn about the linear regression model.</p></td></tr><tr><td style='padding: 15px;'><p>4. What is the Difference Between Correlation and Regression?</p><p style='margin: 0'>In this video, you will learn about correlation and regression.</p></td></tr><tr><td style='padding: 15px;'><p>5. A Geometrical Representation of the Linear Regression Model</p><p style='margin: 0'>In this video, the geometrical representation of linear regression model is shown.</p></td></tr><tr><td style='padding: 15px;'><p>6. A Practical Example - Reinforced Learning</p><p style='margin: 0'>In this video, you will learn a practical example of reinforced learning.</p></td></tr></tbody></table><br><h4>7. Subtleties of Regression Analysis</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Decomposing the Linear Regression Model - Understanding its Nuts and Bolts</p><p style='margin: 0'>This video explains about decomposing the linear regression model - understanding its nuts and bolts.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is R-Squared and How Does it Help Us?</p><p style='margin: 0'>In this video, you will learn about R-squared and its features.</p></td></tr><tr><td style='padding: 15px;'><p>3. The Ordinary Least Squares Setting and its Practical Applications</p><p style='margin: 0'>In this video, you will learn about ordinary least squares setting and its practical applications.</p></td></tr><tr><td style='padding: 15px;'><p>4. Studying Regression Tables</p><p style='margin: 0'>In this video, you'll learn about regression tables.</p></td></tr><tr><td style='padding: 15px;'><p>5. The Multiple Linear Regression Model</p><p style='margin: 0'>In this video, you will learn about multiple linear regression models.</p></td></tr><tr><td style='padding: 15px;'><p>6. Adjusted R-Squared</p><p style='margin: 0'>Learn about adjusted R-squared.</p></td></tr><tr><td style='padding: 15px;'><p>7. What Does the F-Statistic Show Us and Why Do We Need to Understand It?</p><p style='margin: 0'>Learn about F-statistic in detail.</p></td></tr></tbody></table><br><h4>8. Assumptions for Linear Regression Analysis</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. OLS Assumptions</p><p style='margin: 0'>This video explains OLS assumptions in detail.</p></td></tr><tr><td style='padding: 15px;'><p>2. A1. Linearity</p><p style='margin: 0'>In this video, the first assumption of OLS, linearity, is explained.</p></td></tr><tr><td style='padding: 15px;'><p>3. A2. No Endogeneity</p><p style='margin: 0'>In this video, the second assumption of OLS, no endogeneity, is explained.</p></td></tr><tr><td style='padding: 15px;'><p>4. A3. Normality and Homoscedasticity</p><p style='margin: 0'>In this video, the third assumption of OLS, normality and homoscedasticity, are explained.</p></td></tr><tr><td style='padding: 15px;'><p>5. A4. No Autocorrelation</p><p style='margin: 0'>In this video, the fourth assumption of OLS, no autocorrelation, is explained.</p></td></tr><tr><td style='padding: 15px;'><p>6. A5. No Multicollinearity</p><p style='margin: 0'>This video is about the final assumption-no multicollinearity.</p></td></tr></tbody></table><br><h4>9. Dealing with Categorical Data</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Dummy Variables</p><p style='margin: 0'>In this video, you'll learn about dummy variables.</p></td></tr></tbody></table><br><h4>10. Practical Example: Regression Analysis</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Practical Example: Regression Analysis</p><p style='margin: 0'>This video shows a practical example of regression analysis.</p></td></tr></tbody></table>

Is statistics a driving force in the industry you want to enter? Do you want to work as a marketing analyst, a business intelligence analyst, a data analyst, or a data scientist? Well then, you've come to the right place!
This course will teach you fundamental skills that will enable you to understand complicated statistical analysis directly applicable to real-life situations. Modern software packages and programming languages are now automating most of these activities, but this course gives you something more valuable-critical thinking abilities. This course will help you understand the fundamentals of statistics, learn how to work with different types of data, calculate correlation and covariance, and more. Careers in the field of data science are some of the most popular in the corporate world today. And, given that most businesses are starting to realize the advantages of working with the data at their disposal, this trend will only continue to grow. The course has been designed as follows: Easy to understand
Comprehensive
Practical
To the point
Packed with plenty of exercises and resources
Data-driven
Introduces you to the statistical scientific lingo
Teaches you about data visualization
Shows you the main pillars of quant research By the end of this course, you will acquire the fundamental skills that will enable you to understand complicated statistical analysis directly applicable to real-life situations. With this course, you will develop a habit of critical thinking that will take you miles ahead in your career. The code files and all related files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Statistics-for-Data-Science-and-Business-Analysis-V-
What You Will Learn
Understand the fundamentals of statistics
Work and plot with different types of data
Calculate the measures of central tendency, asymmetry, and variability
Calculate correlation and covariance
Perform hypothesis testing and make data-driven decisions
Carry out regression analysis along with using dummy variables
Audience
This course targets anyone who wants a career in data science or business intelligence; individuals who are passionate about numbers and quant analysis; anyone who wants to learn the subtleties of statistics and how it is used in the business world; people who want to learn the fundamentals of statistics; business analysts; and business executives.

Absolutely no prior experience is required for this course. We will start from the basics and gradually build up your knowledge. Everything is in the course.
Approach
A complete course (packed with extensive case studies, complete training, superb course materials, quiz questions, handouts, course notes, an HD video, and animations) that covers major statistical topics and helps you become a marketing analyst, a business intelligence analyst, a data analyst, or a data scientist.
Key Features
Learn and understand the fundamentals of statistics for data science and business analysis * Practical tutorial with extensive case studies that will help to reinforce the learned concepts * High-quality production - HD video and animations along with a knowledgeable instructor
Github Repo
https://github.com/PacktPublishing/Statistics-for-Data-Science-and-Business-Analysis-V-
About the Author
365 Careers Ltd.
365 Careers' courses have been taken by more than 203,000 students in 204 countries. People working at world-class firms such as Apple, PayPal, and Citibank have completed 365 Careers trainings. By choosing 365 Careers, you make sure you will learn from proven experts who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time. If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers' courses are the perfect place to start.
Course Outline
1. Introduction to the Course

1. What Does the Course Cover?
This video describes about what the course is about and other details.
2. Understanding the Difference Between a Population and a Sample
This video helps in understanding the difference between a population and a sample. Furthermore, you will know the difference between a random sample and a representative sample.
2. Descriptive Statistics Fundamentals

1. The Various Types of Data We can Work With
This video explains about the various types of data to work with.
2. Levels of Measurement
In this video, the other classification of variables is shown - levels of measurement.
3. Categorical Variables and Visualization Techniques for Categorical Variables
This video looks into techniques for visualizing categorical variables; namely frequency distribution tables, bar charts, pie charts, and Pareto diagrams.
4. Numerical Variables and Using a Frequency Distribution Table
In this video, the frequency distribution table is explained.
5. Histogram Charts
Learn how to illustrate data with histograms.
6. Cross Tables and Scatter Plots
In this video, you will explore the different ways to demonstrate relationship between variables.
7. The Main Measures of Central Tendency: Mean, Median, Mode
This video will introduce you to the three measures of central tendency - mean, median, and mode.
8. Measuring Skewness
This video explains the most commonly used tool to measure asymmetry - skewness, and its relationship with the mean, median, and mode.
9. Measuring How Data is Spread Out: Calculating Variance
In this video, you will learn how to calculate variance.
10. Standard Deviation and Coefficient of Variation
This video explains standard deviation and coefficient of variation.
11. Calculating and Understanding Covariance
This video continues with the most common measure of interconnection between variables: covariance.
12. The Correlation Coefficient
This video explains correlation coefficient - the quantitative representation of correlation between variables.
13. Practical Example
This video contains a practical example on descriptive statistics.
3. Inferential Statistics Fundamentals

1. Introduction to Inferential Statistics
This video provides an introduction to inferential statistics.
2. What is a Distribution?
This video explains what a distribution is, what types of distributions are there, and how this helps us better understand statistics.
3. The Normal Distribution
This video introduces the normal distribution and its great importance to statistics as a field.
4. The Standard Normal Distribution
This video explains the standard normal distribution by deriving it from the normal distribution through the method of standardization. You will also be elaborated on its use for testing.
5. Understanding the central limit theorem
This video is about the central limit theorem - one of the most important statistical concepts.
6. Standard Error
This video introduces the standard error - an important ingredient for making predictions.
7. Working with Estimators and Estimates
This video explores the estimators and estimates, and differentiates between the two concepts.
4. Confidence Intervals

1. Confidence Intervals - an Invaluable Tool for Decision Making
This video explains confidence intervals in detail.
2. Calculating Confidence Intervals Within a Population with a Known Variance
In this video, you will see the first example of the use of confidence intervals and introduce the concept of the z-score.
3. Confidence Interval Clarifications
This video deep dives into the explanation of confidence intervals.
4. Student's T Distribution
This video is about the inception of the student's T distribution - a valuable tool when working with small samples.
5. Calculating Confidence Intervals Within a Population with an Unknown Variance
This video is about calculating confidence intervals within a population with an unknown variance.
6. What is a Margin of Error and Why is it Important in Statistics?
This video explains about margin of error and its importance in statistics.
7. Calculating Confidence Intervals for Two Means with Dependent Samples
This video shows real life examples of confidence intervals on dependent samples.
8. Calculating Confidence Intervals for Two Means with Independent Samples (Part 1)
This is the first of the three-part video, where you'll calculate confidence intervals for two means with independent samples.
9. Calculating Confidence Intervals for Two Means with Independent Samples (Part 2)
This is the second of the three-part video, where you'll calculate confidence intervals for two means with independent samples.
10. Calculating Confidence Intervals for Two Means with Independent Samples (Part 3)
This is the third of the three-part video, where you'll calculate confidence intervals for two means with independent samples.
11. Practical Example: Inferential Statistics
This video contains a practical example on inferential statistics.
5. Hypothesis Testing

1. The Null and the Alternative Hypothesis
This video explains null and alternative hypotheses. You will be shown different examples and explained how to form hypotheses that are later to be tested.
2. Establishing a Rejection Region and a Significance Level
This video explains the rationale behind testing. You will establish a rejection region and a significance level.
3. Type I Error Versus Type II Error
In this video, you'll learn about Type I error versus Type II error.
4. Test for the Mean; Population Variance Known
This video is about test for the mean when population variance is known.
5. What is P-Value and Why is it One of the Most Useful Tools for Statisticians?
In this video, you'll learn about p-value and understand why it is one of the most useful tools for statisticians.
6. Test for the Mean; Population Variance Unknown
In this video, using the new p-value notion, some t-tests are performed.
7. Test for the Mean; Dependent Samples
In this video, the mean for two dependent samples are tested.
8. Test for the Mean; Independent Samples (Part 1)
This is the first of the two-part video, where the means for two dependent samples are tested.
9. Test for the Mean; Independent Samples (Part 2)
This is the second of the two-part video, where the means for two dependent samples are tested.
10. Practical Example: Hypothesis Testing
This video is a practical example on hypothesis testing.
6. The Fundamentals of Regression Analysis

1. Introduction to Regression Analysis
In this video, you will learn the theoretical foundations of simple regression analysis.
2. Correlation and Causation
In this video, you will learn about correlation and causation.
3. The Linear Regression Model Made Easy
In this video, you will learn about the linear regression model.
4. What is the Difference Between Correlation and Regression?
In this video, you will learn about correlation and regression.
5. A Geometrical Representation of the Linear Regression Model
In this video, the geometrical representation of linear regression model is shown.
6. A Practical Example - Reinforced Learning
In this video, you will learn a practical example of reinforced learning.
7. Subtleties of Regression Analysis

1. Decomposing the Linear Regression Model - Understanding its Nuts and Bolts
This video explains about decomposing the linear regression model - understanding its nuts and bolts.
2. What is R-Squared and How Does it Help Us?
In this video, you will learn about R-squared and its features.
3. The Ordinary Least Squares Setting and its Practical Applications
In this video, you will learn about ordinary least squares setting and its practical applications.
4. Studying Regression Tables
In this video, you'll learn about regression tables.
5. The Multiple Linear Regression Model
In this video, you will learn about multiple linear regression models.
6. Adjusted R-Squared
Learn about adjusted R-squared.
7. What Does the F-Statistic Show Us and Why Do We Need to Understand It?
Learn about F-statistic in detail.
8. Assumptions for Linear Regression Analysis

1. OLS Assumptions
This video explains OLS assumptions in detail.
2. A1. Linearity
In this video, the first assumption of OLS, linearity, is explained.
3. A2. No Endogeneity
In this video, the second assumption of OLS, no endogeneity, is explained.
4. A3. Normality and Homoscedasticity
In this video, the third assumption of OLS, normality and homoscedasticity, are explained.
5. A4. No Autocorrelation
In this video, the fourth assumption of OLS, no autocorrelation, is explained.
6. A5. No Multicollinearity
This video is about the final assumption-no multicollinearity.
9. Dealing with Categorical Data

1. Dummy Variables
In this video, you'll learn about dummy variables.
10. Practical Example: Regression Analysis

1. Practical Example: Regression Analysis
This video shows a practical example of regression analysis.

Business Analysis

Is statistics a driving force in the industry you want to enter? Do you want to work as a marketing analyst, a business intelligence analyst, a data analyst, or a data scientist?

Well then, you've come to the right place!

Statistics for Data Science and Business Analysis

<strong>This course is a perfect supplement for ML enthusiasts. If you are only just beginning your adventures in machine learning and want to know the basics of statistics and regression used for machine learning, then go for it. Discover how you can level up and gain confidence to implement statistical methods and regression in machine learning with Python.</strong>
        <p>This course is for ML enthusiasts who want to understand basic statistics and regression for machine learning. The course starts with setting up the environment and understanding the basics of Python language and different libraries. Next, you'll see the basics of machine learning and different types of data. After that, you'll learn a statistics technique called Central Tendency Analysis.

Post this, you'll focus on statistical techniques such as variance and standard deviation. Several techniques and mathematical concepts such as percentile, normal distribution, uniform distribution, finding z-score, linear regression, polynomial linear regression, and multiple regression with the help of manual calculation and Python functions are introduced as the course progresses.

The dataset will get more complex as you proceed ahead; you'll use a CSV file to save the dataset. You'll see the traditional and complex method of finding the coefficient of regression and then explore ways to solve it easily with some Python functions.

Finally, you'll learn a technique called data normalization or standardization, which will improve the performance of the algorithms very much compared to a non-scaled dataset.

By the end of this course, you'll gain a solid foundation in machine learning and statistical regression using Python.

All the code files and related files are available on the GitHub repository at https://github.com/PacktPublishing/Basic-Statistics-and-Regression-for-Machine-Learning-in-Python</p>
        <h2>What You Will Learn</h2><p>Set up the environment<br>Learn central tendency analysis<br>Learn statistical models and analysis<br>Learn regression models and analysis<br>Use NumPy, matplotlib, and scikit-learn libraries<br>Learn the data normalization or standardization technique</p>
        <h2>Audience</h2><p>This course is for beginners and individuals who want to learn mathematics for machine learning. You need not have any prior experience or knowledge in coding; just be ready with your learning mindset at the highest level.<br><br>Individuals interested in learning what's actually happening behind the scenes of Python functions and algorithms (at least in a shallow layman's way) will be highly benefitted.<br><br>Basic computer knowledge and an interest to learn mathematics for machine learning is the only prerequisite for this course.</p>
        <h2>Approach</h2><p>This is a comprehensive and hands-on course to learn from basic to advanced mathematics and statistical concepts that cover machine learning algorithms. The instructor will take you through every step of the code.<br><br>The instructor shows both the manual calculation approach and then the Python functions to work around in solving statistical and regression problems.</p>
        <h2>Key Features</h2><p>A comprehensive course that includes Python coding, visualization, loops, variables, and functions * Manual calculation and then using Python functions/codes to understand the difference * Beginner to advanced mathematics and statistical concepts that cover machine learning algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Basic-Statistics-and-Regression-for-Machine-Learning-in-Python</p>
        <h2>About the Author</h2><h3>Abhilash Nelson</h3><p>Abhilash Nelson is a pioneering, talented, and security-oriented Android/iOS mobile and PHP/Python web application developer with more than 8 years of IT experience involving designing, implementing, integrating, testing, and supporting impactful web and mobile applications. He has a master's degree in computer science and engineering and has PHP/Python programming experience, which is an added advantage for server-based Android and iOS client applications. Abhilash is currently a senior solution architect managing projects from start to finish to ensure high quality and innovative and functional design.</p>
        <h2>Course Outline</h2><h4>1. Course Introduction and Table of Contents</h4><p></p><br><h4>2. Environment Setup: Preparing  your Computer</h4><p></p><br><h4>3. Essential Components Included in Anaconda</h4><p></p><br><h4>4. Python Basics - Assignment</h4><p></p><br><h4>5. Python Basics - Flow Control</h4><p></p><br><h4>6. Python Basics - List and Tuples</h4><p></p><br><h4>7. Python Basics - Dictionary and Functions</h4><p></p><br><h4>8. Numpy Basics</h4><p></p><br><h4>9. Matplotlib Basics</h4><p></p><br><h4>10. Basics of Data for Machine Learning</h4><p></p><br><h4>11. Central Data Tendency - Mean</h4><p></p><br><h4>12. Central Data Tendency - Median and Mode</h4><p></p><br><h4>13. Variance and Standard Deviation Manual Calculation</h4><p></p><br><h4>14. Variance and Standard Deviation using Python</h4><p></p><br><h4>15. Percentile Manual Calculation</h4><p></p><br><h4>16. Percentile using Python</h4><p></p><br><h4>17. Uniform Distribution</h4><p></p><br><h4>18. Normal Distribution</h4><p></p><br><h4>19. Manual Z score calculation</h4><p></p><br><h4>20. Z score calculation using python</h4><p></p><br><h4>21. Multi Variable Dataset Scatter Plot</h4><p></p><br><h4>22. Introduction to Linear Regression</h4><p></p><br><h4>23. Manually finding Linear Regression Correlation Coefficient</h4><p></p><br><h4>24. Manually finding Linear Regression Slope Equation</h4><p></p><br><h4>25. Manually Predicting the Future Value using Equation</h4><p></p><br><h4>26. Linear Regression using Python Introduction</h4><p></p><br><h4>27. Linear Regression using Python</h4><p></p><br><h4>28. Strong and Weak Linear Regression</h4><p></p><br><h4>29. Predicting Future value using Linear Regression in Python</h4><p></p><br><h4>30. Polynomial Regression Introduction</h4><p></p><br><h4>31. Polynomial Regression Visualization</h4><p></p><br><h4>32. Polynomial Regression Prediction and R2 value</h4><p></p><br><h4>33. Polynomial Regression Finding SD Components</h4><p></p><br><h4>34. Polynomial Regression Manual Method Equations</h4><p></p><br><h4>35. Finding SD components for abc</h4><p></p><br><h4>36. Finding abc</h4><p></p><br><h4>37. Polynomial Regression Equation and Prediction</h4><p></p><br><h4>38. Polynomial Regression coefficient</h4><p></p><br><h4>39. Multiple Regression Introduction</h4><p></p><br><h4>40. Multiple Regression using Python - Part 1 - Data Import as CSV</h4><p></p><br><h4>41. Multiple Regression using Python - Part 2 - Data Visualization</h4><p></p><br><h4>42. Creating Multiple Regression Object and Prediction using Python</h4><p></p><br><h4>43. Manual Multiple Regression - Intro and Finding Means</h4><p></p><br><h4>44. Manual Multiple Regression - Finding Components</h4><p></p><br><h4>45. Manual Multiple Regression - Finding a b c</h4><p></p><br><h4>46. Manual Multiple Regression Equation Prediction and Coefficients</h4><p></p><br><h4>47. Feature Scaling Introduction</h4><p></p><br><h4>48. Standardization Scaling using Python</h4><p></p><br><h4>49. Standardization Scaling using Manual Calculation</h4><p></p><br><h4>50. Further Learning References and Resource Download</h4><p></p>

This course is a perfect supplement for ML enthusiasts. If you are only just beginning your adventures in machine learning and want to know the basics of statistics and regression used for machine learning, then go for it. Discover how you can level up and gain confidence to implement statistical methods and regression in machine learning with Python.
This course is for ML enthusiasts who want to understand basic statistics and regression for machine learning. The course starts with setting up the environment and understanding the basics of Python language and different libraries. Next, you'll see the basics of machine learning and different types of data. After that, you'll learn a statistics technique called Central Tendency Analysis. Post this, you'll focus on statistical techniques such as variance and standard deviation. Several techniques and mathematical concepts such as percentile, normal distribution, uniform distribution, finding z-score, linear regression, polynomial linear regression, and multiple regression with the help of manual calculation and Python functions are introduced as the course progresses. The dataset will get more complex as you proceed ahead; you'll use a CSV file to save the dataset. You'll see the traditional and complex method of finding the coefficient of regression and then explore ways to solve it easily with some Python functions. Finally, you'll learn a technique called data normalization or standardization, which will improve the performance of the algorithms very much compared to a non-scaled dataset. By the end of this course, you'll gain a solid foundation in machine learning and statistical regression using Python. All the code files and related files are available on the GitHub repository at https://github.com/PacktPublishing/Basic-Statistics-and-Regression-for-Machine-Learning-in-Python
What You Will Learn
Set up the environment
Learn central tendency analysis
Learn statistical models and analysis
Learn regression models and analysis
Use NumPy, matplotlib, and scikit-learn libraries
Learn the data normalization or standardization technique
Audience
This course is for beginners and individuals who want to learn mathematics for machine learning. You need not have any prior experience or knowledge in coding; just be ready with your learning mindset at the highest level.

Individuals interested in learning what's actually happening behind the scenes of Python functions and algorithms (at least in a shallow layman's way) will be highly benefitted.

Basic computer knowledge and an interest to learn mathematics for machine learning is the only prerequisite for this course.
Approach
This is a comprehensive and hands-on course to learn from basic to advanced mathematics and statistical concepts that cover machine learning algorithms. The instructor will take you through every step of the code.

The instructor shows both the manual calculation approach and then the Python functions to work around in solving statistical and regression problems.
Key Features
A comprehensive course that includes Python coding, visualization, loops, variables, and functions * Manual calculation and then using Python functions/codes to understand the difference * Beginner to advanced mathematics and statistical concepts that cover machine learning algorithms
Github Repo
https://github.com/PacktPublishing/Basic-Statistics-and-Regression-for-Machine-Learning-in-Python
About the Author
Abhilash Nelson
Abhilash Nelson is a pioneering, talented, and security-oriented Android/iOS mobile and PHP/Python web application developer with more than 8 years of IT experience involving designing, implementing, integrating, testing, and supporting impactful web and mobile applications. He has a master's degree in computer science and engineering and has PHP/Python programming experience, which is an added advantage for server-based Android and iOS client applications. Abhilash is currently a senior solution architect managing projects from start to finish to ensure high quality and innovative and functional design.
Course Outline
1. Course Introduction and Table of Contents

2. Environment Setup: Preparing your Computer

3. Essential Components Included in Anaconda

4. Python Basics - Assignment

5. Python Basics - Flow Control

6. Python Basics - List and Tuples

7. Python Basics - Dictionary and Functions

8. Numpy Basics

9. Matplotlib Basics

10. Basics of Data for Machine Learning

11. Central Data Tendency - Mean

12. Central Data Tendency - Median and Mode

13. Variance and Standard Deviation Manual Calculation

14. Variance and Standard Deviation using Python

15. Percentile Manual Calculation

16. Percentile using Python

17. Uniform Distribution

18. Normal Distribution

19. Manual Z score calculation

20. Z score calculation using python

21. Multi Variable Dataset Scatter Plot

22. Introduction to Linear Regression

23. Manually finding Linear Regression Correlation Coefficient

24. Manually finding Linear Regression Slope Equation

25. Manually Predicting the Future Value using Equation

26. Linear Regression using Python Introduction

27. Linear Regression using Python

28. Strong and Weak Linear Regression

29. Predicting Future value using Linear Regression in Python

30. Polynomial Regression Introduction

31. Polynomial Regression Visualization

32. Polynomial Regression Prediction and R2 value

33. Polynomial Regression Finding SD Components

34. Polynomial Regression Manual Method Equations

35. Finding SD components for abc

36. Finding abc

37. Polynomial Regression Equation and Prediction

38. Polynomial Regression coefficient

39. Multiple Regression Introduction

40. Multiple Regression using Python - Part 1 - Data Import as CSV

41. Multiple Regression using Python - Part 2 - Data Visualization

42. Creating Multiple Regression Object and Prediction using Python

43. Manual Multiple Regression - Intro and Finding Means

44. Manual Multiple Regression - Finding Components

45. Manual Multiple Regression - Finding a b c

46. Manual Multiple Regression Equation Prediction and Coefficients

47. Feature Scaling Introduction

48. Standardization Scaling using Python

49. Standardization Scaling using Manual Calculation

50. Further Learning References and Resource Download


Programming

This course is a perfect supplement for ML enthusiasts. If you are only just beginning your adventures in machine learning and want to know the basics of statistics and regression used for machine learning, then go for it. Discover how you can level up and gain confidence to implement statistical methods and regression in machine learning with Python.

Basic Statistics and Regression for Machine Learning in Python

<strong>Learn complete hands-on Regression analysis for practical Statistical modelling and Machine Learning in R</strong>
        <p>With so many R Statistics and Machine Learning courses around, why enroll for this?

Regression analysis is one of the central aspects of both Statistics and Machine Learning based analysis. This course will teach you Regression analysis for both Statistical data analysis and ML in R. It explores relevant concepts in a practical way, from basic to expert level. This course can help you achieve better grades, gain new analysis tools for your academic career, implement your knowledge in a work setting, and make business forecasting-related decisions. You will go all the way from implementing and inferring simple OLS (Ordinary Least Square) regression models to dealing with issues of multicollinearity in regression to ML based regression models.

Become a Regression analysis expert and harness the power of R for your analysis

&bull; Get started with R and RStudio. Install these on your system, learn to load packages, and read in different types of data in R
&bull; Carry out data cleaning and data visualization using R
&bull; Implement Ordinary Least Square (OLS) regression in R and learn how to interpret the results.
&bull; Learn how to deal with multicollinearity both through the variable selection and regularization techniques such as ridge regression
&bull; Carry out variable and regression model selection using both statistical and machine learning techniques, including using cross-validation methods.
&bull; Evaluate the regression model accuracy
&bull; Implement Generalized Linear Models (GLMs) such as logistic regression and Poisson regression. Use logistic regression as a binary classifier to distinguish between male and female voices.
&bull; Use non-parametric techniques such as Generalized Additive Models (GAMs) to work with non-linear and non-parametric data.
&bull; Work with tree-based ML models

All the code and supporting files for this course are available at - https://github.com/PacktPublishing/Regression-Analysis-for-Statistics-and-Machine-Learning-in-R</p>
        <h2>What You Will Learn</h2><p>Implement and infer Ordinary Least Square (OLS) regression using R<br>Apply statistical and ML based regression models to deal with problems such as multicollinearity<br>Carry out the variable selection and assess model accuracy using techniques such as cross-validation<br>Implement and infer Generalized Linear Models (GLMs), including using logistic regression as a binary classifier</p>
        <h2>Audience</h2><p>This course is for students interested in getting started with data science applications in the RStudio environment. Students wishing to learn to implement statistical modeling techniques for regression analysis on real data. Anyone with prior exposure to R who wants to get started with practical data science.</p>
        <h2>Approach</h2><p>This is a practical, hands-on course. We will spend some time dealing with some theoretical concepts related to both statistical and machine learning regression analysis. However, the majority of the course will focus on implementing different techniques on real data and interpreting the results. After each video, you will learn a new concept or technique which you can apply to your own projects.</p>
        <h2>Key Features</h2><p>Provided practical indepth training for you to get started with R * Perform some of the most common advanced regression analysis-based techniques * Use R to perform different statistical and machine learning data analysis and visualization tasks</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/regression-analysis-for-statistics-and-machine-learning-in-r</p>
        <h2>About the Author</h2><h3>Minerva Singh</h3><p>Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a part-time Data Scientist. As part of her research, she must carry out extensive data analysis, including spatial data analysis. For this purpose, she prefers to use a combination of freeware tools: R, QGIS, and Python. She does most of her spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing, and analysis. She also holds an MPhil degree in Geography and Environment from Oxford University. She has honed her statistical and data analysis skills through several MOOCs, including The Analytics Edge and Statistical. In addition to spatial data analysis, she is also proficient in statistical analysis, machine learning, and data mining.</p>
        <h2>Course Outline</h2><h4>1. Get Started with Practical Regression Analysis in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools</p><p style='margin: 0'>Get Started with Practical Regression Analysis in R: INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools</p></td></tr><tr><td style='padding: 15px;'><p>2. Difference Between Statistical Analysis & Machine Learning</p><p style='margin: 0'>Get Started with Practical Regression Analysis in R: Difference Between Statistical Analysis &amp; Machine Learning</p></td></tr><tr><td style='padding: 15px;'><p>3. Getting Started with R and R Studio</p><p style='margin: 0'>Get Started with Practical Regression Analysis in R: Getting Started with R and R Studio</p></td></tr><tr><td style='padding: 15px;'><p>4. Reading in Data with R</p><p style='margin: 0'>Get Started with Practical Regression Analysis in R: Reading in Data with R</p></td></tr><tr><td style='padding: 15px;'><p>5. Data Cleaning with R</p><p style='margin: 0'>Get Started with Practical Regression Analysis in R: Data Cleaning with R</p></td></tr><tr><td style='padding: 15px;'><p>6. Some More Data Cleaning with R</p><p style='margin: 0'>Get Started with Practical Regression Analysis in R: Some More Data Cleaning with R</p></td></tr><tr><td style='padding: 15px;'><p>7. Basic Exploratory Data Analysis in R</p><p style='margin: 0'>Get Started with Practical Regression Analysis in R: Basic Exploratory Data Analysis in R</p></td></tr><tr><td style='padding: 15px;'><p>8. Conclusion to Section 1</p><p style='margin: 0'>Get Started with Practical Regression Analysis in R: Conclusion to Section 1</p></td></tr></tbody></table><br><h4>2. Ordinary Least Square Regression Modelling</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. OLS Regression- Theory</p><p style='margin: 0'>Ordinary Least Square Regression Modelling: OLS Regression- Theory</p></td></tr><tr><td style='padding: 15px;'><p>2. OLS-Implementation</p><p style='margin: 0'>Ordinary Least Square Regression Modelling: OLS-Implementation</p></td></tr><tr><td style='padding: 15px;'><p>3. More on Result Interpretations</p><p style='margin: 0'>Ordinary Least Square Regression Modelling: More on Result Interpretations</p></td></tr><tr><td style='padding: 15px;'><p>4. Confidence Interval-Theory</p><p style='margin: 0'>Ordinary Least Square Regression Modelling: Confidence Interval-Theory</p></td></tr><tr><td style='padding: 15px;'><p>5. Calculate the Confidence Interval in R</p><p style='margin: 0'>Ordinary Least Square Regression Modelling: Calculate the Confidence Interval in R</p></td></tr><tr><td style='padding: 15px;'><p>6. Confidence Interval and OLS Regressions</p><p style='margin: 0'>Ordinary Least Square Regression Modelling: Confidence Interval and OLS Regressions</p></td></tr><tr><td style='padding: 15px;'><p>7. Linear Regression without Intercept</p><p style='margin: 0'>Ordinary Least Square Regression Modelling: Linear Regression without Intercept</p></td></tr><tr><td style='padding: 15px;'><p>8. Implement ANOVA on OLS Regression</p><p style='margin: 0'>Ordinary Least Square Regression Modelling: Implement ANOVA on OLS Regression</p></td></tr><tr><td style='padding: 15px;'><p>9. Multiple Linear Regression</p><p style='margin: 0'>Ordinary Least Square Regression Modelling: Multiple Linear Regression</p></td></tr><tr><td style='padding: 15px;'><p>10. Multiple Linear regression with Interaction and Dummy Variables</p><p style='margin: 0'>Ordinary Least Square Regression Modelling: Multiple Linear regression with Interaction and Dummy Variables</p></td></tr><tr><td style='padding: 15px;'><p>11. Some Basic Conditions that OLS Models Have to Fulfill</p><p style='margin: 0'>Ordinary Least Square Regression Modelling: Some Basic Conditions that OLS Models Have to Fulfill</p></td></tr><tr><td style='padding: 15px;'><p>12. Conclusions to Section 2</p><p style='margin: 0'>Ordinary Least Square Regression Modelling: Conclusions to Section 2</p></td></tr></tbody></table><br><h4>3. Deal with Multicollinearity in OLS Regression Models</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Identify Multicollinearity</p><p style='margin: 0'>Deal with Multicollinearity in OLS Regression Models: Identify Multicollinearity</p></td></tr><tr><td style='padding: 15px;'><p>2. Doing Regression Analyses with Correlated Predictor Variables</p><p style='margin: 0'>Deal with Multicollinearity in OLS Regression Models: Doing Regression Analyses with Correlated Predictor Variables</p></td></tr><tr><td style='padding: 15px;'><p>3. Principal Component Regression in R</p><p style='margin: 0'>Deal with Multicollinearity in OLS Regression Models: Principal Component Regression in R</p></td></tr><tr><td style='padding: 15px;'><p>4. Partial Least Square Regression in R</p><p style='margin: 0'>Deal with Multicollinearity in OLS Regression Models: Partial Least Square Regression in R</p></td></tr><tr><td style='padding: 15px;'><p>5. Ridge Regression in R</p><p style='margin: 0'>Deal with Multicollinearity in OLS Regression Models: Ridge Regression in R</p></td></tr><tr><td style='padding: 15px;'><p>6. LASSO Regression</p><p style='margin: 0'>Deal with Multicollinearity in OLS Regression Models: LASSO Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Conclusion to Section 3</p><p style='margin: 0'>Deal with Multicollinearity in OLS Regression Models: Conclusion to Section 3</p></td></tr></tbody></table><br><h4>4. Variable & Model Selection</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Why Do Any Kind of Selection?</p><p style='margin: 0'>Variable &amp; Model Selection: Why Do Any Kind of Selection?</p></td></tr><tr><td style='padding: 15px;'><p>2. Select the Most Suitable OLS Regression Model</p><p style='margin: 0'>Variable &amp; Model Selection: Select the Most Suitable OLS Regression Model</p></td></tr><tr><td style='padding: 15px;'><p>3. Select Model Subsets</p><p style='margin: 0'>Variable &amp; Model Selection: Select Model Subsets</p></td></tr><tr><td style='padding: 15px;'><p>4. Machine Learning Perspective on Evaluate Regression Model Accuracy</p><p style='margin: 0'>Variable &amp; Model Selection: Machine Learning Perspective on Evaluate Regression Model Accuracy</p></td></tr><tr><td style='padding: 15px;'><p>5. Evaluate Regression Model Performance</p><p style='margin: 0'>Variable &amp; Model Selection: Evaluate Regression Model Performance</p></td></tr><tr><td style='padding: 15px;'><p>6. LASSO Regression for Variable Selection</p><p style='margin: 0'>Variable &amp; Model Selection: LASSO Regression for Variable Selection</p></td></tr><tr><td style='padding: 15px;'><p>7. Identify the Contribution of Predictors in Explaining the Variation in Y</p><p style='margin: 0'>Variable &amp; Model Selection: Identify the Contribution of Predictors in Explaining the Variation in Y</p></td></tr><tr><td style='padding: 15px;'><p>8. Conclusions to Section 4</p><p style='margin: 0'>Variable &amp; Model Selection: Conclusions to Section 4</p></td></tr></tbody></table><br><h4>5. Dealing with Other Violations of the OLS Regression Models</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Transformations</p><p style='margin: 0'>Dealing with Other Violations of the OLS Regression Models: Data Transformations</p></td></tr><tr><td style='padding: 15px;'><p>2. Robust Regression-Deal with Outliers</p><p style='margin: 0'>Dealing with Other Violations of the OLS Regression Models: Robust Regression-Deal with Outliers</p></td></tr><tr><td style='padding: 15px;'><p>3. Dealing with Heteroscedasticity</p><p style='margin: 0'>Dealing with Other Violations of the OLS Regression Models: Dealing with Heteroscedasticity</p></td></tr><tr><td style='padding: 15px;'><p>4. Conclusions to Section 5</p><p style='margin: 0'>Dealing with Other Violations of the OLS Regression Models: Conclusions to Section 5</p></td></tr></tbody></table><br><h4>6. Generalized Linear Models (GLMs)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What are GLMs?</p><p style='margin: 0'>Generalized Linear Models (GLMs): What are GLMs?</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic regression</p><p style='margin: 0'>Generalized Linear Models (GLMs): Logistic regression</p></td></tr><tr><td style='padding: 15px;'><p>3. Logistic Regression for Binary Response Variable</p><p style='margin: 0'>Generalized Linear Models (GLMs): Logistic Regression for Binary Response Variable</p></td></tr><tr><td style='padding: 15px;'><p>4. Multinomial Logistic Regression</p><p style='margin: 0'>Generalized Linear Models (GLMs): Multinomial Logistic Regression</p></td></tr><tr><td style='padding: 15px;'><p>5. Regression for Count Data</p><p style='margin: 0'>Generalized Linear Models (GLMs): Regression for Count Data</p></td></tr><tr><td style='padding: 15px;'><p>6. Goodness of fit testing</p><p style='margin: 0'>Generalized Linear Models (GLMs): Goodness of fit testing</p></td></tr><tr><td style='padding: 15px;'><p>7. Conclusions to Section 6</p><p style='margin: 0'>Generalized Linear Models (GLMs): Conclusions to Section 6</p></td></tr></tbody></table><br><h4>7. Working with Non-Parametric and Non-Linear Data</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Polynomial and Non-linear regression</p><p style='margin: 0'>Working with Non-Parametric and Non-Linear Data: Polynomial and Non-linear regression</p></td></tr><tr><td style='padding: 15px;'><p>2. Generalized Additive Models (GAMs) in R</p><p style='margin: 0'>Working with Non-Parametric and Non-Linear Data: Generalized Additive Models (GAMs) in R</p></td></tr><tr><td style='padding: 15px;'><p>3. Boosted GAM Regression</p><p style='margin: 0'>Working with Non-Parametric and Non-Linear Data: Boosted GAM Regression</p></td></tr><tr><td style='padding: 15px;'><p>4. Multivariate Adaptive Regression Splines (MARS)</p><p style='margin: 0'>Working with Non-Parametric and Non-Linear Data: Multivariate Adaptive Regression Splines (MARS)</p></td></tr><tr><td style='padding: 15px;'><p>5. CART-Regression Trees in R</p><p style='margin: 0'>Working with Non-Parametric and Non-Linear Data: CART-Regression Trees in R</p></td></tr><tr><td style='padding: 15px;'><p>6. Conditional Inference Trees</p><p style='margin: 0'>Working with Non-Parametric and Non-Linear Data: Conditional Inference Trees</p></td></tr><tr><td style='padding: 15px;'><p>7. Random Forest(RF)</p><p style='margin: 0'>Working with Non-Parametric and Non-Linear Data: Random Forest(RF)</p></td></tr><tr><td style='padding: 15px;'><p>8. Gradient Boosting Regression</p><p style='margin: 0'>Working with Non-Parametric and Non-Linear Data: Gradient Boosting Regression</p></td></tr><tr><td style='padding: 15px;'><p>9. ML Model Selection</p><p style='margin: 0'>Working with Non-Parametric and Non-Linear Data: ML Model Selection</p></td></tr><tr><td style='padding: 15px;'><p>10. Conclusions to Section 7</p><p style='margin: 0'>Working with Non-Parametric and Non-Linear Data: Conclusions to Section 7</p></td></tr></tbody></table>

Learn complete hands-on Regression analysis for practical Statistical modelling and Machine Learning in R
With so many R Statistics and Machine Learning courses around, why enroll for this? Regression analysis is one of the central aspects of both Statistics and Machine Learning based analysis. This course will teach you Regression analysis for both Statistical data analysis and ML in R. It explores relevant concepts in a practical way, from basic to expert level. This course can help you achieve better grades, gain new analysis tools for your academic career, implement your knowledge in a work setting, and make business forecasting-related decisions. You will go all the way from implementing and inferring simple OLS (Ordinary Least Square) regression models to dealing with issues of multicollinearity in regression to ML based regression models. Become a Regression analysis expert and harness the power of R for your analysis • Get started with R and RStudio. Install these on your system, learn to load packages, and read in different types of data in R
• Carry out data cleaning and data visualization using R
• Implement Ordinary Least Square (OLS) regression in R and learn how to interpret the results.
• Learn how to deal with multicollinearity both through the variable selection and regularization techniques such as ridge regression
• Carry out variable and regression model selection using both statistical and machine learning techniques, including using cross-validation methods.
• Evaluate the regression model accuracy
• Implement Generalized Linear Models (GLMs) such as logistic regression and Poisson regression. Use logistic regression as a binary classifier to distinguish between male and female voices.
• Use non-parametric techniques such as Generalized Additive Models (GAMs) to work with non-linear and non-parametric data.
• Work with tree-based ML models All the code and supporting files for this course are available at - https://github.com/PacktPublishing/Regression-Analysis-for-Statistics-and-Machine-Learning-in-R
What You Will Learn
Implement and infer Ordinary Least Square (OLS) regression using R
Apply statistical and ML based regression models to deal with problems such as multicollinearity
Carry out the variable selection and assess model accuracy using techniques such as cross-validation
Implement and infer Generalized Linear Models (GLMs), including using logistic regression as a binary classifier
Audience
This course is for students interested in getting started with data science applications in the RStudio environment. Students wishing to learn to implement statistical modeling techniques for regression analysis on real data. Anyone with prior exposure to R who wants to get started with practical data science.
Approach
This is a practical, hands-on course. We will spend some time dealing with some theoretical concepts related to both statistical and machine learning regression analysis. However, the majority of the course will focus on implementing different techniques on real data and interpreting the results. After each video, you will learn a new concept or technique which you can apply to your own projects.
Key Features
Provided practical indepth training for you to get started with R * Perform some of the most common advanced regression analysis-based techniques * Use R to perform different statistical and machine learning data analysis and visualization tasks
Github Repo
https://github.com/packtpublishing/regression-analysis-for-statistics-and-machine-learning-in-r
About the Author
Minerva Singh
Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a part-time Data Scientist. As part of her research, she must carry out extensive data analysis, including spatial data analysis. For this purpose, she prefers to use a combination of freeware tools: R, QGIS, and Python. She does most of her spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing, and analysis. She also holds an MPhil degree in Geography and Environment from Oxford University. She has honed her statistical and data analysis skills through several MOOCs, including The Analytics Edge and Statistical. In addition to spatial data analysis, she is also proficient in statistical analysis, machine learning, and data mining.
Course Outline
1. Get Started with Practical Regression Analysis in R

1. INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
Get Started with Practical Regression Analysis in R: INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
2. Difference Between Statistical Analysis & Machine Learning
Get Started with Practical Regression Analysis in R: Difference Between Statistical Analysis & Machine Learning
3. Getting Started with R and R Studio
Get Started with Practical Regression Analysis in R: Getting Started with R and R Studio
4. Reading in Data with R
Get Started with Practical Regression Analysis in R: Reading in Data with R
5. Data Cleaning with R
Get Started with Practical Regression Analysis in R: Data Cleaning with R
6. Some More Data Cleaning with R
Get Started with Practical Regression Analysis in R: Some More Data Cleaning with R
7. Basic Exploratory Data Analysis in R
Get Started with Practical Regression Analysis in R: Basic Exploratory Data Analysis in R
8. Conclusion to Section 1
Get Started with Practical Regression Analysis in R: Conclusion to Section 1
2. Ordinary Least Square Regression Modelling

1. OLS Regression- Theory
Ordinary Least Square Regression Modelling: OLS Regression- Theory
2. OLS-Implementation
Ordinary Least Square Regression Modelling: OLS-Implementation
3. More on Result Interpretations
Ordinary Least Square Regression Modelling: More on Result Interpretations
4. Confidence Interval-Theory
Ordinary Least Square Regression Modelling: Confidence Interval-Theory
5. Calculate the Confidence Interval in R
Ordinary Least Square Regression Modelling: Calculate the Confidence Interval in R
6. Confidence Interval and OLS Regressions
Ordinary Least Square Regression Modelling: Confidence Interval and OLS Regressions
7. Linear Regression without Intercept
Ordinary Least Square Regression Modelling: Linear Regression without Intercept
8. Implement ANOVA on OLS Regression
Ordinary Least Square Regression Modelling: Implement ANOVA on OLS Regression
9. Multiple Linear Regression
Ordinary Least Square Regression Modelling: Multiple Linear Regression
10. Multiple Linear regression with Interaction and Dummy Variables
Ordinary Least Square Regression Modelling: Multiple Linear regression with Interaction and Dummy Variables
11. Some Basic Conditions that OLS Models Have to Fulfill
Ordinary Least Square Regression Modelling: Some Basic Conditions that OLS Models Have to Fulfill
12. Conclusions to Section 2
Ordinary Least Square Regression Modelling: Conclusions to Section 2
3. Deal with Multicollinearity in OLS Regression Models

1. Identify Multicollinearity
Deal with Multicollinearity in OLS Regression Models: Identify Multicollinearity
2. Doing Regression Analyses with Correlated Predictor Variables
Deal with Multicollinearity in OLS Regression Models: Doing Regression Analyses with Correlated Predictor Variables
3. Principal Component Regression in R
Deal with Multicollinearity in OLS Regression Models: Principal Component Regression in R
4. Partial Least Square Regression in R
Deal with Multicollinearity in OLS Regression Models: Partial Least Square Regression in R
5. Ridge Regression in R
Deal with Multicollinearity in OLS Regression Models: Ridge Regression in R
6. LASSO Regression
Deal with Multicollinearity in OLS Regression Models: LASSO Regression
7. Conclusion to Section 3
Deal with Multicollinearity in OLS Regression Models: Conclusion to Section 3
4. Variable & Model Selection

1. Why Do Any Kind of Selection?
Variable & Model Selection: Why Do Any Kind of Selection?
2. Select the Most Suitable OLS Regression Model
Variable & Model Selection: Select the Most Suitable OLS Regression Model
3. Select Model Subsets
Variable & Model Selection: Select Model Subsets
4. Machine Learning Perspective on Evaluate Regression Model Accuracy
Variable & Model Selection: Machine Learning Perspective on Evaluate Regression Model Accuracy
5. Evaluate Regression Model Performance
Variable & Model Selection: Evaluate Regression Model Performance
6. LASSO Regression for Variable Selection
Variable & Model Selection: LASSO Regression for Variable Selection
7. Identify the Contribution of Predictors in Explaining the Variation in Y
Variable & Model Selection: Identify the Contribution of Predictors in Explaining the Variation in Y
8. Conclusions to Section 4
Variable & Model Selection: Conclusions to Section 4
5. Dealing with Other Violations of the OLS Regression Models

1. Data Transformations
Dealing with Other Violations of the OLS Regression Models: Data Transformations
2. Robust Regression-Deal with Outliers
Dealing with Other Violations of the OLS Regression Models: Robust Regression-Deal with Outliers
3. Dealing with Heteroscedasticity
Dealing with Other Violations of the OLS Regression Models: Dealing with Heteroscedasticity
4. Conclusions to Section 5
Dealing with Other Violations of the OLS Regression Models: Conclusions to Section 5
6. Generalized Linear Models (GLMs)

1. What are GLMs?
Generalized Linear Models (GLMs): What are GLMs?
2. Logistic regression
Generalized Linear Models (GLMs): Logistic regression
3. Logistic Regression for Binary Response Variable
Generalized Linear Models (GLMs): Logistic Regression for Binary Response Variable
4. Multinomial Logistic Regression
Generalized Linear Models (GLMs): Multinomial Logistic Regression
5. Regression for Count Data
Generalized Linear Models (GLMs): Regression for Count Data
6. Goodness of fit testing
Generalized Linear Models (GLMs): Goodness of fit testing
7. Conclusions to Section 6
Generalized Linear Models (GLMs): Conclusions to Section 6
7. Working with Non-Parametric and Non-Linear Data

1. Polynomial and Non-linear regression
Working with Non-Parametric and Non-Linear Data: Polynomial and Non-linear regression
2. Generalized Additive Models (GAMs) in R
Working with Non-Parametric and Non-Linear Data: Generalized Additive Models (GAMs) in R
3. Boosted GAM Regression
Working with Non-Parametric and Non-Linear Data: Boosted GAM Regression
4. Multivariate Adaptive Regression Splines (MARS)
Working with Non-Parametric and Non-Linear Data: Multivariate Adaptive Regression Splines (MARS)
5. CART-Regression Trees in R
Working with Non-Parametric and Non-Linear Data: CART-Regression Trees in R
6. Conditional Inference Trees
Working with Non-Parametric and Non-Linear Data: Conditional Inference Trees
7. Random Forest(RF)
Working with Non-Parametric and Non-Linear Data: Random Forest(RF)
8. Gradient Boosting Regression
Working with Non-Parametric and Non-Linear Data: Gradient Boosting Regression
9. ML Model Selection
Working with Non-Parametric and Non-Linear Data: ML Model Selection
10. Conclusions to Section 7
Working with Non-Parametric and Non-Linear Data: Conclusions to Section 7

Learn complete hands-on Regression analysis for practical Statistical modelling and Machine Learning in R

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