Name: R Programming for Data Science
Price: 12 GBP
Availability: InStock

<strong>This course equips learners with a comprehensive understanding of the NumPy stack, including NumPy, Matplotlib, Pandas, and SciPy, to effectively tackle common challenges in deep learning and data science. Master the basics with this carefully structured course.</strong>
        <p>Welcome to the course where you will learn about the NumPy stack in Python, which is an important prerequisite for deep learning, machine learning, and data science.

In this self-paced course, you will learn how to use NumPy, Matplotlib, Pandas, and SciPy to perform critical tasks related to data science and machine learning. This involves performing numerical computation and representing data, visualizing data with plots, loading in, and manipulating data using DataFrames, performing statistics and probability, and building machine learning models for classification and regression.

In this course, we will first start with NumPy; we will understand the benefits of NumPy array and then we will look at some complicated matrix operations, such as products, inverses, determinants, and solving linear systems.

Then we will cover Matplotlib. In this section, we will go over some common plots, namely the line chart, scatter plot, and histogram. We will also look at how to show images using Matplotlib.

Next, we will talk about Pandas. We will look at how much easier it is to load a dataset using Pandas versus trying to do it manually. Then we will look at some data frame operations useful in machine learning, such as filtering by column, filtering by row, and the apply function.

Later, you will learn about SciPy. In this section, you will learn how to do common statistics calculations, including getting the PDF value, the CDF value, sampling from a distribution, and statistical testing.

Finally, we will also cover some basics of machine learning that will help us start our deep learning journey.

By the end of the course, we will be able to confidently use the NumPy stack in deep learning and data science.</p>
        <h2>What You Will Learn</h2><p>Understand supervised machine learning with real-world examples<br>Understand and code using the NumPy stack<br>Make use of NumPy, SciPy, Matplotlib, and Pandas to implement numerical algorithms<br>Understand the pros and cons of various machine learning models<br>Get a brief introduction to the classification and regression<br>Learn how to calculate the PDF and CDF under the normal distribution</p>
        <h2>Audience</h2><p>This course is designed for anyone who is interested in data science and machine learning, who knows Python and wants to take the next step into Python libraries for data science, or who is interested in acquiring tools to implement machine learning algorithms.<br><br>One must have decent Python programming skills and a basic understanding of linear algebra and probability for this course.</p>
        <h2>Approach</h2><p>You will learn how to use NumPy, Matplotlib, Pandas, and SciPy to carry out crucial data science and machine learning tasks in this self-paced course. The course is well-balanced with both theoretical and practical coding exercises. Each section, we first cover the theory concept and demonstrate it using a real-world example for better understanding.</p>
        <h2>Key Features</h2><p>Study basics of machine learning and understand how to use the NumPy stack for deep learning in data science * Learn how to use NumPy, Matplotlib, Pandas, and SciPy for critical tasks in data science and machine learning * Perform numerical computations, visualize data, load, and manipulate datasets using Pandas</p>
        
        <h2>About the Author</h2><h3>Lazy Programmer</h3><p>The Lazy Programmer is an AI/ML engineer focusing on deep learning with experience in data science, big data engineering, and full-stack development. With a background in computer engineering and specialization in ML, he holds two master's degrees in computer engineering and statistics with finance applications. His online advertising and digital media expertise include data science and big data.

He has created DL models for prediction and has experience in recommender systems using reinforcement learning and collaborative filtering. He is a skilled instructor who has taught at universities including Columbia, NYU, Hunter College, and The New School. He is a web programmer with experience in Python, Ruby/Rails, PHP, and Angular.</p>
        <h2>Course Outline</h2><h4>1. Welcome and Logistics</h4><p>Welcome to the course! In this section, we will get introduced to the course goals.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction and Outline</p><p style='margin: 0'>In this video, we will get introduced to the course and understand the learning objective.</p></td></tr></tbody></table><br><h4>2. NumPy</h4><p>In this section of the course, we will dive into the world of NumPy, a powerful numerical computing library in Python that is widely used in data science and machine learning.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. NumPy Section Introduction</p><p style='margin: 0'>In this video, we will have a quick overview of NumPy.</p></td></tr><tr><td style='padding: 15px;'><p>2. Arrays Versus Lists</p><p style='margin: 0'>In this video, we will be comparing NumPy array to Python list.</p></td></tr><tr><td style='padding: 15px;'><p>3. Dot Product</p><p style='margin: 0'>In this video, we will discuss Dot Product in NumPy.</p></td></tr><tr><td style='padding: 15px;'><p>4. Speed Test</p><p style='margin: 0'>In this video, we will do a speed test to measure how much faster NumPy is than regular Python list in terms of the dot product.</p></td></tr><tr><td style='padding: 15px;'><p>5. Matrices</p><p style='margin: 0'>In this video, you will learn about matrices.</p></td></tr><tr><td style='padding: 15px;'><p>6. Solving Linear Systems</p><p style='margin: 0'>In this video, we will work on solving an example problem to understand linear systems.</p></td></tr><tr><td style='padding: 15px;'><p>7. Generating Data</p><p style='margin: 0'>In this video, we will discuss about generatingabout how to generate data.</p></td></tr><tr><td style='padding: 15px;'><p>8. NumPy Exercise</p><p style='margin: 0'>In this video, we will look at the NumPy exercise that must be resolved on our own.</p></td></tr><tr><td style='padding: 15px;'><p>9. Where to Learn More NumPy</p><p style='margin: 0'>In this video, we will understand why we need NumPy and how it is applied in more advanced scenarios.</p></td></tr></tbody></table><br><h4>3. Matplotlib</h4><p>In this section, we will talk about Matplotlib.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Matplotlib Section Introduction</p><p style='margin: 0'>In this video, we will have a quick overview of Matplotlib that is used to visualize data and see what we are going to cover in this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Line Chart</p><p style='margin: 0'>In this video, you will learn about a line chart.</p></td></tr><tr><td style='padding: 15px;'><p>3. Scatterplot</p><p style='margin: 0'>In this video, you will learn about a scatterplot.</p></td></tr><tr><td style='padding: 15px;'><p>4. Histogram</p><p style='margin: 0'>In this video, you will learn about a histogram.</p></td></tr><tr><td style='padding: 15px;'><p>5. Plotting Images</p><p style='margin: 0'>In this video, you will learn how to plot images.</p></td></tr><tr><td style='padding: 15px;'><p>6. Matplotlib Exercise</p><p style='margin: 0'>In this video, we will have a look at the Matplotlib exercise that needs to be resolved on our own.</p></td></tr><tr><td style='padding: 15px;'><p>7. Where to Learn More Matplotlib</p><p style='margin: 0'>In this video, we will understand from where we can learn more about Matplotlib.</p></td></tr></tbody></table><br><h4>4. Pandas</h4><p>In this section, we will talk about Pandas.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Pandas Section Introduction</p><p style='margin: 0'>In this video, we will be introduced to Pandas and understand this section''s learning objective.</p></td></tr><tr><td style='padding: 15px;'><p>2. Loading in Data</p><p style='margin: 0'>In this video, you will learn how to load in data using Pandas.</p></td></tr><tr><td style='padding: 15px;'><p>3. Selecting Rows and Columns</p><p style='margin: 0'>In this video, you will learn how to select rows and columns from a data frame.</p></td></tr><tr><td style='padding: 15px;'><p>4. The apply() Function</p><p style='margin: 0'>In this video, you will learn about the apply() function.</p></td></tr><tr><td style='padding: 15px;'><p>5. Plotting with Pandas</p><p style='margin: 0'>In this video, you will learn how to plot with Pandas.</p></td></tr><tr><td style='padding: 15px;'><p>6. Pandas Exercise</p><p style='margin: 0'>In this video, we will have a look at the Pandas exercise that needs to be resolved on our own.</p></td></tr><tr><td style='padding: 15px;'><p>7. Where to Learn More Pandas</p><p style='margin: 0'>In this video, we will understand from where we can learn more about Pandas.</p></td></tr></tbody></table><br><h4>5. SciPy</h4><p>In this section, we will talk about SciPy.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. SciPy Section Introduction</p><p style='margin: 0'>In this video, we will be introduced to SciPy and understand this section''s learning objective.</p></td></tr><tr><td style='padding: 15px;'><p>2. PDF and CDF</p><p style='margin: 0'>In this video, you will learn how to calculate PDF and CDF under normal distribution.</p></td></tr><tr><td style='padding: 15px;'><p>3. Convolution</p><p style='margin: 0'>In this video, you will learn how to apply convolution to an image to create a blurring filter.</p></td></tr><tr><td style='padding: 15px;'><p>4. SciPy Exercise</p><p style='margin: 0'>In this video, we will have a look at the SciPy exercise that needs to be resolved on our own.</p></td></tr><tr><td style='padding: 15px;'><p>5. Where to Learn More SciPy</p><p style='margin: 0'>In this video, we will summarize what we have learnt about SciPy and understand where it can be applied.</p></td></tr></tbody></table><br><h4>6. Machine Learning Basics</h4><p>In this section, we will cover machine learning basics.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Machine Learning: Section Introduction</p><p style='margin: 0'>In this video, we will understand what machine learning is and the learning objective of this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. What Is Classification?</p><p style='margin: 0'>In this video, we will understand classification.</p></td></tr><tr><td style='padding: 15px;'><p>3. Classification in Code</p><p style='margin: 0'>In this video, we will practice all that we have learnt in the previous video about classification.</p></td></tr><tr><td style='padding: 15px;'><p>4. What Is Regression?</p><p style='margin: 0'>In this video, we will understand what regression is.</p></td></tr><tr><td style='padding: 15px;'><p>5. Regression in Code</p><p style='margin: 0'>In this video, we will practice all that we have learnt in the previous video about regression.</p></td></tr><tr><td style='padding: 15px;'><p>6. What Is a Feature Vector?</p><p style='margin: 0'>In this video, you will learn about a feature vector.</p></td></tr><tr><td style='padding: 15px;'><p>7. Machine Learning Is Nothing but Geometry.</p><p style='margin: 0'>In this video, we will understand that machine learning is nothing but a geometry problem and see how it works for classification and regression.</p></td></tr><tr><td style='padding: 15px;'><p>8. All Data Is the Same</p><p style='margin: 0'>In this video, we will understand how all data is the same as all machine learning interfaces are the same.</p></td></tr><tr><td style='padding: 15px;'><p>9. Comparing Different Machine Learning Models</p><p style='margin: 0'>In this video, we will understand how all machine learning interfaces are the same.</p></td></tr><tr><td style='padding: 15px;'><p>10. Machine Learning and Deep Learning: Future Topics</p><p style='margin: 0'>In this video, you will learn what other types of machine learning exist and some future topics that we can look forward to learning.</p></td></tr><tr><td style='padding: 15px;'><p>11. Machine Learning: Section Summary</p><p style='margin: 0'>In this video, we will summarize our learning from this section.</p></td></tr></tbody></table>

Packt

Data Science

Python

Pandas

Deep Learning

This course equips learners with a comprehensive understanding of the NumPy stack, including NumPy, Matplotlib, Pandas, and SciPy, to effectively tackle common challenges in deep learning and data science. Master the basics with this carefully structured course.

Data Science Prerequisites - NumPy, Matplotlib, and Pandas in Python

General Admission

<div>
	<h3><b>Course Overview</b></h3>
<p><span>Peek into the world of data science and machine learning with the comprehensive <strong>Data Science &amp; Machine Learning With R in 2021</strong> course. This course will provide you with a detailed understanding of both machine learning and data science. In addition, you will acquire essential skills to pursue a career in this growing industry.</span></p>
<p><span>The <strong>Data Science &amp; Machine Learning With R in 2021</strong> course will teach you the core concept of data science. You will be able to recognize different data types and structures. From the modules, you will receive an introduction to the intermediate R Section. The course will show you the techniques of data manipulation in R. You will know the process of data visualization with R and learn to create reports with R markdown. The Data Science &amp; Machine Learning With R in 2021 course will provide you with an insight into the fundamentals of machine learning. You will understand the principles of data processing, linear regression, logistic regression and more. </span></p>
<p><span>This highly informative <strong>Data Science &amp; Machine Learning With R in 2021</strong> course will equip you with the essential skills of data science. If you desire to become a professional data scientist, this course can be your stepping stone. So, enroll in the course and fast track your career.</span></p>
<h3><b>Learning Outcomes</b></h3>
<ul>
<li><span>Learn the definition of data science </span></li>
<li><span>Understand the basics of machine learning</span></li>
<li><span>Enrich your knowledge of data types and structures</span></li>
<li><span>Know the process of data manipulation in R</span></li>
<li><span>Gain the ability to create reports with R markdown</span></li>
<li><span>Become skilled in building web apps with R shiny</span></li>
</ul>
<h3><b>Who is this course for?</b></h3>
<p><span>Aspiring data scientists or individuals interested in learning data science and machine learning </span></p>
<h3><b>Entry Requirement</b></h3>
<ul>
<li><span>This course is available to all learners, of all academic backgrounds.</span></li>
<li><span>Learners should be aged 16 or over to undertake the qualification.</span></li>
<li><span>Good understanding of English language, numeracy and ICT are required to attend this course.</span></li>
</ul>
<h3><b>Certification</b></h3>
<ul>
<li><span>After you have successfully completed the course, you will be able to obtain an Accredited Certificate of Achievement. You can however also obtain a Course Completion Certificate following the course completion without sitting for the test. Certificates can be obtained either in hardcopy at the cost of £39 or in PDF format at the cost of £24.</span></li>
<li><span>PDF certificate's turnaround time is 24 hours, and for the hardcopy certificate, it is 3-9 working days.</span></li>
</ul>
<h3><b>Why choose us?</b></h3>
<ul>
<li><span>Affordable, engaging &amp; high-quality e-learning study materials;</span></li>
<li><span>Tutorial videos/materials from the industry-leading experts;</span></li>
<li><span>Study in a user-friendly, advanced online learning platform;</span></li>
<li><span>Efficient exam systems for the assessment and instant result;</span></li>
<li><span>The UK &amp; internationally recognized accredited qualification;</span></li>
<li><span>Access to course content on mobile, tablet or desktop from anywhere anytime;</span></li>
<li><span>The benefit of career advancement opportunities;</span></li>
<li><span>24/7 student support via email.</span></li>
</ul>
<h3><b>Career Path</b></h3>
<p><b> </b><span>The <strong>Data Science &amp; Machine Learning With R in 2021</strong></span><b>  </b><span>course is a useful qualification to possess and would be beneficial for any related profession or industry such as:</span></p>
<ul>
<li><span>Data Scientist </span></li>
</ul>
		</div><table width="100%">				<tbody><tr>
					<td colspan="4">Data Science and Machine Learning Course Intro</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Science and Machine Learning Introduction</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>What is Data Science</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Machine Learning Overview</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Who is This Course for</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Science and Machine Learning Marketplace</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Science and Machine Learning Job Opportunities</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Data Types and Structures in R</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Getting Started</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Basics</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Files</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>RStudio</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Tidyverse</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Resources</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Data Types and Structures in R</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Unit Introduction</td>
					<td> </td>
					<td><span><i></i> 00:30:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Basic Type</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vector Part One</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors Part Two</td>
					<td> </td>
					<td><span><i></i> 00:25:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors - Missing Values</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors - Coercion</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors - Naming</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors - Misc</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Creating Matrics</td>
					<td> </td>
					<td><span><i></i> 00:31:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>List</td>
					<td> </td>
					<td><span><i></i> 00:32:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to Data Frames</td>
					<td> </td>
					<td><span><i></i> 00:19:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Creating Data Frames</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Frames: Helper Functions</td>
					<td> </td>
					<td><span><i></i> 00:31:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Frames Tibbles</td>
					<td> </td>
					<td><span><i></i> 00:39:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Intermediate R</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Intermediate Introduction</td>
					<td> </td>
					<td><span><i></i> 00:47:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Relational Operations</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Conditional Statements</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Loops</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Functions</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Packages</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Factors</td>
					<td> </td>
					<td><span><i></i> 00:28:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Dates and Times</td>
					<td> </td>
					<td><span><i></i> 00:30:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Functional Programming</td>
					<td> </td>
					<td><span><i></i> 00:37:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Import or Export</td>
					<td> </td>
					<td><span><i></i> 00:22:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Database1</td>
					<td> </td>
					<td><span><i></i> 00:27:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Data Manipulation in R</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Manipulation in R Introduction</td>
					<td> </td>
					<td><span><i></i> 00:36:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Tidy Data</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Pipe Operator</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Filter Verb</td>
					<td> </td>
					<td><span><i></i> 00:22:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Select Verb</td>
					<td> </td>
					<td><span><i></i> 00:46:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Mutate Verb</td>
					<td> </td>
					<td><span><i></i> 00:32:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Arrange Verb</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Summarize Verb</td>
					<td> </td>
					<td><span><i></i> 00:23:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Pivoting</td>
					<td> </td>
					<td><span><i></i> 00:43:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>JSON Parsing</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>String Manipulation</td>
					<td> </td>
					<td><span><i></i> 00:33:00</span></td>
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					<td>Web Scraping</td>
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					<td><span><i></i> 00:59:00</span></td>
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					<td colspan="4">Data Visualization in R</td>
				</tr>
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					<td><i></i></td>
					<td>Data Visualization in R Section Intro</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Getting Started</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
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								<tr>
					<td><i></i></td>
					<td>Aesthetics Mappings</td>
					<td> </td>
					<td><span><i></i> 00:25:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Single Variable Plots</td>
					<td> </td>
					<td><span><i></i> 00:37:00</span></td>
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								<tr>
					<td><i></i></td>
					<td>Two Variable Plots</td>
					<td> </td>
					<td><span><i></i> 00:21:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Facets, Layering, and Coordinate Systems</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Styling and Saving</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
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					<td colspan="4">Creating Reports with R Markdown</td>
				</tr>
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					<td><i></i></td>
					<td>Creating with R Markdown</td>
					<td> </td>
					<td><span><i></i> 00:29:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Building Webapps with R Shiny</td>
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					<td><i></i></td>
					<td>Introduction to R Shiny</td>
					<td> </td>
					<td><span><i></i> 00:26:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>A Basic R Shiny App</td>
					<td> </td>
					<td><span><i></i> 00:31:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Other Examples with R Shiny</td>
					<td> </td>
					<td><span><i></i> 00:34:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Introduction to Machine Learning</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Machine Learning Part 1</td>
					<td> </td>
					<td><span><i></i> 00:22:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Machine Learning Part 2</td>
					<td> </td>
					<td><span><i></i> 00:47:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Starting A Career in Data Science</td>
				</tr>
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					<td><i></i></td>
					<td>Starting a Data Science Career Section Overview</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Science Resume</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Getting Started with Freelancing</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Top Freelance Websites</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Personal Branding</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Importance of Website and Blo</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Networking Do's and Don'ts</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Certificate and Transcript</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Order Your Certificates and Transcripts</td>
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
				</tr>
				</tbody></table>

John Academy

Data Engineering

Data Skills

Data Science & Machine Learning With R in 2021

<p>Learn the logical concepts of Data Science &amp; Machine Learning with R, a comprehensive course that covers key topics such as data manipulation, visualisation, report creation, web app development, and introduction to machine learning. Gain the necessary skills to start a successful career in the field of Data Science and Machine Learning.</p><p><strong>Key Takeaways:</strong></p><ul><li>Understand the fundamentals of Data Science and Machine Learning using R.</li><li>Analyse and manipulate data using various data types and structures in R.</li><li>Create visually appealing data visualisations to extract insights from complex datasets.</li><li>Develop interactive web applications using R Shiny for data presentation and analysis.</li><li>Gain proficiency in creating reports using R Markdown for efficient data communication.</li><li>Explore the basics of Machine Learning and its applications in solving real-world problems.</li><li>Acquire the knowledge and confidence to pursue a rewarding career in Data Science.</li></ul><p><strong>Our Service for the </strong><strong>Data Science &amp; Machine Learning with R</strong><strong> Includes: </strong></p><ul><li>Intensive Study Notes</li><li>Tutor Support</li><li>Customer Support</li><li>Assignment Help</li><li>QLS Endorsed Certificate</li><li>24/7 Learning Portal Access</li><li>Widely Compatible Study Materials</li></ul><p><strong>Course Curriculum of </strong><strong>Data Science &amp; Machine Learning with R</strong></p><ul><li>Data Science and Machine Learning Course Intro</li><li>Getting Started with R</li><li>Data Types and Structures in R</li><li>Intermediate R</li><li>Data Manipulation in R</li><li>Data Visualization in R</li><li>Creating Reports with R Markdown</li><li>Building Webapps with R Shiny</li><li>Introduction to Machine Learning</li><li>Starting A Career in Data Science</li></ul><p><strong>Assessment &amp; Certification</strong></p><p>Learners must submit a comprehensive summerised assignment on all the units that will be assessed by our expert tutor if all the learning outcomes are met as per the standard set by QLS. After the quality check, learner will be provided with the Data Science &amp; Machine Learning with R certificate.</p><p><strong>Endorsed Certificate of Achievement from the Quality Licence Scheme</strong></p><p>An endorsed certificate will be issued to learners at the end of the course as recognition of course completion. Provided that learner completes all the assessment of a course can claim for certificate.</p><p><strong>Endorsement:</strong></p><p>QLS endorsed this certified course as a highly qualified, non-regulated provision and training programme. There will be a trainer to answer all your questions including your progression routes into further higher education.</p><h2>
        
        <div>CPD</div>
    </h2>

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                                        20 CPD hours / points
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                                            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 entering the field of Data Science and Machine Learning.</li><li>Professionals looking to enhance their data manipulation and analysis skills using R.</li><li>Graduates seeking to gain a competitive edge in the job market with specialized knowledge in Data Science.</li><li>Those who want to explore the applications of Machine Learning and its impact on various industries.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>There are no formal requirements needed for this Data Science &amp; Machine Learning with R course. So learners do not require any prior qualifications to enrol in this course.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Analyst: £25,000 - £45,000 per annum</li><li>Machine Learning Engineer: £35,000 - £65,000 per annum</li><li>Data Scientist: £40,000 - £70,000 per annum</li><li>Business Intelligence Analyst: £30,000 - £55,000 per annum</li><li>Data Engineer: £35,000 - £60,000 per annum</li><li>AI Research Scientist: £50,000 - £90,000 per annum</li></ul>
                        </div>

Kingston Open College

Learning & Development

Learning Support Assistant (LSA)

Level 7 QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support | All-Inclusive Cost

Level 7 Diploma in Data Science & Machine Learning with R - QLS Endorsed

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			<h2><b>Course Overview</b></h2>		</div>
				</div>
				<div>
				<div>
					<div><p><span>Discover how to become a data scientist, prove hypotheses, and build complex algorithms with this advanced course on </span><b>Statistics &amp; Probability for Data Science &amp; Machine Learning. </b><span>This intuitive training will empower you to manipulate records and understand how to break down the most complex processes in this fascinating field.          </span></p><p><span>This comprehensive </span><b>Data Science </b><span>tutorial delivers the ideal way to learn the methodology and principles needed to excel in this sector. You will be given expert tuition in using all the relevant concepts for analysing information, gain a genuine understanding of these concepts, and attain the skills to excel in appropriate IT commercial industries. Complete this training, and you will have a unique advantage to work in such areas as automobile design, banking service, media forecasting, and much more. </span></p></div>
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					<div><p>This best selling <strong>Statistics &amp; Probability for Data Science &amp; Machine Learning </strong>has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth <strong>Statistics &amp; Probability for Data Science &amp; Machine Learning </strong>is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p>
<p>The <strong>Statistics &amp; Probability for Data Science &amp; Machine Learning </strong>is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The <strong>Statistics &amp; Probability for Data Science &amp; Machine Learning </strong>is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p>
<p>After successfully completing the<strong> Statistics &amp; Probability for Data Science &amp; Machine Learning</strong>, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the <strong>Statistics &amp; Probability for Data Science &amp; Machine Learning </strong>will help you stand out from the crowd. You can also validate your certification on our website.</p>
<p>We know that you are busy and that time is precious, so we have designed the <strong>Statistics &amp; Probability for Data Science &amp; Machine Learning </strong>to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p>
<p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Course Cloud

Statistics & Probability for Data Science & Machine Learning

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			<h2>Overview
</h2>		</div>
				</div>
				<div>
				<div>
								<div>
					<p>Mastering data science skills and expertise can open new doors of opportunities for you in a wide range of fields. Learn the fundamentals and develop a solid grasp of Python data science with the comprehensive Data Science with Python course. This course is designed to assist you in securing a valuable skill set and boosting your career.</p><p>This course will provide you with quality training on the fundamentals of data analysis with Python. From the step-by-step learning process, you will learn the techniques of setting up the system. Then the course will teach you Python data structure and functions. You will receive detailed lessons on NumPy, Matplotlib, and Pandas. Furthermore, you will develop the skills for Algorithm Evaluation Techniques, visualising datasets and much more.</p><p>After completing the course you will receive a certificate of achievement. This certificate will help you create an impressive resume. So join today!</p>					</div>
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			<h2>How will I get my certificate?</h2>		</div>
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					<p><span>You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.</span></p>					</div>
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			<h2>Who is This course for?</h2>		</div>
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					<p>This course Data Science with Python course is ideal for beginners in data science. It will help them develop a solid grasp of Python and help them pursue their dream career in the field of data science.</p>					</div>
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			<h2>Requirements</h2>		</div>
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					<p>The students will not require any formal qualifications or previous experience to enrol in this course. Anyone can learn from the course anytime from anywhere through smart devices like laptops, tabs, PC, and smartphones with stable internet connections. They can complete the course according to their preferable pace so, there is no need to rush.</p>					</div>
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			<h2>Career Path</h2>		</div>
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					<p>This course will equip you with valuable knowledge and effective skills in this area. After completing the course, you will be able to explore career opportunities in the fields such as</p><ul><li>Data Analyst </li><li>Data Scientist </li><li>Data Manager </li><li>Business Analyst</li></ul><p>And much more!</p>					</div>
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					</div> <h3>Course Curriculum</h3><div><p>90 sections • 90 lectures • 10:19:00 total length</p></div><div><div><div><div><div><div><div><p>•Course Overview &amp; Table of Contents: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Introduction to Machine Learning - Part 2 - Classifications and Applications: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•System and Environment preparation - Part 1: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•System and Environment preparation - Part 2: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of python - Assignment 1: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of python - Assignment 2: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of python - Functions: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of python - Data Structures: 00:12:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of NumPy - NumPy Array: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of NumPy - NumPy Data: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of NumPy - NumPy Arithmetic: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of Matplotlib: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of Pandas - Part 1: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of Pandas - Part 2: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Understanding the CSV data file: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Load and Read CSV data file using Python Standard Library: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Load and Read CSV data file using NumPy: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Load and Read CSV data file using Pandas: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Dataset Summary - Peek, Dimensions and Data Types: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Dataset Summary - Class Distribution and Data Summary: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Dataset Summary - Explaining Correlation: 00:11:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Dataset Summary - Explaining Skewness - Gaussian and Normal Curve: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Dataset Visualization - Using Histograms: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Dataset Visualization - Using Density Plots: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Dataset Visualization - Box and Whisker Plots: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Multivariate Dataset Visualization - Correlation Plots: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Multivariate Dataset Visualization - Scatter Plots: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Preparation (Pre-Processing) - Introduction: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Preparation - Re-scaling Data - Part 1: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Preparation - Re-scaling Data - Part 2: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Preparation - Standardizing Data - Part 1: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Preparation - Standardizing Data - Part 2: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Preparation - Normalizing Data: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Preparation - Binarizing Data: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Feature Selection - Introduction: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Feature Selection - Uni-variate Part 1 - Chi-Squared Test: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Feature Selection - Uni-variate Part 2 - Chi-Squared Test: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Feature Selection - Recursive Feature Elimination: 00:11:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Feature Selection - Principal Component Analysis (PCA): 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Feature Selection - Feature Importance: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Refresher Session - The Mechanism of Re-sampling, Training and Testing: 00:12:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Techniques - Introduction: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Techniques - Train and Test Set: 00:11:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Techniques - K-Fold Cross Validation: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Techniques - Leave One Out Cross Validation: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Techniques - Repeated Random Test-Train Splits: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Introduction: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Classification Accuracy: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Log Loss: 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Area Under ROC Curve: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Confusion Matrix: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Classification Report: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Mean Absolute Error: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Mean Square Error: 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - R Squared: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Classification Algorithm Spot Check - Logistic Regression: 00:12:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Classification Algorithm Spot Check - Linear Discriminant Analysis: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Classification Algorithm Spot Check - K-Nearest Neighbors: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Classification Algorithm Spot Check - Naive Bayes: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Classification Algorithm Spot Check - CART: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Classification Algorithm Spot Check - Support Vector Machines: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Regression Algorithm Spot Check - Linear Regression: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Regression Algorithm Spot Check - Ridge Regression: 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Regression Algorithm Spot Check - Lasso Linear Regression: 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Regression Algorithm Spot Check - Elastic Net Regression: 00:02:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Regression Algorithm Spot Check - K-Nearest Neighbors: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Regression Algorithm Spot Check - CART: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Regression Algorithm Spot Check - Support Vector Machines (SVM): 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Compare Algorithms - Part 1 : Choosing the best Machine Learning Model: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Compare Algorithms - Part 2 : Choosing the best Machine Learning Model: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Pipelines : Data Preparation and Data Modelling: 00:11:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Pipelines : Feature Selection and Data Modelling: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Performance Improvement: Ensembles - Voting: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Performance Improvement: Ensembles - Bagging: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Performance Improvement: Ensembles - Boosting: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Performance Improvement: Parameter Tuning using Grid Search: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Performance Improvement: Parameter Tuning using Random Search: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Export, Save and Load Machine Learning Models : Pickle: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Export, Save and Load Machine Learning Models : Joblib: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Finalizing a Model - Introduction and Steps: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Finalizing a Classification Model - The Pima Indian Diabetes Dataset: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Quick Session: Imbalanced Data Set - Issue Overview and Steps: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Iris Dataset : Finalizing Multi-Class Dataset: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Finalizing a Regression Model - The Boston Housing Price Dataset: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Real-time Predictions: Using the Pima Indian Diabetes Classification Model: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset: 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Real-time Predictions: Using the Boston Housing Regression Model: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Resources - Data Science &amp; Machine Learning with Python: 00:00:00</p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>

Apex Learning

Computing Skills

Big Data Analytics

Data Science with Python

<h2 style="color: var(--text-heading); box-sizing: border-box;" id="isPasted" fr-original-style="color: var(--text-heading);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">This course at a glance</span></strong></h2><ul style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Accredited by CPDQE</li><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Learn how to become a data scientist, data engineer, data analyst or consultant</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Know the different processes of Data Science, such as data cleaning, processing, wrangling, manipulation</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Discover the usage of R to clean, analyse, and visualise data</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">How to write complex R programs for real-life industry situations</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Get data scientist resume tips and land your first job</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Explore the various applications of Machine Learning</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Understand Tidyverse, Operators, Vectors, Lists and their applications</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Uncover data extraction and scraping</span></li></ul><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(38, 99, 82) !important;">Data science and Machine Learning are one of the most popular fields in the world today. Data science is crucial to analysing and developing data-based machine learning models. We will cover programming in R in this course for data science and machine learning.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">R programming is a specialised skill that is in high demand today. We&rsquo;ve designed our&nbsp;<b fr-original-style="" style="box-sizing: border-box;">Data Science and Machine Learning with R</b>&nbsp;course to help you learn fundamentals through advanced R programming. This course teaches you how to effectively use R programming for machine learning or data science. You&rsquo;ll learn how to visualise data by plotting in R, as well as how to clean data.&nbsp;</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">You will receive training in machine learning and its many real-world applications while working with R Shiny to create web applications and online interactive dashboards. In other words, you will gain practical, step-by-step knowledge of the R programming language that you may use for business or professional purposes.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">An expert instructor designed this&nbsp;<b fr-original-style="" style="box-sizing: border-box;">&ldquo;Data Science and Machine Learning with R&rdquo;</b>&nbsp;course to provide you with the best learning experience possible. Our goal is for you to learn and apply the lessons from this course to improve your skills.&nbsp;</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">To explore the world of R Programming Language, Enrol Now!</p><h3 style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Accredited by</span></h3><p style="color: rgb(15, 21, 26); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);">This course is <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CPDQE&nbsp;</strong>accredited, which serves as an impactful mechanism for skill enhancement.&nbsp;</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">Continuing Professional Development (CPD) stands as a crucial, widely acknowledged method that aids individuals, organisations, and entire industries in staying current with their skills and knowledge.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">CPD not only facilitates the elevation of global standards and benchmarks, aligning with the growing forces of globalisation and consumer expectations but also transforms learning into a deliberate and proactive process. It motivates professionals to uphold a consistently high level of performance and demonstrates their unwavering commitment to a specific job role or profession.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Course Curriculum</span></strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Data Science +ML with R from A-Z</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Getting Started with R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Types and Structures in R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Intermediate R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Manipulation in R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Visualization in R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Creating Reports with R Markdown</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Building Webapps with R Shiny</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Machine Learning</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Preprocessing</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Linear Regression: A Simple Model</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Exploratory Data Analysis</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Linear Regression: A Real Model</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Logistic Regression</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Starting a Career in Data Science</strong></li></ul>

LearnDrive UK

Machine Learning (ML)

The course covers data science and machine learning with R.



Data Science and Machine Learning with R

<div><h2> About Course</h2><div><p><strong>Data Science and Data Analytics with Python: A Comprehensive Course for Beginners</strong></p><p><strong>Unlock the power of data and gain insights that drive informed decisions with this comprehensive course on data science and data analytics with Python.</strong></p><p><span>This course is designed for beginners of all skill levels,</span><span> with no prior programming experience required.</span><span> You will learn the essential skills to embark on your data-driven journey,</span><span> including:</span></p><ul><li><span>Data manipulation with NumPy and Pandas</span></li><li><span>Data visualization with Matplotlib and Seaborn</span></li><li><span>Statistical analysis with Python</span></li><li><span>Machine learning and artificial intelligence</span></li></ul><p><span>You will also gain hands-on experience with real-world data projects,</span><span> allowing you to apply your newfound knowledge to solve real-world problems.</span></p><p><strong>By the end of this course, you will be able to:</strong></p><ul><li><span>Understand the fundamentals of data science and data analytics</span></li><li><span>Apply Python to manipulate,</span><span> visualize,</span><span> and analyze data</span></li><li><span>Use Python to build machine learning and artificial intelligence models</span></li><li><span>Solve real-world data problems</span></li></ul><p><span>This course is the perfect launchpad for your data science journey.</span><span> Whether you are looking to pivot your career,</span><span> enhance your skill set,</span><span> or simply quench your curiosity,</span><span> this course will give you the foundation you need to succeed.</span></p>  <p><strong>Enroll today and start exploring the fascinating world of data science together!</strong></p></div></div> <div><h3> What Will You Learn?</h3><ul><li> <span></span> <span>Understand the fundamentals of data science and data analytics</span></li><li> <span></span> <span>Apply Python to manipulate, visualize, and analyze data</span></li><li> <span></span> <span>Use Python to build machine learning and artificial intelligence models</span></li><li> <span></span> <span>Solve real-world data problems</span></li></ul></div><div><h3> Course Content</h3><div><div><h4> Introduction to Python Data Science</h4><div><div><ul><li><div> <span></span><h5> Introduction to Python Data Science</h5></div><div> </div></li><li><div> <span></span><h5> Environment Setup</h5></div><div> </div></li></ul></div></div></div><div><h4> Data Cleaning Packages</h4><div><div><ul><li><div> <span></span><h5> Working with the Numpy package</h5></div><div> </div></li><li><div> <span></span><h5> Working with Pandas Data science package</h5></div><div> </div></li></ul></div></div></div><div><h4> Data Visualization Packages</h4><div><div><ul><li><div> <span></span><h5> Working with Matplotlib Data Science package (Part - 1)</h5></div><div> </div></li><li><div> <span></span><h5> Working with Matplotlib Data Science (Part - 2)</h5></div><div> </div></li></ul></div></div></div></div></div>A course by  Uditha Bandara  Microsoft Most Valuable Professional (MVP) RequirementsBeginners level knowledge for working with Data .Programming knowledge not required. Audience  Beginners with no prior programming experience  Anyone interested in learning data science and data analytics<h3> Audience</h3><ul><li> <span></span> <span>Beginners with no prior programming experience</span></li><li> <span></span> <span>Anyone interested in learning data science and data analytics</span></li></ul>

Xpert Learning

Business Analytics & Intelligence

Data Analyst

Data Science and Data Analytics with Python

<h2 style="color: var(--text-heading); box-sizing: border-box;" id="isPasted" fr-original-style="color: var(--text-heading);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">This course at a glance</span></strong></h2><ul style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Accredited by CPDQE</li><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Learn how to clean, process, wrangle, and manipulate data</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Know how to create a resume for a data science job</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Get a grasp of NumPy by learning how to use it for numerical data</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Uncover the secret of different Python libraries, such as Pandas, Matplotlib, SciPy and many more</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Explore machine learning concepts and algorithms</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Discover developing custom data solutions</span></li></ul><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(38, 99, 82) !important;">You can create various applications for science, math, machine learning, and data science with Python. As a high-level, general-purpose programming language, Python is quite English-like and easy to learn. This&nbsp;<b fr-original-style="" style="box-sizing: border-box;">Python for Data Science and Machine Learning</b>&nbsp;course will show you how to use Python for machine learning and data science.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">During the course, you will master every useful Python skill you need for data science and machine learning. If you find it challenging to write complex Python programs, we&rsquo;ll show you how to do so with our step-by-step instructions based on actual work contexts. You&rsquo;ll also learn how to run the regression, classification, and clustering and the specifics of supervised and unsupervised machine learning. Moreover, this course covers how statistics work for data science, hypothesis testing, and much more.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">An expert instructor designed this&nbsp;<b fr-original-style="" style="box-sizing: border-box;">&ldquo;Python for Data Science and Machine Learning&rdquo;</b>&nbsp;course to provide you with the best learning experience possible. Our goal is for you to learn and apply the lessons from this course to improve your skills.&nbsp;</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">To become a master of Python, Enrol Now!</p><h3 style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Accredited by</span></h3><p style="color: rgb(15, 21, 26); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);">This course is <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CPDQE&nbsp;</strong>accredited, which serves as an impactful mechanism for skill enhancement.&nbsp;</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">Continuing Professional Development (CPD) stands as a crucial, widely acknowledged method that aids individuals, organisations, and entire industries in staying current with their skills and knowledge.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">CPD not only facilitates the elevation of global standards and benchmarks, aligning with the growing forces of globalisation and consumer expectations but also transforms learning into a deliberate and proactive process. It motivates professionals to uphold a consistently high level of performance and demonstrates their unwavering commitment to a specific job role or profession.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><br fr-original-style="" style="box-sizing: border-box;"></p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Course Curriculum</span></strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Python for Data Science &amp; Machine Learning from A-Z</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Science &amp; Machine Learning Concepts</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python For Data Science</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Statistics for Data Science</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Probability and Hypothesis Testing</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">NumPy Data Analysis</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Pandas Data Analysis</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Data Visualization</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Machine Learning</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Loading &amp; Exploration</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Cleaning</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Feature Selecting and Engineering</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Linear and Logistic Regression</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">K Nearest Neighbours</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Decision Trees</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Ensemble Learning and Random Forests</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Support Vector Machines</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">K-Means</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">PCA</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Science Career</strong></li></ul>

This course aims to teach you how to use Python for machine learning and data science.



Python for Data Science and Machine Learning

<p>Get ready for an exceptional online learning experience with the <strong>Big Data Analysis, Data Science, Fintech &amp; Python for Data Analyst </strong>bundle! This carefully curated collection of 20 premium courses is designed to cater to a variety of interests and disciplines. Dive into a sea of knowledge and skills, tailoring your learning journey to suit your unique aspirations.</p><p>The Big Data Analysis, Data Science, Fintech &amp; Python for Data Analyst is a dynamic package, that blends the expertise of industry professionals with the flexibility of digital learning. It offers the perfect balance of foundational understanding and advanced insights. Whether you're looking to break into a new field or deepen your existing knowledge, the<strong> Data Analysis </strong>package has something for everyone.</p><p>As part of the Big Data Analysis, Data Science, Fintech &amp; Python for Data Analyst package, you will receive complimentary PDF certificates for all courses in this bundle at no extra cost. Equip yourself with the Data Analysis bundle to confidently navigate your career path or personal development journey. Enrol today and start your career growth!</p><p><strong>This bundle comprises the following courses:</strong></p><p><strong>CPD Quality Standards</strong><strong> Courses:</strong></p><ol><li><strong>Big Data Analytics with PySpark Power BI and MongoDB</strong></li><li><strong>Big Data Analytics with PySpark Tableau Desktop and MongoDB</strong></li><li><strong>Building Big Data Pipelines with PySpark MongoDB and Bokeh</strong></li><li><strong>Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau</strong></li><li><strong>Develop Big Data Pipelines with R, Sparklyr &amp; Power BI</strong></li><li><strong>Learn Python, JavaScript, and Microsoft SQL for Data Science</strong></li><li><strong>SQL for Data Science, Data Analytics, and Data Visualization</strong></li><li><strong>Excel Data Analysis</strong></li><li><strong>Introduction to Data Analytics with Tableau</strong></li><li><strong>Business and Data Analytics for Beginners</strong></li><li><strong>Google Data Studio: Data Analytics </strong></li><li><strong>Basic Data Analysis</strong></li><li><strong>FinTech</strong></li></ol><p><strong>Learning Outcome:</strong></p><ul><li>Gain comprehensive insights into multiple fields.</li><li>Foster critical thinking and problem-solving skills across various disciplines.</li><li>Understand industry trends and best practices through the<strong> Data Analysis</strong> Bundle.</li><li>Develop practical skills applicable to real-world situations.</li><li>Enhance personal and professional growth with <strong>Data Analysis.</strong></li><li>Build a strong knowledge base in your chosen course via <strong>Data Analysis</strong>.</li><li>Benefit from the flexibility and convenience of online learning.</li><li>With the <strong>Data Analysis</strong> package, validate your learning with a CPD certificate.</li></ul><p>Each course in this bundle holds a prestigious CPD accreditation, symbolising exceptional quality. The materials, brimming with knowledge, are regularly updated, ensuring their relevance. This bundle promises not just education but an evolving learning experience. Engage with this extraordinary collection, and prepare to enrich your personal and professional development.</p><p>Embrace the future of learning with the Big Data Analysis, Data Science, Fintech &amp; Python for Data Analyst, a rich anthology of 15 diverse courses. Each course in the <strong>Data Analysis</strong> bundle is handpicked by our experts to ensure a wide spectrum of learning opportunities. ThisBig Data Analysis, Data Science, Fintech &amp; Python for Data Analyst bundle will take you on a unique and enriching educational journey.</p><p>The bundle encapsulates our mission to provide quality, accessible education for all. Whether you are just starting your career, looking to switch industries, or hoping to enhance your professional skill set, the Big Data Analysis, Data Science, Fintech &amp; Python for Data Analyst bundle offers you the flexibility and convenience to learn at your own pace. Make the Data Analysis package your trusted companion in your lifelong learning journey.</p><h2>
        
        <div>CPD</div>
    </h2>

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                                        35 CPD hours / points
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                                            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">
                            <p>The <strong>Big Data Analysis, Data Science, Fintech &amp; Python for Data Analyst</strong> bundle is perfect for:</p><ul><li>Lifelong learners looking to expand their knowledge and skills.</li><li>Professionals seeking to enhance their career with CPD certification.</li><li>Individuals wanting to explore new fields and disciplines.</li><li>Anyone who values flexible, self-paced learning from the comfort of home.</li></ul>
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        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Unleash your potential with the <strong>Big Data Analysis, Data Science, Fintech &amp; Python for Data Analyst </strong>bundle. Acquire versatile skills across multiple fields, foster problem-solving abilities, and stay ahead of industry trends. Ideal for those seeking career advancement, a new professional path, or personal growth. Embrace the journey with the <strong>Data Analysis </strong>bundle package.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate Of Completion</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>Certificate Of Completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p><strong>You will get a complimentary Hard Copy Certificate.</strong></p></div>

                        </div>

NextGen Learning

Continuous Professional Development (CPD)

Big Data Analysis, Data Science, Fintech & Python for Data Analyst

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					<p><span>This comprehensive course on </span><b><span>R Programming for Data Science</span></b> will deepen your understanding on this topic.</p><p><span><span>After successful completion of this course you can acquire the required skills in this sector. This </span><b>R Programming for Data Science</b> comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market.</span></p><p><span><span>So enrol in this course today to fast track your career ladder.</span></span></p>					</div>
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					<p>You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.</p>					</div>
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			<h2>Who is This course for?</h2>		</div>
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					<p><span>There is no experience or previous qualifications required for enrolment on this </span><b><span>R Programming for Data Science</span></b>. It is available to all students, of all academic backgrounds.</p>					</div>
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					<p><span>Our </span><b><span>R Programming for Data Science</span></b> is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.</p><p><span><span>There is no time limit for completing this course, it can be studied in your own time at your own pace.</span></span></p>					</div>
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					<p><span>Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business &amp; Management, Admin, Accountancy &amp; Finance, Secretarial &amp; PA, Teaching &amp; Mentoring etc.</span></p>					</div>
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					</div> <h3>Course Curriculum</h3><div><p>23 sections • 129 lectures • 06:25:00 total length</p></div><div><div><div><div><div><div><div><p>•Introduction to Data Science: 00:01:00</p></div><div><div><p>•Data Science: Career of the Future: 00:04:00</p></div><div><div><p>•What is Data Science?: 00:02:00</p></div><div><div><p>•Data Science as a Process: 00:02:00</p></div><div><div><p>•Data Science Toolbox: 00:03:00</p></div><div><div><p>•Data Science Process Explained: 00:05:00</p></div><div><div><p>•What's Next?: 00:01:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Engine and coding environment: 00:03:00</p></div><div><div><p>•Installing R and RStudio: 00:04:00</p></div><div><div><p>•RStudio: A quick tour: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Arithmetic with R: 00:03:00</p></div><div><div><p>•Variable assignment: 00:04:00</p></div><div><div><p>•Basic data types in R: 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Creating a vector: 00:05:00</p></div><div><div><p>•Naming a vector: 00:04:00</p></div><div><div><p>•Vector selection: 00:06:00</p></div><div><div><p>•Selection by comparison: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•What's a Matrix?: 00:02:00</p></div><div><div><p>•Analyzing Matrices: 00:03:00</p></div><div><div><p>•Naming a Matrix: 00:05:00</p></div><div><div><p>•Adding columns and rows to a matrix: 00:06:00</p></div><div><div><p>•Selection of matrix elements: 00:03:00</p></div><div><div><p>•Arithmetic with matrices: 00:07:00</p></div><div><div><p>•Additional Materials: 00:00:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•What's a Factor?: 00:02:00</p></div><div><div><p>•Categorical Variables and Factor Levels: 00:04:00</p></div><div><div><p>•Summarizing a Factor: 00:01:00</p></div><div><div><p>•Ordered Factors: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•What's a Data Frame?: 00:03:00</p></div><div><div><p>•Creating Data Frames: 00:20:00</p></div><div><div><p>•Selection of Data Frame elements: 00:03:00</p></div><div><div><p>•Conditional selection: 00:03:00</p></div><div><div><p>•Sorting a Data Frame: 00:03:00</p></div><div><div><p>•Additional Materials: 00:00:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Why would you need lists?: 00:01:00</p></div><div><div><p>•Creating a List: 00:06:00</p></div><div><div><p>•Selecting elements from a list: 00:03:00</p></div><div><div><p>•Adding more data to the list: 00:02:00</p></div><div><div><p>•Additional Materials: 00:00:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Equality: 00:03:00</p></div><div><div><p>•Greater and Less Than: 00:03:00</p></div><div><div><p>•Compare Vectors: 00:03:00</p></div><div><div><p>•Compare Matrices: 00:02:00</p></div><div><div><p>•Additional Materials: 00:00:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•AND, OR, NOT Operators: 00:04:00</p></div><div><div><p>•Logical operators with vectors and matrices: 00:04:00</p></div><div><div><p>•Reverse the result: (!): 00:01:00</p></div><div><div><p>•Relational and Logical Operators together: 00:06:00</p></div><div><div><p>•Additional Materials: 00:00:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•The IF statement: 00:04:00</p></div><div><div><p>•IFELSE: 00:03:00</p></div><div><div><p>•The ELSEIF statement: 00:05:00</p></div><div><div><p>•Full Exercise: 00:03:00</p></div><div><div><p>•Additional Materials: 00:00:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Write a While loop: 00:04:00</p></div><div><div><p>•Looping with more conditions: 00:04:00</p></div><div><div><p>•Break: stop the While Loop: 00:04:00</p></div><div><div><p>•What's a For loop?: 00:02:00</p></div><div><div><p>•Loop over a vector: 00:02:00</p></div><div><div><p>•Loop over a list: 00:03:00</p></div><div><div><p>•Loop over a matrix: 00:04:00</p></div><div><div><p>•For loop with conditionals: 00:01:00</p></div><div><div><p>•Using Next and Break with For loop: 00:03:00</p></div><div><div><p>•Additional Materials: 00:00:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•What is a Function?: 00:02:00</p></div><div><div><p>•Arguments matching: 00:03:00</p></div><div><div><p>•Required and Optional Arguments: 00:03:00</p></div><div><div><p>•Nested functions: 00:02:00</p></div><div><div><p>•Writing own functions: 00:03:00</p></div><div><div><p>•Functions with no arguments: 00:02:00</p></div><div><div><p>•Defining default arguments in functions: 00:04:00</p></div><div><div><p>•Function scoping: 00:02:00</p></div><div><div><p>•Control flow in functions: 00:03:00</p></div><div><div><p>•Additional Materials: 00:00:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Installing R Packages: 00:01:00</p></div><div><div><p>•Loading R Packages: 00:04:00</p></div><div><div><p>•Different ways to load a package: 00:02:00</p></div><div><div><p>•Additional Materials: 00:00:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•What is lapply and when is used?: 00:04:00</p></div><div><div><p>•Use lapply with user-defined functions: 00:03:00</p></div><div><div><p>•lapply and anonymous functions: 00:01:00</p></div><div><div><p>•Use lapply with additional arguments: 00:04:00</p></div><div><div><p>•Additional Materials: 00:00:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•What is sapply?: 00:02:00</p></div><div><div><p>•How to use sapply: 00:02:00</p></div><div><div><p>•sapply with your own function: 00:02:00</p></div><div><div><p>•sapply with a function returning a vector: 00:02:00</p></div><div><div><p>•When can't sapply simplify?: 00:02:00</p></div><div><div><p>•What is vapply and why is it used?: 00:04:00</p></div><div><div><p>•Additional Materials: 00:00:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Mathematical functions: 00:05:00</p></div><div><div><p>•Data Utilities: 00:08:00</p></div><div><div><p>•Additional Materials: 00:00:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•grepl &amp; grep: 00:04:00</p></div><div><div><p>•Metacharacters: 00:05:00</p></div><div><div><p>•sub &amp; gsub: 00:02:00</p></div><div><div><p>•More metacharacters: 00:04:00</p></div><div><div><p>•Additional Materials: 00:00:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Today and Now: 00:02:00</p></div><div><div><p>•Create and format dates: 00:06:00</p></div><div><div><p>•Create and format times: 00:03:00</p></div><div><div><p>•Calculations with Dates: 00:03:00</p></div><div><div><p>•Calculations with Times: 00:07:00</p></div><div><div><p>•Additional Materials: 00:00:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Get and set current directory: 00:04:00</p></div><div><div><p>•Get data from the web: 00:04:00</p></div><div><div><p>•Loading flat files: 00:03:00</p></div><div><div><p>•Loading Excel files: 00:05:00</p></div><div><div><p>•Additional Materials: 00:00:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Base plotting system: 00:03:00</p></div><div><div><p>•Base plots: Histograms: 00:03:00</p></div><div><div><p>•Base plots: Scatterplots: 00:05:00</p></div><div><div><p>•Base plots: Regression Line: 00:03:00</p></div><div><div><p>•Base plots: Boxplot: 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Introduction to dplyr package: 00:04:00</p></div><div><div><p>•Using the pipe operator (%&gt;%): 00:02:00</p></div><div><div><p>•Columns component: select(): 00:05:00</p></div><div><div><p>•Columns component: rename() and rename_with(): 00:02:00</p></div><div><div><p>•Columns component: mutate(): 00:02:00</p></div><div><div><p>•Columns component: relocate(): 00:02:00</p></div><div><div><p>•Rows component: filter(): 00:01:00</p></div><div><div><p>•Rows component: slice(): 00:04:00</p></div><div><div><p>•Rows component: arrange(): 00:01:00</p></div><div><div><p>•Rows component: rowwise(): 00:02:00</p></div><div><div><p>•Grouping of rows: summarise(): 00:03:00</p></div><div><div><p>•Grouping of rows: across(): 00:02:00</p></div><div><div><p>•COVID-19 Analysis Task: 00:08:00</p></div><div><div><p>•Additional Materials: 00:00:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Assignment - R Programming for Data Science: 00:00:00</p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>

Programming Fundamentals

R (programming language)

Computer Programming

Trainers Course

R Programming for Data Science

London

<article>
<div>
<h5>Java EVENINGS Course</h5>
</div>
<div></div>
<p>  						 
	</p><div>
	<ul>
		<p>
		<b>Python EVENINGS Course. </b>Basic Python syntax and principles of Object Orientated Programming. 
		<br />Most attendees are in-work IT Professional.
		<br />Private individuals are also very welcome. 
		<br />Evening courses also running.
		<br /><b>Our Style: </b>Hands-on, Practical  
		<br /><b>Location:  </b>Online, Instructor-led  
		<br /><b>Download:  </b>anaconda.com
		<br /><b>Duration:  </b>6 weeks, 1 evening per week, 6pm - 8pm
		<br /><b>Times: </b>arrange a time for your time zone
	    </p>
	</ul>	

<p></p>
</div></article>

<div>
	  <div>
		 <h5>Python Basics EVENINGS Course </h5>
	  </div>
	  <div></div>
	  <p> 
 
				This Python course London Evening covers the essential Python Basics, in our interactive, instructor led Live Virtual Classroom.
				This Python Basics course is a very good introduction to essential fundamental programming concepts using Python as programming language.
				These concepts are daily used by programmers and is your first step to working as a programmer.
				By the end, you'll be comfortable in programming Python code.
				You will have done small projects.
				This will serve for you as examples and samples that you can use to build larger projects.
				You can publish your code online to keep as a portfolio.

<br />
This Python Programming course is hands-on and practical, to give you in-depth, practical experience of the topics covered.

	  </p>
	<div>
	<h5>Week 1: Basics</h5>
	</div>
	<div></div>
	<div> 
					<p>  <strong>Python Data Types and Variables:</strong>  
					<br />  Primitive types; Characters &amp; Strings; Boolean; Working with variables and its scope; Conversion and casting types in Python.	 
					<br /> <strong>Operators and Expressions: </strong>   
					<br />  Introduction of operators; Arithmetic operators; Relational operators; Assignment operator; Logical operators; Increment and decrement operators.
					</p> 
			 
				<p><strong> Decision Making:</strong>
				<br />If statement; If - else statement; If- elif - else statement; Nested if - else.
				</p><p><strong>Using Loops:</strong>
				<br />The while, while-else.
				<br />For loop;
				<br />Jump statements: break, continue;
				<br />Nesting loops.				
				</p>
			</div>
	<div>
	<h5>Week 2: Data Structures</h5>
	</div> <div></div>
	<div> 
				<p> Lists.
					<br />Tuples.
					<br />Sets.
					<br />Dictionary. Nested Dictionary.
					<br />Json Files.
				</p>
			</div>
	<div>
	<h5>Week 3: Python built-in functions</h5>
	</div> <div></div>
	<div> 
				<p>  
				<br />Using Built-in modules and functions:
				<br />Strings Functions.
				<br />Pattern matching with Regex.
				<br />Maths Functions.
				<br />Random Functions.
				<br />Python Date functions.
				</p>
			</div>
	<div>
	<h5>Week 4: OOP: Using Modules, functions, classes</h5>
	</div> <div></div>
	<div>
				<p>  Creating and using custom Functions.
					<br />Creating functions;
					<br />Using parameters and return values;
					<br />
					<br />Introduction to Classes and Objects:
					<br />Creating a Class; 
					<br />Creating an Object; Using an Object; 
					<br />Adding Instance variables; Controlling accessibility; Naming conventions for class members. 
					<br /> Class Constructors; Parameterized Constructors.
					<br />Inheritance. Overload.
				</p>
			</div>

	<div>
	<h5>Week 5: Pandas DataBase Basics</h5>
	</div> <div></div>
	<div>	 
		<p> <b>Connection details:</b>
				Connect to a database using access codes. 
				<br /><b>Select Queries:</b>
				Retrieving records and iterating results sets, using variables and parameters in queries. 
				<br />Working with DDL comands:
				Create a database, drop a database.
				Create a table, alter tables, drop a table. 
				<br />Working with DML Commands:
				Insert, delete, update records.
		</p>
	</div>
	
	 
	<div>
	<h5>Week 6: Pandas Dataframe Basics</h5>
	</div> <div></div>
	<div>	 
		<p> Getting data into a dataframe:
			<br />Dict to Dataframe, Dataframe to Dict.
			<br />Excel To Dict, Dict to Excel , working with Excel data, multiple Excel sheets.

			<br />Dataframe basics:
			<br />Slicing Dataframes,
			<br />Getting information about the dataframe,
			<br />Filter, sort and query a Dataframes,
			<br />Duplicate values,
			<br />Working with null-values,
			<br />Sampling,
			<br />Group by,
			<br />Join.

		</p>
	</div>
	  
</div>
 

<div>   

	   <div id="cincluded">
		  <div>
			 <h5>What is included</h5>
		  </div>

		  <article>
		  <div>
			 <ul>
				<li>PCWorkshops Course Certificate on completion  </li>
									   
				<li>Python Course Notes</li>
				<li>Python Code Examples</li>
				<li>Practical Python Course exercises and Code Samples</li>
				<li>After the course: 1-Hour personalised online revision session</li>
			 </ul>
		  </div>
		  </article>
	   </div>
				    
<div>
	                  <div>
	                     <h5 id="cincluded2">Book the Python  Course</h5>
	                  </div>
 
<div id="eventbrite-widget-container-60636103217"></div>




							 
</div>
				   <div id="caboutus">
	                  <div>
	                     <h5>About us</h5>
	                  </div> 
	                  	<article>
					    <div>
					    	<div>
					    		<div>
					    			
					    			<h5>Our experienced trainers are award winners. </h5> 
					    			<ul>
										<li>
											More about us
										</li>
										<li>
											FAQ's
										</li>
										<li>
											Client Comments  
										</li>
									 </ul>
					    		</div>
					    		<div>
					    			
					    				
					    			
					    		</div>
					    	</div>
	                     
					    </div>
					    </article>						
	               </div>
				   
				   
	        </div>

Python EVENINGS Course
Basic Python syntax and principles of object-oriented programming. 
Most attendees are in-work IT Professional. 
Private individuals are also very welcome. 
Evening courses also running. 

Details:
Our Style: Hands-on, Practical 
Location: Online, Instructor-led 
Download: anaconda.com 
Duration: 4 weeks, 1 evening per week, 6pm - 8pm 
Times: arrange a time for your time zone 
How to attend:  
Connection Link For the course: https://meet.goto.com/910697125
Once you have made the payment, use the above link on the date booked, to connect with the trainer. 
Python Basics EVENINGS Course
 This Python course London Evening covers the essential Python Basics, in our interactive, instructor led Live Virtual Classroom. This Python Basics course is a very good introduction to essential fundamental programming concepts using Python as programming language. These concepts are daily used by programmers and is your first step to working as a programmer. By the end, you'll be comfortable in programming Python code. You will have done small projects. This will serve for you as examples and samples that you can use to build larger projects. You can publish your code online to keep as a portfolio. 

This Python Programming course is hands-on and practical, to give you in-depth, practical experience of the topics covered. 
Week 1: Basics
 Python Data Types and Variables: 
Primitive Data types; Working with variables and its scope; Conversion and casting types in Python. 
 Operators and Expressions:  
 Introduction of operators; Arithmetic operators; Relational operators; Assignment operator; Logical operators; Increment and decrement operators. 
 Decision Making: 
If statement; If - else statement; If- elif - else statement; Nested if - else. 
Using Loops: 
The while, while-else. 
For loop; 
Jump statements: break, continue; 
Nesting loops. 
Week 2: Data Structures & Database
Lists.  
Tuples. 
Sets. 
Dictionary. Nested Dictionary. 
Json Files. 
Reading csv and text files
Connecting with a database and run SQL Queries. 
Week 3: Python built-in functions &  Pandas Dataframe Basics
 
Using built-in modules and functions: 
Strings Functions. 
Maths Functions. 
Random Functions. 
Data Frames; 
Getting data into a dataframe: 
Dict to Dataframe, Dataframe to Dict.  
Dataframe basics: 
Slicing Dataframes, 
Getting information about the dataframe, 
Filter and query a Dataframes, 
Working with null-values.
Week 4: OOP: Using Modules, functions, classes
 Creating and using custom Functions. 
Creating functions; 
Using parameters and return values; 
 
Introduction to Classes and Objects: 
Creating a Class; 
Creating an Object; Using an Object; 
Adding instance variables; controlling accessibility; and naming conventions for class members. 
 Class Constructors; Parameterized Constructors. 
Inheritance. Overload. 

What is included
PCWorkshops Course Certificate on completion
Python Course Notes
Python Code Examples
Practical Python Course exercises and Code Samples
After the course: 1-Hour personalised online revision session
  

PCWorkshops

Online Course Creation

Professional Skills Course (PSC)

Instructor

Basic Python syntax and principles of Object Orientated Programming. 
Most attendees are in-work IT Professional. 
Private individuals are also very welcome. 
Evening courses also running. 
Our Style: Hands-on, Practical 
Location: Online, Instructor-led 
Download: anaconda.com 
Duration: 6 weeks, 1 evening per week, 6pm - 8pm 
Times: arrange a time for your time zone 

Python EVENINGS Course, Online Instructor-led

General admission

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">This is a free Q&amp;A session hosted by Paul McGowran of Proactive NLP Ltd and the owner of <a href="https://staging2.alifewithoutanxiety.com" fr-original-style="" style="user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;">alifewithoutanxiety.com</a>.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">During the session Paul will open to all your questions about how clinical hypnotherapy and NLP can help you achieve a life without anxiety.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">This is a &#39;taster&#39; session, &nbsp;that sets out to show delegates how they can use NLP &amp; Hypnotherapy techniques to achieve a level of control over their, or their client&#39;s anxiety. &nbsp;Learn how NeuroScience is now demonstrating what we in the field of NLP have been teaching for decades now...our repeated patterns of thinking create the results we achieve. &nbsp;Anxiety...is the result of an ingrained way of thinking. &nbsp;The good news is that it does not take long for a new pattern to replace the old pattern. &nbsp;A pattern that can help you achieve happiness and contentedness.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The session is designed to last for one hour (1 Hr) and the plan is to run at least one per month.</p>

This is a free Q&A session hosted by Paul McGowran of Proactive NLP Ltd and the owner of alifewithoutanxiety.com.
During the session Paul will open to all your questions about how clinical hypnotherapy and NLP can help you achieve a life without anxiety.
This is a 'taster' session, that sets out to show delegates how they can use NLP & Hypnotherapy techniques to achieve a level of control over their, or their client's anxiety. Learn how NeuroScience is now demonstrating what we in the field of NLP have been teaching for decades now...our repeated patterns of thinking create the results we achieve. Anxiety...is the result of an ingrained way of thinking. The good news is that it does not take long for a new pattern to replace the old pattern. A pattern that can help you achieve happiness and contentedness.
The session is designed to last for one hour (1 Hr) and the plan is to run at least one per month.

Proactive NLP Ltd

Stress Management

Hypnotherapy

Depression

Neuro-Linguistic Programming (NLP)

Anxiety Management

NLP Practitioner

Happiness

Anxiety

Therapy

A life without anxiety can be yours with our bespoke blend of clinical hypnotherapy and advanced NLP therapy techniques.  Start with a free online Q&A session.  Start enjoying a life free from anxiety

Anxiety Relief with NLP & Hypnotherapy

<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;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Welcome to Becoming Anxiety Free with NLP!</strong></strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Are you tired of feeling anxious and stressed all the time? Join us for this online event where we will explore how Neuro-Linguistic Programming (NLP) can help you overcome anxiety and live a more peaceful life.</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;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Date:</strong></strong>&nbsp;Thurs Jul 04 2024</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;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Time:</strong></strong>&nbsp;10:00:00 GMT+0100 (British Summer Time)</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">During this event, you will learn practical techniques and strategies to reprogram your mind and break free from the grips of anxiety. Say goodbye to constant worrying and hello to a calmer, more confident you!</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Don&#39;t miss out on this opportunity to transform your life. Register now and take the first step towards becoming anxiety-free with NLP!</p>

Welcome to Becoming Anxiety Free with NLP!
Are you tired of feeling anxious and stressed all the time? Join us for this online event where we will explore how Neuro-Linguistic Programming (NLP) can help you overcome anxiety and live a more peaceful life.
Date: Thurs Jul 04 2024
Time: 10:00:00 GMT+0100 (British Summer Time)
During this event, you will learn practical techniques and strategies to reprogram your mind and break free from the grips of anxiety. Say goodbye to constant worrying and hello to a calmer, more confident you!
Don't miss out on this opportunity to transform your life. Register now and take the first step towards becoming anxiety-free with NLP!

Psychological Wellbeing

Emotional Freedom Techniques (EFT)

Become free from Anxiety and say goodbye to managing your anxiety and start living a happy and content life.  This masterclass will show you can change how your mind works to eliminate the negative and debilitating emotional impact of anxiety, once and for all!

Becoming Anxiety Free with NLP

<p><strong>Start your career in Data Science and earn up to £90,000 per month.</strong></p><p>Are you eager to dive into the high-speed world of Data Science powered by Python? In the UK alone, Data Scientist job postings witnessed a dramatic increase of 67% in 2022, emphasising the burgeoning demand for proficient Python programmers. Amid such a dynamic job market, our online course - Data Science with Python, serves as your stepping-stone to a universe of opportunities. Whether you're taking your first step into the realm of Data Science or aiming to augment your existing skills, our program offers peerless support, ensuring you're industry-ready by the time you complete our course.</p><p><strong>Our mission is simple</strong> - to be your trusted partner every step of the way, from training to employment.</p><p>In addition to teaching you the technical skills you need, we will also provide you with <strong>career mentoring and support</strong>. We will help you build your resume, prepare for interviews, and land your <strong>dream job</strong>. We also have<strong> partnerships with many companies</strong> that are hiring Data <strong>Scientists</strong>, so we can help you get your foot in the door.</p><p>If you are not happy with our service, we also offer a <strong>100% money-back guarantee</strong>. So what are you waiting for? Enrol in our Data Scientist with Python Training Program today and start your journey to becoming a successful Data Scientist!</p><p>If you have any questions, you can <strong>contact us</strong>. We will be happy to provide you with all the information you need.</p><p><strong>Why Choose Us?</strong></p><p>So, what sets us apart from other programs? Let's dive into the exceptional benefits you'll experience when you join our Data Scientist with Python:</p><ul><li><strong>Personalised Guidance:</strong> We believe in the power of individual attention. Our experienced mentors will provide one-on-one counselling sessions tailored to your specific needs. Whether you're a beginner or have some Python experience, we will guide you towards honing your skills and developing a strong foundation in both Data Science and Python.<br /><br /></li><li><strong>One-On-One Consultation Sessions with Industry Experts:</strong> Gain invaluable insights and guidance from seasoned professionals who have thrived in the Data Science field. Our consultation sessions provide you with insider tips, tricks, and advice, empowering you to navigate the industry with confidence and expertise.<br /><br /></li><li><strong>Extensive Job Opportunities:</strong> We have established partnerships with numerous companies actively seeking Data Scientists. Through our network, we'll connect you with exclusive job openings that are not easily accessible elsewhere.<br /><br /></li><li><strong>Interview Preparation:</strong> No more stress over unexpected interview questions. We provide you with access to a comprehensive database of potential interview questions curated over years of industry experience. Walk into your interviews confident, well-prepared, and ready to impress.<br /><br /></li><li><strong>Money-Back Guarantee:</strong> Your satisfaction is our top priority. We are confident in the quality of our training and support, which is why we offer a <strong>100% money-back guarantee. </strong>If, for any reason, you're not happy with our services, we'll refund your investment, no questions asked. We believe in the value we provide and want you to feel completely satisfied with your decision to join us.<br /><br /></li><li><strong>Continuous Career Support: </strong>Our commitment doesn't end when you secure a job. We'll be there for you throughout your career journey, offering continued support and guidance.</li></ul><p><strong>Here are the courses we will provide once you enrol in the program:</strong></p><ul><li>Course 01: Business and Data Analytics for Beginners</li><li>Course 02: Quick Data Science Approach from Scratch</li><li>Course 03: Learn MySQL from Scratch for Data Science and Analytics</li><li>Course 04: SQL for Data Science, Data Analytics and Data Visualization</li><li>Course 05: Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li>Course 06: R Programming for Data Science</li><li>Course 07: Python Data Science with Numpy, Pandas and Matplotlib</li><li>Course 08: Complete Python Machine Learning &amp; Data Science Fundamentals</li><li>Course 09: 2021 Data Science &amp; Machine Learning with R from A-Z</li><li>Course 10: Python Programming from Scratch with My SQL Database</li><li>Course 11: Level 2 Python Course</li><li>Course 12: Machine Learning for Predictive Maps in Python and Leaflet</li><li>Course 13: Python Programming Bible | Networking, GUI, Email, XML, CGI</li><li>Course 14: Python for Spatial Analysis in ArcGIS</li><li>Course 15: Ultimate Python Training for Beginners</li><li>Course 16: PyScript Fundamentals Training</li></ul><p><strong>How Can We Offer Job Guarantees?</strong></p><p>HF Online provides consultancy, professional and educational services to many of the companies in the UK. During our intense exclusive training program, you will not just gain and complete the industry valuable certification but will gain industry experience as well, which is imperative to get your 1st job in the sector.</p> <p><strong>The </strong><strong>Data Scientist with Python </strong><strong>Program is completed in 9 easy steps:<br /><br /></strong></p><p><strong>Step 1: Enrol in the Programme</strong></p><p>Begin your exciting journey with us by enrolling in the Data Science with Python Training program. Complete your registration and make a secure online payment. Remember, we offer a 14-day money-back guarantee if you're not completely satisfied.</p><p>After you enrol in the Program, you will get lifetime access to 16 premium courses related to Data Science with Python. These courses will teach you the knowledge and skills required to become a successful Data Scientist. Our customer service team will help you and keep in contact with you every step of the way. So you won't have to worry about a thing!</p><p><strong>Step 2: Initial One-On-One Counselling Session</strong></p><p>Once enrolled, you will be paired with a dedicated career mentor. Schedule your first one-on-one session to discuss your career aspirations, skills, experience, and any areas for potential growth. This conversation will shape your learning and development path.<br /><br /></p><p><strong>Step 3 - </strong><strong>Certification upon Course Completion</strong></p><p>After learning from the courses, you must obtain certificates for each course. There will be exams for every course, and you have to pass them to get your certificate. To pass successfully, you must get 90% marks. Once you pass the exams, you will receive hardcopy certificates. These certificates will prove that you're an expert in the subject.<br /><br /></p><p><strong>Step 4: </strong><strong>CV Revamping</strong></p><p>Our team of professionals will build you a compelling CV and <strong>LinkedIn profile</strong>. We'll ensure it presents your skills and qualifications effectively and is tailored to the needs and expectations of the Data Science with Python industry. With these powerful tools in hand, you'll be fully prepared to tackle job interviews confidently. <br /><br /></p><p><strong>Step 5: Building Network and Submitting CV</strong></p><p>We understand the power of casting a wide net. We'll strategically submit your CV to various platforms and networks, expanding your reach and connecting you with valuable opportunities that align with your career goals. We will also make connections with many<strong> high-profile individuals and companies</strong> through your LinkedIn profile.<br /><br /></p><p><strong>Step 6: Interview Preparation</strong></p><p>With your CV ready, we'll move on to interview preparation. Gain exclusive access to our database of potential interview questions. Through simulated interviews with your mentor, you'll practice your responses and receive valuable feedback to further refine your skills.<br /><br /></p><p><strong>Step 7: Securing Job Interviews</strong></p><p>Leveraging our partnerships with leading companies, <strong>we'll secure job interviews for you</strong>. We'll ensure you get the opportunity to showcase your skills to potential employers and <strong>get the dream job you want</strong>.<br /><br /></p><p><strong>Step 8: Post-Interview Support</strong></p><p>Post-interview, we'll provide a debriefing session to reflect on your performance and identify areas of improvement for future interviews if necessary. Remember, our commitment extends until you land your dream job.<br /><br /></p><p><strong>Step 9: Celebrate Your New Job!</strong></p><p>Once you've secured your dream job in Data Science with Python, it's time to celebrate! However, our support doesn't end there. We'll provide you with ongoing career advice to ensure you continue to thrive in your new role.</p><p>We're excited to accompany you on this journey to success. Enrol today, and let's get started! Your path to a successful career in Data Science with Python begins with us.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        100 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>Beginners with an interest in Data Science and Python programming.</li><li>Programmers and Software Engineers looking to transition into Data Science.</li><li>Business Analysts and Project Managers aiming to leverage Data Science in their domains.</li><li>Researchers and Academics who wish to utilise Python for complex data analysis.</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>No experience required. Just enrol &amp; start learning.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Scientist: £60,000 - £90,000</li><li>Machine Learning Engineer: £65,000 - £100,000</li><li>Data Analyst: £40,000 - £60,000</li><li>Business Intelligence Analyst: £45,000 - £70,000</li><li>Data Engineer: £50,000 - £80,000</li><li>Big Data Architect: £70,000 - £120,000</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited e-Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPD Accredited Framed (Hardcopy) Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>Enrolment Letter</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>QLS Endorsed Hard Copy Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>Student ID Card</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Start your career in Data Science and earn up to £90,000 per month.
Are you eager to dive into the high-speed world of Data Science powered by Python? In the UK alone, Data Scientist job postings witnessed a dramatic increase of 67% in 2022, emphasising the burgeoning demand for proficient Python programmers. Amid such a dynamic job market, our online course - Data Science with Python, serves as your stepping-stone to a universe of opportunities. Whether you're taking your first step into the realm of Data Science or aiming to augment your existing skills, our program offers peerless support, ensuring you're industry-ready by the time you complete our course.
Our mission is simple - to be your trusted partner every step of the way, from training to employment.
In addition to teaching you the technical skills you need, we will also provide you with career mentoring and support. We will help you build your resume, prepare for interviews, and land your dream job. We also have partnerships with many companies that are hiring Data Scientists, so we can help you get your foot in the door.
If you are not happy with our service, we also offer a 100% money-back guarantee. So what are you waiting for? Enrol in our Data Scientist with Python Training Program today and start your journey to becoming a successful Data Scientist!
If you have any questions, you can contact us. We will be happy to provide you with all the information you need.
Why Choose Us?
So, what sets us apart from other programs? Let's dive into the exceptional benefits you'll experience when you join our Data Scientist with Python:
Personalised Guidance: We believe in the power of individual attention. Our experienced mentors will provide one-on-one counselling sessions tailored to your specific needs. Whether you're a beginner or have some Python experience, we will guide you towards honing your skills and developing a strong foundation in both Data Science and Python.
One-On-One Consultation Sessions with Industry Experts: Gain invaluable insights and guidance from seasoned professionals who have thrived in the Data Science field. Our consultation sessions provide you with insider tips, tricks, and advice, empowering you to navigate the industry with confidence and expertise.
Extensive Job Opportunities: We have established partnerships with numerous companies actively seeking Data Scientists. Through our network, we'll connect you with exclusive job openings that are not easily accessible elsewhere.
Interview Preparation: No more stress over unexpected interview questions. We provide you with access to a comprehensive database of potential interview questions curated over years of industry experience. Walk into your interviews confident, well-prepared, and ready to impress.
Money-Back Guarantee: Your satisfaction is our top priority. We are confident in the quality of our training and support, which is why we offer a 100% money-back guarantee. If, for any reason, you're not happy with our services, we'll refund your investment, no questions asked. We believe in the value we provide and want you to feel completely satisfied with your decision to join us.
Continuous Career Support: Our commitment doesn't end when you secure a job. We'll be there for you throughout your career journey, offering continued support and guidance.
Here are the courses we will provide once you enrol in the program:
Course 01: Business and Data Analytics for Beginners
Course 02: Quick Data Science Approach from Scratch
Course 03: Learn MySQL from Scratch for Data Science and Analytics
Course 04: SQL for Data Science, Data Analytics and Data Visualization
Course 05: Statistics & Probability for Data Science & Machine Learning
Course 06: R Programming for Data Science
Course 07: Python Data Science with Numpy, Pandas and Matplotlib
Course 08: Complete Python Machine Learning & Data Science Fundamentals
Course 09: 2021 Data Science & Machine Learning with R from A-Z
Course 10: Python Programming from Scratch with My SQL Database
Course 11: Level 2 Python Course
Course 12: Machine Learning for Predictive Maps in Python and Leaflet
Course 13: Python Programming Bible | Networking, GUI, Email, XML, CGI
Course 14: Python for Spatial Analysis in ArcGIS
Course 15: Ultimate Python Training for Beginners
Course 16: PyScript Fundamentals Training
How Can We Offer Job Guarantees?
HF Online provides consultancy, professional and educational services to many of the companies in the UK. During our intense exclusive training program, you will not just gain and complete the industry valuable certification but will gain industry experience as well, which is imperative to get your 1st job in the sector.
The Data Scientist with Python Program is completed in 9 easy steps:


Step 1: Enrol in the Programme
Begin your exciting journey with us by enrolling in the Data Science with Python Training program. Complete your registration and make a secure online payment. Remember, we offer a 14-day money-back guarantee if you're not completely satisfied.
After you enrol in the Program, you will get lifetime access to 16 premium courses related to Data Science with Python. These courses will teach you the knowledge and skills required to become a successful Data Scientist. Our customer service team will help you and keep in contact with you every step of the way. So you won't have to worry about a thing!
Step 2: Initial One-On-One Counselling Session
Once enrolled, you will be paired with a dedicated career mentor. Schedule your first one-on-one session to discuss your career aspirations, skills, experience, and any areas for potential growth. This conversation will shape your learning and development path.


Step 3 - Certification upon Course Completion
After learning from the courses, you must obtain certificates for each course. There will be exams for every course, and you have to pass them to get your certificate. To pass successfully, you must get 90% marks. Once you pass the exams, you will receive hardcopy certificates. These certificates will prove that you're an expert in the subject.


Step 4: CV Revamping
Our team of professionals will build you a compelling CV and LinkedIn profile. We'll ensure it presents your skills and qualifications effectively and is tailored to the needs and expectations of the Data Science with Python industry. With these powerful tools in hand, you'll be fully prepared to tackle job interviews confidently. 


Step 5: Building Network and Submitting CV
We understand the power of casting a wide net. We'll strategically submit your CV to various platforms and networks, expanding your reach and connecting you with valuable opportunities that align with your career goals. We will also make connections with many high-profile individuals and companies through your LinkedIn profile.


Step 6: Interview Preparation
With your CV ready, we'll move on to interview preparation. Gain exclusive access to our database of potential interview questions. Through simulated interviews with your mentor, you'll practice your responses and receive valuable feedback to further refine your skills.


Step 7: Securing Job Interviews
Leveraging our partnerships with leading companies, we'll secure job interviews for you. We'll ensure you get the opportunity to showcase your skills to potential employers and get the dream job you want.


Step 8: Post-Interview Support
Post-interview, we'll provide a debriefing session to reflect on your performance and identify areas of improvement for future interviews if necessary. Remember, our commitment extends until you land your dream job.


Step 9: Celebrate Your New Job!
Once you've secured your dream job in Data Science with Python, it's time to celebrate! However, our support doesn't end there. We'll provide you with ongoing career advice to ensure you continue to thrive in your new role.
We're excited to accompany you on this journey to success. Enrol today, and let's get started! Your path to a successful career in Data Science with Python begins with us.
 CPD 
100 CPD hours / points  Accredited by CPD Quality Standards
 Who is this course for? 
Beginners with an interest in Data Science and Python programming.
Programmers and Software Engineers looking to transition into Data Science.
Business Analysts and Project Managers aiming to leverage Data Science in their domains.
Researchers and Academics who wish to utilise Python for complex data analysis.
 Requirements 
No experience required. Just enrol & start learning.
 Career path 
Data Scientist: £60,000 - £90,000
Machine Learning Engineer: £65,000 - £100,000
Data Analyst: £40,000 - £60,000
Business Intelligence Analyst: £45,000 - £70,000
Data Engineer: £50,000 - £80,000
Big Data Architect: £70,000 - £120,000
 Certificates 
CPD Accredited e-Certificate
 Digital certificate - Included 
CPD Accredited Framed (Hardcopy) Certificate
 Hard copy certificate - Included 
Enrolment Letter
 Digital certificate - Included 
QLS Endorsed Hard Copy Certificate
 Hard copy certificate - Included 
Student ID Card
 Digital certificate - Included 

Academy for Health and Fitness

Data Science with Python - IT Job Ready Program with Career Support & Money Back Guarantee

Loughborough

<div>
		

		<div>
			
			<p>This course will enable you to bring value to the business by putting data science concepts into practice. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, but it can also inform - by guiding decisions and influencing day-to-day operations.</p>
			<div><i></i> 5 Days - 1 Exam</div>
			<div><i></i>  £595.00 incl. VAT</div>
			
			
		</div>
	</div>

	

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	</div>

	

    <div>

		
		 
		

		<div></div>

		<div>
			<p>Want to run this event in-house?  Enquire about running this event in-house </p>
			
		</div>

		<div>
			<p>Interested in attending? Have a suggestion about running this event near you?<br />Register your interest now</p>
			
		</div>
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		<h3>Description</h3>
		<p></p><p>For a business to thrive in our data-driven world, it must treat data as one of its most important assets. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, but it can also inform - by guiding decisions and influencing day-to-day operations. This calls for a robust workforce of professionals who can analyze, understand, manipulate, and present data within an effective and repeatable process framework. In other words, the business world needs data science practitioners. This course will enable you to bring value to the business by putting data science concepts into practice. This course includes hands-on activities for each topic area.</p>
<h3>Course Objectives: </h3>
<p>In this course, you will implement data science techniques in order to address business issues. You will:</p>
<p>• Use data science principles to address business issues. <br />• Apply the extract, transform, and load (ETL) process to prepare datasets. <br />• Use multiple techniques to analyze data and extract valuable insights. <br />• Design a machine learning approach to address business issues. <br />• Train, tune, and evaluate classification models. <br />• Train, tune, and evaluate regression and forecasting models. <br />• Train, tune, and evaluate clustering models. <br />• Finalize a data science project by presenting models to an audience, putting models into production, and monitoring model performance.</p>
<p></p>
	
		<h3>Target Student</h3>
		<p></p><p>This course is designed for business professionals who leverage data to address business issues. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming.  However, there is not necessarily a single organizational role that this course targets. A prospective student might be a programmer looking to expand their knowledge of how to guide business decisions by collecting, wrangling, analyzing, and manipulating data through code; or a data analyst with a background in applied math and statistics who wants to take their skills to the next level; or any number of other data-driven situations. </p>
<p>Ultimately, the target student is someone who wants to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business.  This course is also designed to assist students in preparing for the Certified Data Science Practitioner certification.</p>
<p></p>
	
		<h3>Prerequisites</h3>
		<p></p><p>To ensure your success in this course, you should have at least a high-level understanding of fundamental data science concepts, including, but not limited to: types of data, data science roles, the overall data science lifecycle, and the benefits and challenges of data science.</p>
<p>You should have also have experience with high-level programming languages like Python. Being comfortable using fundamental Python data science libraries like NumPy and pandas is highly recommended.</p>
<p>In addition to programming, you should also have experience working with databases, including querying languages like SQL. </p>
<p></p>
	
		<h3>Modules/Lessons</h3>
		<p></p><h4>Lesson 1: Addressing Business Issues with Data Science</h4>
<div>
<p>Topic A: Initiate a Data Science Project<br />Topic B: Formulate a Data Science Problem</p>
<h4>Lesson 2: Extracting, Transforming, and Loading Data</h4>
<p>Topic A: Extract Data <br />Topic B: Transform Data <br />Topic C: Load Data</p>
<h4>Lesson 3: Analyzing Data</h4>
<p>Topic A: Examine Data<br />Topic B: Explore the Underlying Distribution of Data <br />Topic C: Use Visualizations to Analyze Data<br />Topic D: Preprocess Data</p>
<h4>Lesson 4: Designing a Machine Learning Approach</h4>
<p>Topic A: Identify Machine Learning Concepts<br />Topic B: Test a Hypothesis</p>
</div>
<h4>Lesson 5: Developing Classification Models</h4>
<p>Topic A: Train and Tune Classification Models <br />Topic B: Evaluate Classification Models</p>
<h4>Lesson 6: Developing Regression Models</h4>
<p>Topic A: Train and Tune Regression Models <br />Topic B: Evaluate Regression Models</p>
<h4>Lesson 7: Developing Clustering Models</h4>
<p>Topic A: Train and Tune Clustering Models <br />Topic B: Evaluate Clustering Models</p>
<h4>Lesson 8: Finalizing a Data Science Project</h4>
<p>Topic A: Communicate Results to Stakeholders <br />Topic B: Demonstrate Models in a Web App <br />Topic C: Implement and Test Production Pipelines</p>
<p></p>

Mpi Learning - Professional Learning And Development Provider

This course will enable you to bring value to the business by putting data science concepts into practice. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, but it can also inform - by guiding decisions and influencing day-to-day operations.

Certified Data Science Practitioner

<p><strong>Start your career in Data Science and earn up to £90,000 per month.</strong></p><p>Are you eager to dive into the high-speed world of Data Science powered by Python? In the UK alone, Data Scientist job postings witnessed a dramatic increase of 67% in 2022, emphasising the burgeoning demand for proficient Python programmers. Amid such a dynamic job market, our online course - Data Science with Python, serves as your stepping-stone to a universe of opportunities. Whether you're taking your first step into the realm of Data Science or aiming to augment your existing skills, our program offers peerless support, ensuring you're industry-ready by the time you complete our course.</p><p><strong>Our mission is simple</strong> - to be your trusted partner every step of the way, from training to employment.</p><p>In addition to teaching you the technical skills you need, we will also provide you with <strong>career mentoring and support</strong>. We will help you build your resume, prepare for interviews, and land your <strong>dream job</strong>. We also have<strong> partnerships with many companies</strong> that are hiring Data <strong>Scientists</strong>, so we can help you get your foot in the door.</p><p>If you are not happy with our service, we also offer a <strong>100% money-back guarantee</strong>. So what are you waiting for? Enrol in our Data Scientist with Python Training Program today and start your journey to becoming a successful Data Scientist!</p><p>If you have any questions, you can <strong>contact us</strong>. We will be happy to provide you with all the information you need.</p><p><strong>Why Choose Us?</strong></p><p>So, what sets us apart from other programs? Let's dive into the exceptional benefits you'll experience when you join our Data Scientist with Python:</p><ul><li><strong>Personalised Guidance:</strong> We believe in the power of individual attention. Our experienced mentors will provide one-on-one counselling sessions tailored to your specific needs. Whether you're a beginner or have some Python experience, we will guide you towards honing your skills and developing a strong foundation in both Data Science and Python.<br /><br /></li><li><strong>One-On-One Consultation Sessions with Industry Experts:</strong> Gain invaluable insights and guidance from seasoned professionals who have thrived in the Data Science field. Our consultation sessions provide you with insider tips, tricks, and advice, empowering you to navigate the industry with confidence and expertise.<br /><br /></li><li><strong>Extensive Job Opportunities:</strong> We have established partnerships with numerous companies actively seeking Data Scientists. Through our network, we'll connect you with exclusive job openings that are not easily accessible elsewhere. Our aim is to maximise your employment prospects and provide you with a range of exciting opportunities to choose from.<br /><br /></li><li><strong>Interview Preparation:</strong> No more stress over unexpected interview questions. We provide you with access to a comprehensive database of potential interview questions curated over years of industry experience. Walk into your interviews confident, well-prepared, and ready to impress.<br /><br /></li><li><strong>Money-Back Guarantee:</strong> Your satisfaction is our top priority. We are confident in the quality of our training and support, which is why we offer a <strong>100% money-back guarantee. </strong>If, for any reason, you're not happy with our services, we'll refund your investment, no questions asked. We believe in the value we provide and want you to feel completely satisfied with your decision to join us.<br /><br /></li><li><strong>Continuous Career Support: </strong>Our commitment doesn't end when you secure a job. We'll be there for you throughout your career journey, offering continued support and guidance. Whether you need advice on career advancement, assistance with new projects, or simply a friendly ear to share your achievements, we'll be your trusted partner for long-term success.</li></ul><p><strong>Here are the courses we will provide once you enrol in the program:</strong></p><ul><li>Course 01: Business and Data Analytics for Beginners</li><li>Course 02: Quick Data Science Approach from Scratch</li><li>Course 03: Learn MySQL from Scratch for Data Science and Analytics</li><li>Course 04: SQL for Data Science, Data Analytics and Data Visualization</li><li>Course 05: Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li>Course 06: R Programming for Data Science</li><li>Course 07: Python Data Science with Numpy, Pandas and Matplotlib</li><li>Course 08: Complete Python Machine Learning &amp; Data Science Fundamentals</li><li>Course 09: 2021 Data Science &amp; Machine Learning with R from A-Z</li><li>Course 10: Python Programming from Scratch with My SQL Database</li><li>Course 11: Level 2 Python Course</li><li>Course 12: Machine Learning for Predictive Maps in Python and Leaflet</li><li>Course 13: Python Programming Bible | Networking, GUI, Email, XML, CGI</li><li>Course 14: Python for Spatial Analysis in ArcGIS</li><li>Course 15: Ultimate Python Training for Beginners</li><li>Course 16: PyScript Fundamentals Training</li></ul><p>These courses will help you to develop your knowledge and skills to become a <strong>successful </strong>Cyber Security Expert.</p> <p><strong>The </strong><strong>Data Scientist with Python </strong><strong>Program is completed in 9 easy steps:<br /><br /></strong></p><p><strong>Step 1: Enrol in the Programme</strong></p><p>Begin your exciting journey with us by enrolling in the Data Science with Python Training program. Complete your registration and make a secure online payment. Remember, we offer a 14-day money-back guarantee if you're not completely satisfied.</p><p>After you enrol in the Program, you will get lifetime access to 16 premium courses related to Data Science with Python. These courses will teach you the knowledge and skills required to become a successful Data Scientist. Our customer service team will help you and keep in contact with you every step of the way. So you won't have to worry about a thing!</p><p><strong>Step 2: Initial One-On-One Counselling Session</strong></p><p>Once enrolled, you will be paired with a dedicated career mentor. Schedule your first one-on-one session to discuss your career aspirations, skills, experience, and any areas for potential growth. This conversation will shape your learning and development path.<br /><br /></p><p><strong>Step 3 - </strong><strong>Certification upon Course Completion</strong></p><p>After learning from the courses, you must obtain certificates for each course. There will be exams for every course, and you have to pass them to get your certificate. To pass successfully, you must get 90% marks. Once you pass the exams, you will receive hardcopy certificates. These certificates will prove that you're an expert in the subject.<br /><br /></p><p><strong>Step 4: </strong><strong>CV Revamping</strong></p><p>Our team of professionals will build you a compelling CV and <strong>LinkedIn profile</strong>. We'll ensure it presents your skills and qualifications effectively and is tailored to the needs and expectations of the Data Science with Python industry. With these powerful tools in hand, you'll be fully prepared to tackle job interviews confidently. <br /><br /></p><p><strong>Step 5: Building Network and Submitting CV</strong></p><p>We understand the power of casting a wide net. We'll strategically submit your CV to various platforms and networks, expanding your reach and connecting you with valuable opportunities that align with your career goals. We will also make connections with many<strong> high-profile individuals and companies</strong> through your LinkedIn profile.<br /><br /></p><p><strong>Step 6: Interview Preparation</strong></p><p>With your CV ready, we'll move on to interview preparation. Gain exclusive access to our database of potential interview questions. Through simulated interviews with your mentor, you'll practice your responses and receive valuable feedback to further refine your skills.<br /><br /></p><p><strong>Step 7: Securing Job Interviews</strong></p><p>Leveraging our partnerships with leading companies, <strong>we'll secure job interviews for you</strong>. We'll ensure you get the opportunity to showcase your skills to potential employers and <strong>get the dream job you want</strong>.<br /><br /></p><p><strong>Step 8: Post-Interview Support</strong></p><p>Post-interview, we'll provide a debriefing session to reflect on your performance and identify areas of improvement for future interviews if necessary. Remember, our commitment extends until you land your dream job.<br /><br /></p><p><strong>Step 9: Celebrate Your New Job!</strong></p><p>Once you've secured your dream job in Data Science with Python, it's time to celebrate! However, our support doesn't end there. We'll provide you with ongoing career advice to ensure you continue to thrive in your new role.</p><p>We're excited to accompany you on this journey to success. Enrol today, and let's get started! Your path to a successful career in Data Science with Python begins with us.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        100 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>Beginners with an interest in Data Science and Python programming.</li><li>Programmers and Software Engineers looking to transition into Data Science.</li><li>Business Analysts and Project Managers aiming to leverage Data Science in their domains.</li><li>Researchers and Academics who wish to utilise Python for complex data analysis.</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>No experience required. Just enrol &amp; start learning.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Scientist: £60,000 - £90,000</li><li>Machine Learning Engineer: £65,000 - £100,000</li><li>Data Analyst: £40,000 - £60,000</li><li>Business Intelligence Analyst: £45,000 - £70,000</li><li>Data Engineer: £50,000 - £80,000</li><li>Big Data Architect: £70,000 - £120,000</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited e-Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPD Accredited Framed (Hardcopy) Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>Enrolment Letter</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>QLS Endorsed Hard Copy Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>Student ID Card</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Start your career in Data Science and earn up to £90,000 per month.
Are you eager to dive into the high-speed world of Data Science powered by Python? In the UK alone, Data Scientist job postings witnessed a dramatic increase of 67% in 2022, emphasising the burgeoning demand for proficient Python programmers. Amid such a dynamic job market, our online course - Data Science with Python, serves as your stepping-stone to a universe of opportunities. Whether you're taking your first step into the realm of Data Science or aiming to augment your existing skills, our program offers peerless support, ensuring you're industry-ready by the time you complete our course.
Our mission is simple - to be your trusted partner every step of the way, from training to employment.
In addition to teaching you the technical skills you need, we will also provide you with career mentoring and support. We will help you build your resume, prepare for interviews, and land your dream job. We also have partnerships with many companies that are hiring Data Scientists, so we can help you get your foot in the door.
If you are not happy with our service, we also offer a 100% money-back guarantee. So what are you waiting for? Enrol in our Data Scientist with Python Training Program today and start your journey to becoming a successful Data Scientist!
If you have any questions, you can contact us. We will be happy to provide you with all the information you need.
Why Choose Us?
So, what sets us apart from other programs? Let's dive into the exceptional benefits you'll experience when you join our Data Scientist with Python:
Personalised Guidance: We believe in the power of individual attention. Our experienced mentors will provide one-on-one counselling sessions tailored to your specific needs. Whether you're a beginner or have some Python experience, we will guide you towards honing your skills and developing a strong foundation in both Data Science and Python.
One-On-One Consultation Sessions with Industry Experts: Gain invaluable insights and guidance from seasoned professionals who have thrived in the Data Science field. Our consultation sessions provide you with insider tips, tricks, and advice, empowering you to navigate the industry with confidence and expertise.
Extensive Job Opportunities: We have established partnerships with numerous companies actively seeking Data Scientists. Through our network, we'll connect you with exclusive job openings that are not easily accessible elsewhere. Our aim is to maximise your employment prospects and provide you with a range of exciting opportunities to choose from.
Interview Preparation: No more stress over unexpected interview questions. We provide you with access to a comprehensive database of potential interview questions curated over years of industry experience. Walk into your interviews confident, well-prepared, and ready to impress.
Money-Back Guarantee: Your satisfaction is our top priority. We are confident in the quality of our training and support, which is why we offer a 100% money-back guarantee. If, for any reason, you're not happy with our services, we'll refund your investment, no questions asked. We believe in the value we provide and want you to feel completely satisfied with your decision to join us.
Continuous Career Support: Our commitment doesn't end when you secure a job. We'll be there for you throughout your career journey, offering continued support and guidance. Whether you need advice on career advancement, assistance with new projects, or simply a friendly ear to share your achievements, we'll be your trusted partner for long-term success.
Here are the courses we will provide once you enrol in the program:
Course 01: Business and Data Analytics for Beginners
Course 02: Quick Data Science Approach from Scratch
Course 03: Learn MySQL from Scratch for Data Science and Analytics
Course 04: SQL for Data Science, Data Analytics and Data Visualization
Course 05: Statistics & Probability for Data Science & Machine Learning
Course 06: R Programming for Data Science
Course 07: Python Data Science with Numpy, Pandas and Matplotlib
Course 08: Complete Python Machine Learning & Data Science Fundamentals
Course 09: 2021 Data Science & Machine Learning with R from A-Z
Course 10: Python Programming from Scratch with My SQL Database
Course 11: Level 2 Python Course
Course 12: Machine Learning for Predictive Maps in Python and Leaflet
Course 13: Python Programming Bible | Networking, GUI, Email, XML, CGI
Course 14: Python for Spatial Analysis in ArcGIS
Course 15: Ultimate Python Training for Beginners
Course 16: PyScript Fundamentals Training
These courses will help you to develop your knowledge and skills to become a successful Cyber Security Expert.
The Data Scientist with Python Program is completed in 9 easy steps:


Step 1: Enrol in the Programme
Begin your exciting journey with us by enrolling in the Data Science with Python Training program. Complete your registration and make a secure online payment. Remember, we offer a 14-day money-back guarantee if you're not completely satisfied.
After you enrol in the Program, you will get lifetime access to 16 premium courses related to Data Science with Python. These courses will teach you the knowledge and skills required to become a successful Data Scientist. Our customer service team will help you and keep in contact with you every step of the way. So you won't have to worry about a thing!
Step 2: Initial One-On-One Counselling Session
Once enrolled, you will be paired with a dedicated career mentor. Schedule your first one-on-one session to discuss your career aspirations, skills, experience, and any areas for potential growth. This conversation will shape your learning and development path.


Step 3 - Certification upon Course Completion
After learning from the courses, you must obtain certificates for each course. There will be exams for every course, and you have to pass them to get your certificate. To pass successfully, you must get 90% marks. Once you pass the exams, you will receive hardcopy certificates. These certificates will prove that you're an expert in the subject.


Step 4: CV Revamping
Our team of professionals will build you a compelling CV and LinkedIn profile. We'll ensure it presents your skills and qualifications effectively and is tailored to the needs and expectations of the Data Science with Python industry. With these powerful tools in hand, you'll be fully prepared to tackle job interviews confidently. 


Step 5: Building Network and Submitting CV
We understand the power of casting a wide net. We'll strategically submit your CV to various platforms and networks, expanding your reach and connecting you with valuable opportunities that align with your career goals. We will also make connections with many high-profile individuals and companies through your LinkedIn profile.


Step 6: Interview Preparation
With your CV ready, we'll move on to interview preparation. Gain exclusive access to our database of potential interview questions. Through simulated interviews with your mentor, you'll practice your responses and receive valuable feedback to further refine your skills.


Step 7: Securing Job Interviews
Leveraging our partnerships with leading companies, we'll secure job interviews for you. We'll ensure you get the opportunity to showcase your skills to potential employers and get the dream job you want.


Step 8: Post-Interview Support
Post-interview, we'll provide a debriefing session to reflect on your performance and identify areas of improvement for future interviews if necessary. Remember, our commitment extends until you land your dream job.


Step 9: Celebrate Your New Job!
Once you've secured your dream job in Data Science with Python, it's time to celebrate! However, our support doesn't end there. We'll provide you with ongoing career advice to ensure you continue to thrive in your new role.
We're excited to accompany you on this journey to success. Enrol today, and let's get started! Your path to a successful career in Data Science with Python begins with us.
 CPD 
100 CPD hours / points  Accredited by CPD Quality Standards
 Who is this course for? 
Beginners with an interest in Data Science and Python programming.
Programmers and Software Engineers looking to transition into Data Science.
Business Analysts and Project Managers aiming to leverage Data Science in their domains.
Researchers and Academics who wish to utilise Python for complex data analysis.
 Requirements 
No experience required. Just enrol & start learning.
 Career path 
Data Scientist: £60,000 - £90,000
Machine Learning Engineer: £65,000 - £100,000
Data Analyst: £40,000 - £60,000
Business Intelligence Analyst: £45,000 - £70,000
Data Engineer: £50,000 - £80,000
Big Data Architect: £70,000 - £120,000
 Certificates 
CPD Accredited e-Certificate
 Digital certificate - Included 
CPD Accredited Framed (Hardcopy) Certificate
 Hard copy certificate - Included 
Enrolment Letter
 Digital certificate - Included 
QLS Endorsed Hard Copy Certificate
 Hard copy certificate - Included 
Student ID Card
 Digital certificate - Included 

Data Scientist Job Ready Program with Career Support & Money Back Guarantee

<p><strong>***Don't Spend More; Spend Smart with This 14 in 1 Data Science Bundle***</strong></p><p><strong>Get Hard Copy + PDF Certificates + Transcript + Student ID Card + e-Learning App as a Gift - Enrol Data Science Bundle Now</strong></p><p>Tired of browsing and searching for the Data Science course you are looking for? Can't find the complete package that fulfils all your needs? Then don't worry, as you have just found the solution. Take a minute and look through this 14-in-1 extensive Data Science bundle that has everything you need to succeed in <strong>Data Science </strong>and other relevant fields!</p><p>After surveying thousands of learners just like you and considering their valuable feedback, this all-in<strong>-</strong>one <strong>Data Science </strong>bundle has been designed by industry experts. We prioritised what learners were looking for in a complete package and developed this in-demand course that will enhance your Data Science skills and prepare you for the competitive job market.</p><p>Furthermore, to help you showcase your expertise in <strong>Data Science, </strong> we have prepared a special gift of 1 hardcopy certificate and 1 PDF certificate for the title course completely free of cost. These certificates will enhance your credibility and encourage possible employers to pick you over the rest.</p><p><strong>This Data Science Bundle Consists of the following Premium courses: </strong></p><ul><li><strong>Course 01: </strong>Data Analysis</li><li><strong>Course 02: </strong>Data Science &amp; Machine Learning with Python</li><li><strong>Course 03: </strong>R Programming for Data Science</li><li><strong>Course 04: </strong>Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li><strong>Course 05: </strong>Complete Google Analytics Course</li><li><strong>Course 06: </strong>Excel Data Analysis for Beginner</li><li><strong>Course 07: </strong>Introduction to Data Analysis</li><li><strong>Course 08: </strong>Data Analysis in Excel Level 3 Course</li><li><strong>Course 09: </strong>Google Data Studio: Data Analytics</li><li><strong>Course 10: </strong>Data Center Training Essentials: General Introduction</li><li><strong>Course 11: </strong>SQL Programming Masterclass</li><li><strong>Course 12: </strong>Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4</li><li><strong>Course 13: </strong>Learn to Use Python for Spatial Analysis in ArcGIS</li><li><strong>Course 14: </strong>GDPR Data Protection Level 5</li></ul><p>So enrol now in this Data science bundle to kick-start/ advance your career!</p><p>Curriculum of <strong>Data Science Bundle</strong></p><p><strong>Data Science with Python</strong></p><ul><li>Introduction to Python Data Science</li><li>Data Cleaning Packages</li><li>Data Visualization packages</li></ul><p><strong>How will I get my Data Science Certificate?</strong></p><p>After successfully completing the <strong>Data Science </strong>course you will be able to order your CPD Accredited Certificates (PDF + Hard Copy) as proof of your achievement.</p><ul><li><strong>PDF Certificate: Free (For The Title Course)</strong></li><li><strong>Hard Copy Certificate: Free (For The Title Course)</strong></li></ul><h2>
        
        <div>CPD</div>
    </h2>

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                                    <div>
                                        145 CPD hours / points
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                                        <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">
                            <p>Anyone from any background can enrol in this <strong>Data Science </strong>bundle including;</p><ul><li>Administrator</li><li>&amp; Data Analyst</li><li>Architect</li><li>Cabler</li><li>Manager</li><li>Centre Manager</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>Our <strong>Data science</strong>course is fully compatible with PC's, Mac's, laptops, tablets and Smartphone devices.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Having this <strong>Data science </strong>expertise will increase the value of your CV and open you up to multiple job sectors. Explore</p><ul><li>Data Science<br /><ul><li>Administrator</li><li>Data Analyst</li><li>Architect</li><li>Cabler</li><li>Manager</li><li>Centre Manager</li></ul></li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            Included
        </p>
            <div> <p>You will get the PDF Certificate for the title course (<strong>Data Science with Python</strong>) absolutely Free! </p> </div>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div> <p>You will get the Hard Copy certificate for the title course (<strong>Data Science with Python</strong>) absolutely Free! Other Hard Copy certificates are available for £10 each. </p><p>Please Note: The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.</p> </div>

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Data Science - CPD Accredited

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			<p>The ability to identify and respond to changing trends is a hallmark of a successful business. Whether those trends are related to customers and sales, or to regulatory and industry standards, businesses are wise to keep track of the variables that can affect the bottom line. In today's business landscape, data comes from numerous sources and in diverse forms.</p>
			<div><i></i> 120 minutes</div>
			<div><i></i>  £50.00 incl. VAT</div>
			
			
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			<p>Want to run this event in-house?  Enquire about running this event in-house </p>
			
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			<p>Interested in attending? Have a suggestion about running this event near you?<br />Register your interest now</p>
			
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		<h3>Description</h3>
		<p></p><p>The ability to identify and respond to changing trends is a hallmark of a successful business. Whether those trends are related to customers and sales, or to regulatory and industry standards, businesses are wise to keep track of the variables that can affect the bottom line. In today's business landscape, data comes from numerous sources and in diverse forms. By leveraging data science concepts and technologies, businesses can mold all of that raw data into information that facilitates decisions to improve and expand the success of the business.</p>
<p></p>
	
		<h3>Modules/Lessons</h3>
		<p></p><h4><span><strong>Lesson 1:</strong> Data Science Fundamentals</span></h4>
<p>Topic A: What is Data Science?<br />Topic B: Types of Data<br />Topic C: The Data Science Lifecycle</p>
<h4><span><strong>Lesson 2:</strong> Data Science Implementation</span></h4>
<p>Topic A: Data Acquisition and Preparation<br />Topic B: Data Modeling and Visualization<br />Topic C: Data Science Roles</p>
<h4><span><strong>Lesson 3:</strong> The Impact of Data Science</span></h4>
<p>Topic A: Benefits of Data Science<br />Topic B: Challenges of Data Science<br />Topic C: Business Use Cases for Data Science</p>
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		<h3>Target Student</h3>
		<p></p><p>This course is designed for business leaders and decision-makers, including C-level executives, project managers, HR leaders, Marketing and Sales leaders, and technical sales consultants, who want to increase their knowledge of and familiarity with concepts surrounding data science. Other individuals who want to know more about basic data science concepts are also candidates for this course.</p>
<p></p>

The ability to identify and respond to changing trends is a hallmark of a successful business. Whether those trends are related to customers and sales, or to regulatory and industry standards, businesses are wise to keep track of the variables that can affect the bottom line. In today's business landscape, data comes from numerous sources and in diverse forms.

Data Science for Business Professionals

<strong>This course will show you how to create a professional and attractive user interface (UI) in Django for data science using the Semantic UI framework.</strong>
        <p>This course will show you how to create a professional and attractive user interface (UI) in Django for data science using the Semantic UI framework. If you don't have a programming background, this course will help you create custom analytic tools in the browser by taking you through the core concepts of pandas, NumPy, Matplotlib, and Seaborn. With a step-by-step introduction to new concepts, this Django course will gradually help you get to grips with the essentials of data science.
The code bundle for this course available at https://github.com/PacktPublishing/Django-with-Data-Science</p>
        <h2>What You Will Learn</h2><p>Understand the integration of Django and Python data science libraries: pandas, Matplotlib, Seaborn, and NumPy<br>Populate the database from CSV files<br>Explore the Semantic UI framework<br>Get to grips with the basics of data science</p>
        <h2>Audience</h2><p>If you are a developer looking to dive into the world of data science with Django, this course is for you.</p>
        <h2>Approach</h2><p>A good resource for beginners with a programming mindset, this course explains each concept in a precise and easy-to-understand way. The instructor takes you through the content systematically to help you make the most of the topics covered.</p>
        <h2>Key Features</h2><p>Work with Django forms and model forms * Create a modal with price statistics * Transform the database and populate rows of the .csv file with sales data</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/django-with-data-science</p>
        <h2>About the Author</h2><h3>Lukasz Makinia</h3><p>Lukasz Makinia is a freelance web developer and a Lean Sigma Six Black Belt. From the beginning of his 8-year professional career, he's related to the field of continuous improvement by providing IT and Lean Management solutions to medium and large companies. He also creates web applications mainly for data gathering and processing using Python/Django, JavaScript, React, and ML. In his free time, Luke writes articles for his blog-PyPlane.

He is associated with 'Django Ninjas'-a web framework for building APIs with Django and Python 3.6+ type hints. They provide online educational courses mainly about Python/Django by the community of web developers across the globe. Their classes are project-based only, which means 'teaching by doing'.</p>
        <h2>Course Outline</h2><h4>1. Django Project</h4><p></p><br><h4>2. Python Data Science Libraries [Optional]</h4><p></p><br><h4>3. Field Types in Django [OPTIONAL]</h4><p></p>

This course will show you how to create a professional and attractive user interface (UI) in Django for data science using the Semantic UI framework.

Django with Data Science

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>This course is intended for information workers and data science professionals who seek to use database reporting and analysis tools such as Microsoft SQL Server Reporting Services, Excel, Power BI, R, SAS and other business intelligence tools, and wish to use TSQL queries to efficiently retrieve data sets from Microsoft SQL Server relational databases for use with these tools.</p>Overview<p>After completing this course, students will be able to: - Identify independent and dependent variables and measurement levels in their own analytical work scenarios. - Identify variables of interest in relational database tables. - Choose a data aggregation level and data set design appropriate for the intended analysis and tool. - Use TSQL SELECT queries to produce ready-to-use data sets for analysis in tools such as PowerBI, SQL Server Reporting Services, Excel, R, SAS, SPSS, and others. - Create stored procedures, views, and functions to modularize data retrieval code.</p><p>This course is about writing TSQL queries for the purpose of database reporting, analysis, and business intelligence.</p></div><div id="course-pills-2"><h3>1 - INTRODUCTION TO TSQL FOR BUSINESS INTELLIGENCE</h3> <ul> <li>Two Approaches to SQL Programming</li> <li>TSQL Data Retrieval in an Analytics / Business Intelligence Environment</li> <li>The Database Engine</li> <li>SQL Server Management Studio and the CarDeal Sample Database</li> <li>Identifying Variables in Tables</li> <li>SQL is a Declarative Language</li> <li>Introduction to the SELECT Query</li> <li>Lab 1: Introduction to TSQL for Business Intelligence</li> </ul> <h3>2 - TURNING TABLE COLUMNS INTO VARIABLES FOR ANALYSIS: SELECT LIST EXPRESSIONS, WHERE, AND ORDER BY</h3> <ul> <li>Turning Columns into Variables for Analysis</li> <li>Column Expressions, Data Types, and Built-in Functions</li> <li>Column aliases</li> <li>Data type conversions</li> <li>Built-in Scalar Functions</li> <li>Table Aliases</li> <li>The WHERE clause</li> <li>ORDER BY</li> <li>Lab 1: Write queries</li> </ul> <h3>3 - COMBINING COLUMNS FROM MULTIPLE TABLES INTO A SINGLE DATASET: THE JOIN OPERATORS</h3> <ul> <li>Primary Keys, Foreign Keys, and Joins</li> <li>Understanding Joins, Part 1: CROSS JOIN and the Full Cartesian Product</li> <li>Understanding Joins, Part 2: The INNER JOIN</li> <li>Understanding Joins, Part 3: The OUTER JOINS</li> <li>Understanding Joins, Part 4: Joining more than two tables</li> <li>Understanding Joins, Part 5: Combining INNER and OUTER JOINs</li> <li>Combining JOIN Operations with WHERE and ORDER BY</li> <li>Lab 1: Write SELECT queries</li> </ul> <h3>4 - CREATING AN APPROPRIATE AGGREGATION LEVEL USING GROUP BY</h3> <ul> <li>Identifying required aggregation level and granularity</li> <li>Aggregate Functions</li> <li>GROUP BY</li> <li>HAVING</li> <li>Order of operations in SELECT queries</li> <li>Lab 1: Write queries</li> </ul> <h3>5 - SUBQUERIES, DERIVED TABLES AND COMMON TABLE EXPRESSIONS</h3> <ul> <li>Non-correlated and correlated subqueries</li> <li>Derived tables</li> <li>Common table expressions</li> <li>Lab 1: Write queries</li> </ul> <h3>6 - ENCAPSULATING DATA RETRIEVAL LOGIC</h3> <ul> <li>Views</li> <li>Table-valued functions</li> <li>Stored procedures</li> <li>Creating objects for read-access users</li> <li>Creating database accounts for analytical client tools</li> <li>Lab 1: Encapsulating Data Retrieval Logic</li> </ul> <h3>7 - GETTING YOUR DATASET TO THE CLIENT</h3> <ul> <li>Connecting to SQL Server and Submitting Queries from Client Tools</li> <li>Connecting and running SELECT queries from:</li> <li>Excel</li> <li>PowerBI</li> <li>RStudio</li> <li>Exporting datasets to files using</li> <li>Results pane from SSMS</li> <li>The bcp utility</li> <li>The Import/Export Wizard</li> <li>Lab 1: Getting Your Dataset to the Client</li> </ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans 55232 Writing Analytical Queries for Business Intelligence training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the 55232 Writing Analytical Queries for Business Intelligence course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

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