602 Data Analyst courses in Glasgow delivered Online

Overview In this age of technology, data science and machine learning skills have become highly demanding skill sets. In the UK a skilled data scientist can earn around £62,000 per year. If you are aspiring for a career in the IT industry, secure these skills before you start your journey. The Complete Machine Learning & Data Science Bootcamp 2023 course can help you out. This course will introduce you to the essentials of Python. From the highly informative modules, you will learn about NumPy, Pandas and matplotlib. The course will help you grasp the skills required for using python for data analysis and visualisation. After that, you will receive step-by-step guidance on Python for machine learning. The course will then focus on the concepts of Natural Language Processing.  Upon successful completion of the course, you will receive a certificate of achievement. This certificate will help you elevate your resume. So enrol today! How will I get my certificate? 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. Who is This course for? Anyone with an interest in learning about data science can enrol in this course. It will help aspiring professionals develop the basic skills to build a promising career. Professionals already working in this can take the course to improve their skill sets. Requirements 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.   Career Path 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 Data Analyst Data Scientist Data Manager Business Analyst Course Curriculum 18 sections • 98 lectures • 23:48:00 total length •Welcome & Course Overview6: 00:07:00 •Set-up the Environment for the Course (lecture 1): 00:09:00 •Set-up the Environment for the Course (lecture 2): 00:25:00 •Two other options to setup environment: 00:04:00 •Python data types Part 1: 00:21:00 •Python Data Types Part 2: 00:15:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1): 00:16:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2): 00:20:00 •Python Essentials Exercises Overview: 00:02:00 •Python Essentials Exercises Solutions: 00:22:00 •What is Numpy? A brief introduction and installation instructions.: 00:03:00 •NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes.: 00:28:00 •NumPy Essentials - Indexing, slicing, broadcasting & boolean masking: 00:26:00 •NumPy Essentials - Arithmetic Operations & Universal Functions: 00:07:00 •NumPy Essentials Exercises Overview: 00:02:00 •NumPy Essentials Exercises Solutions: 00:25:00 •What is pandas? A brief introduction and installation instructions.: 00:02:00 •Pandas Introduction: 00:02:00 •Pandas Essentials - Pandas Data Structures - Series: 00:20:00 •Pandas Essentials - Pandas Data Structures - DataFrame: 00:30:00 •Pandas Essentials - Handling Missing Data: 00:12:00 •Pandas Essentials - Data Wrangling - Combining, merging, joining: 00:20:00 •Pandas Essentials - Groupby: 00:10:00 •Pandas Essentials - Useful Methods and Operations: 00:26:00 •Pandas Essentials - Project 1 (Overview) Customer Purchases Data: 00:08:00 •Pandas Essentials - Project 1 (Solutions) Customer Purchases Data: 00:31:00 •Pandas Essentials - Project 2 (Overview) Chicago Payroll Data: 00:04:00 •Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data: 00:18:00 •Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach: 00:13:00 •Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials - Exercises Overview: 00:06:00 •Matplotlib Essentials - Exercises Solutions: 00:21:00 •Seaborn - Introduction & Installation: 00:04:00 •Seaborn - Distribution Plots: 00:25:00 •Seaborn - Categorical Plots (Part 1): 00:21:00 •Seaborn - Categorical Plots (Part 2): 00:16:00 •Seborn-Axis Grids: 00:25:00 •Seaborn - Matrix Plots: 00:13:00 •Seaborn - Regression Plots: 00:11:00 •Seaborn - Controlling Figure Aesthetics: 00:10:00 •Seaborn - Exercises Overview: 00:04:00 •Seaborn - Exercise Solutions: 00:19:00 •Pandas Built-in Data Visualization: 00:34:00 •Pandas Data Visualization Exercises Overview: 00:03:00 •Panda Data Visualization Exercises Solutions: 00:13:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1): 00:19:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2): 00:14:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview): 00:11:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions): 00:17:00 •Project 1 - Oil vs Banks Stock Price during recession (Overview): 00:15:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3): 00:17:00 •Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview): 00:03:00 •Introduction to ML - What, Why and Types..: 00:15:00 •Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff: 00:15:00 •scikit-learn - Linear Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Linear Regression Model Hands-on (Part 2): 00:19:00 •Good to know! How to save and load your trained Machine Learning Model!: 00:01:00 •scikit-learn - Linear Regression Model (Insurance Data Project Overview): 00:08:00 •scikit-learn - Linear Regression Model (Insurance Data Project Solutions): 00:30:00 •Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc.: 00:10:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 2): 00:20:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 3): 00:11:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Overview): 00:05:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Solutions): 00:15:00 •Theory: K Nearest Neighbors, Curse of dimensionality .: 00:08:00 •scikit-learn - K Nearest Neighbors - Hands-on: 00:25:00 •scikt-learn - K Nearest Neighbors (Project Overview): 00:04:00 •scikit-learn - K Nearest Neighbors (Project Solutions): 00:14:00 •Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging.: 00:18:00 •scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1): 00:19:00 •scikit-learn - Decision Tree and Random Forests (Project Overview): 00:05:00 •scikit-learn - Decision Tree and Random Forests (Project Solutions): 00:15:00 •Support Vector Machines (SVMs) - (Theory Lecture): 00:07:00 •scikit-learn - Support Vector Machines - Hands-on (SVMs): 00:30:00 •scikit-learn - Support Vector Machines (Project 1 Overview): 00:07:00 •scikit-learn - Support Vector Machines (Project 1 Solutions): 00:20:00 •scikit-learn - Support Vector Machines (Optional Project 2 - Overview): 00:02:00 •Theory: K Means Clustering, Elbow method.: 00:11:00 •scikit-learn - K Means Clustering - Hands-on: 00:23:00 •scikit-learn - K Means Clustering (Project Overview): 00:07:00 •scikit-learn - K Means Clustering (Project Solutions): 00:22:00 •Theory: Principal Component Analysis (PCA): 00:09:00 •scikit-learn - Principal Component Analysis (PCA) - Hands-on: 00:22:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Overview): 00:02:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Solutions): 00:17:00 •Theory: Recommender Systems their Types and Importance: 00:06:00 •Python for Recommender Systems - Hands-on (Part 1): 00:18:00 •Python for Recommender Systems - - Hands-on (Part 2): 00:19:00 •Natural Language Processing (NLP) - (Theory Lecture): 00:13:00 •NLTK - NLP-Challenges, Data Sources, Data Processing ..: 00:13:00 •NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing: 00:19:00 •NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW.: 00:19:00 •NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes : 00:13:00 •NLTK - NLP - Pipeline feature to assemble several steps for cross-validation: 00:09:00

In a data-driven world, those who can interpret and act on data are indispensable. The Data Science: 20-in-1 Premium Online Courses Bundle is your competitive edge—ideal for roles in analysis, business intelligence, and decision support. Make your CV count in one of the world’s fastest-growing job sectors. Description From finance and healthcare to tech and logistics, employers are looking for data-literate professionals who bring more than just numbers—they bring insights. This bundle proves you're ready. With essential tools, communication strategies, and technical proficiencies bundled into one affordable package, you'll position yourself ahead of the hiring curve. Ideal for aspiring analysts, business intelligence officers, or junior data scientists, this bundle offers wide appeal at incredible value. Don’t let others pass you by while you wait—get certified, stand out, and move forward in a high-paying, future-proof career. FAQ Q: What roles is this bundle useful for? A: Data analyst, business intelligence associate, reporting specialist, operations analyst, junior data scientist. Q: What sectors are looking for these skills? A: Technology, finance, retail, government, education, healthcare, and logistics. Q: Are the certificates official? A: Yes, you’ll receive a certificate for every course completed. Q: Do I need prior experience with coding? A: No. This bundle is suitable for both beginners and those seeking to upskill. Q: Can I learn at my own pace? A: Yes. You get full, flexible access to complete the courses at your convenience.

In a data-driven world, those who can interpret and act on data are indispensable. The Data Science: 20-in-1 Premium Online Courses Bundle is your competitive edge—ideal for roles in analysis, business intelligence, and decision support. Make your CV count in one of the world’s fastest-growing job sectors. Description From finance and healthcare to tech and logistics, employers are looking for data-literate professionals who bring more than just numbers—they bring insights. This bundle proves you're ready. With essential tools, communication strategies, and technical proficiencies bundled into one affordable package, you'll position yourself ahead of the hiring curve. Ideal for aspiring analysts, business intelligence officers, or junior data scientists, this bundle offers wide appeal at incredible value. Don’t let others pass you by while you wait—get certified, stand out, and move forward in a high-paying, future-proof career. FAQ Q: What roles is this bundle useful for? A: Data analyst, business intelligence associate, reporting specialist, operations analyst, junior data scientist. Q: What sectors are looking for these skills? A: Technology, finance, retail, government, education, healthcare, and logistics. Q: Are the certificates official? A: Yes, you’ll receive a certificate for every course completed. Q: Do I need prior experience with coding? A: No. This bundle is suitable for both beginners and those seeking to upskill. Q: Can I learn at my own pace? A: Yes. You get full, flexible access to complete the courses at your convenience.

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Learn Microsoft Office Skills with Admin, Secretarial & PA (Executive PA) essentials from anywhere in the world Microsoft Office Skills with Admin, Secretarial & PA (Executive PA) Course is affordable and simple to understand Microsoft Office Skills with Admin, Secretarial & PA (Executive PA) Course is entirely online, interactive lesson with voiceover audio Lifetime access to the Microsoft Office Skills with Admin, Secretarial & PA (Executive PA) materials Microsoft Office Skills with Admin, Secretarial & PA (Executive PA) comes with 24/7 tutor support. CPD 220 CPD hours / points Accredited by CPD Quality Standards Mastering Microsoft Office: Word, Excel, PowerPoint, and 365 (Promo Videos) 13:54 1: Introduction Video Preview 01:10 2: Introduction Preview 02:58 3: Excel Overview Preview 05:20 4: Start Excel Spreadsheet Preview 04:26 Module 01 - Introduction to Personal Assistant 10:33 5: Introduction to Personal Assistant Preview 10:33 Module 02 - Introduction to Office Administration 07:17 6: Introduction to Office Administration 07:17 Module 03 - Skills of an Effective Administrator 09:19 7: Skills of an Effective Administrator 09:19 Module 04 - Introduction to Reception and Receptionist 14:09 8: Introduction to Reception and Receptionist 14:09 Module 05 - Business Telephone Skills 11:04 9: Business Telephone Skills 11:04 Module 06 - Representing Your Boss and Company 23:02 10: Representing Your Boss and Company 23:02 Module 07 - Business Etiquette 11:26 11: Business Etiquette 11:26 Module 08 - Mail Services and Shipping 17:15 12: Mail Services and Shipping 17:15 Module 09 - Travel Arrangements 14:20 13: Travel Arrangements 14:20 Module 10 - Organising Meeting and Conferences 17:24 14: Organising Meeting and Conferences 17:24 Module 11 - Diary Management and Note taking 14:56 15: Diary Management and Note taking 14:56 Module 12 - Time Management 23:16 16: Time Management 23:16 Module 13 - Record Keeping and Filing Systems 17:30 17: Record Keeping and Filing Systems 17:30 Module 14 - Business Writing Skills 28:36 18: Business Writing Skills 28:36 Module 15 - Organisational Skills 17:06 19: Organisational Skills 17:06 Module 16 - Communication Skills 16:21 20: Communication Skills 16:21 Module 17 - Customer Service 09:54 21: Customer Service 09:54 Module 18 - Effective Planning and Scheduling 33:19 22: Effective Planning and Scheduling 33:19 Module 19 - Invoicing/Petty Cash 18:39 23: Invoicing/Petty Cash 17:39 24: Final Words 01:00 Assessment (Optional) 02:00 25: Assignment on- Advanced Diploma in Admin, Secretarial & PA at QLS Level 7 02:00 Order Your Certificate 02:00 26: Order Your CPD Certificate 01:00 27: Order Your QLS Endorsed Certificate 01:00 Free Course No 01: Legal Secretary 4:00:14 28: Module 01: Understanding the Concept of Paralegal 14:11 29: Module 02: Key Legal Terminology 10:47 30: Module 03: Effective Communication Skills for Paralegals 22:43 31: Module 04: The Legal Workplace & Professionals 28:06 32: Module 05: Law & Legal Systems 22:59 33: Module 06: Contract & Business Law 34:56 34: Module 07: Property Law 11:50 35: Module 08: Wills, Probate, Estate Law 07:59 36: Module 09: Criminal Law 16:32 37: Module 10: Common Paralegal Legal Matters 23:34 38: Module 11: Human Rights 09:56 39: Module 12: Conflict and Conflict Resolution 13:16 40: Module 13: Professional Conduct and Ethics of Paralegals 15:03 41: Module 14: Mandatory Knowledge Fields and Survival Tips for Paralegals 08:22 Free Course No 02: GDPR 1:04:50 42: Module 01: GDPR Basics 10:32 43: Module 02: GDPR Explained 09:51 44: Module 03: Lawful Basis for Preparation 14:13 45: Module 04: Rights and Breaches 14:04 46: Module 05: Responsibilities and Obligations 16:10 Free Course No 03: UK Employment Law 3:23:15 47: Module 01: Basic of Employment Law 16:18 48: Module 02: Legal Recruitment Process 26:45 49: Module 03: Employment Contracts 14:06 50: Module 04: Employee Handbook 13:03 51: Module 05: Disciplinary Procedure 13:56 52: Module 06: National Minimum Wage & National Living Wage 21:58 53: Module 07: Parental Right, Sick Pay & Pension Scheme 31:14 54: Module 08: Discrimination in the Workplace 27:43 55: Module 09: Health & Safety at Work 10:21 56: Module 10: Dismissal, Grievances and Employment Tribunals 20:52 57: Module 11: Workplace Monitoring & Data Protection 06:59 Free Course No 04: Organisational Skills Course for Administrator 43:28 58: Module 01: Remove the Clutter 05:41 59: Module 02: Prioritise 04:16 60: Module 03: Scheduling Your Time 04:21 61: Module 04: To Do Lists 03:35 62: Module 05: Paper and Paperless Storage 04:26 63: Module 06: Organisation in Your Work Area 04:05 64: Module 07: Tools to Fight Procrastination 04:22 65: Module 08: Organizing Your Inbox 04:21 66: Module 09: Avoid the Causes of Disorganization 04:18 67: Module 10: Discipline is the Key to Stay Organized 04:03 Who is this course for? Microsoft Office Skills (Excel, Word, Access) Anyone interested in learning more about the topic is advised to take this Microsoft Office Skills (Excel, Word, Access) with Admin, Secretarial & PA course. This Microsoft Office Skills (Excel, Word, Access) with Admin, Secretarial & PA course is open to everybody. Requirements Microsoft Office Skills (Excel, Word, Access) Anyone interested in learning more about the topic is advised to take this Microsoft Office Skills (Excel, Word, Access) with Admin, Secretarial & PA course. This Microsoft Office Skills (Excel, Word, Access) with Admin, Secretarial & PA course is open to everybody. Our Microsoft Office Skills (Excel, Word, Access) with Admin, Secretarial & PA Course is suitable for anyone interested in: Microsoft Office Specialist (MOS) Microsoft Office Specialist (MOS) Expert Microsoft Office Specialist (MOS) Master Microsoft Specialist certification in Office 365 MS Office User Touch Typing Worker Career path Microsoft Office Skills (Excel, Word, Access) The Microsoft Office Skills (Excel, Word, Access) course teaches you essential skills to help you be more productive in your career. Our Course is suitable for the following: Office Administrator Data Analyst HR Admin Microsoft Office Specialist Receptionist Audio Typist Certificates CPD Accredited Digital Certificate Digital certificate - Included Advanced Diploma in Admin, Secretarial & PA at QLS Level 7 Hard copy certificate - £139 Please note that International students have to pay an additional £10 as a shipment fee.

Precision matters in healthcare—and so does data. The Clinical Coding: 20-in-1 Premium Online Courses Bundle is crafted for those pursuing a future in medical data analysis, healthcare administration, or clinical documentation. Build your job profile with skills employers actively seek. Description As healthcare becomes increasingly digital, the demand for professionals who understand data, systems, and terminology is rising fast. This bundle was created to help you access that wave—whether you're looking at clinical coding, medical auditing, data coordination, or NHS records management. Covering everything from biomedical principles and public health to data handling, documentation, and communication, this comprehensive training package is built to showcase your readiness to work in hospitals, research labs, health tech companies, and clinical outsourcing firms. For those serious about entering the healthcare analytics field—or adding proof of competence to an existing career—this is a golden opportunity. Why wait? Get 20 certificates. Build one powerful profile. FAQ Q: What industries is this bundle relevant to? A: Clinical coding, health data analysis, NHS administration, medical documentation, and health informatics. Q: What job titles could this support? A: Clinical Coder, Medical Records Officer, Health Data Analyst, NHS Admin Assistant, Lab Documentation Officer, or Healthcare Support Clerk. Q: Do I need healthcare experience to start? A: Not at all. The bundle is suitable for newcomers and career-changers alike. Q: Will certificates be provided? A: Yes—each course grants a separate certificate to showcase on your CV. Q: Is the training available after enrolment for life? A: Yes, you’ll have unlimited lifetime access to all included courses.

Welcome to 'Python Programming for Non Programmers Level 5,' a course specially designed for those new to the world of coding. This program starts with a comprehensive introduction to Python, a versatile programming language favored in numerous fields. Progressing to the second unit, participants will familiarize themselves with the initial steps of Python programming, setting a strong foundation for future learning. The course then advances to conditional branching in Python, an essential skill for logical problem-solving in coding. A highlight of this course is the practical project: building the game 'Rock Paper Scissors'. This engaging task not only consolidates learning but also adds a fun element to the process. The curriculum further includes critical topics like string operations, date and time functionalities, and the nuances of file handling in Python. Learners will navigate through the complexities of Python data structures-tuples, lists, and dictionaries-and learn to craft user functions, enhancing their coding capabilities. The course also covers email automation, ingenious import tactics, interfacing with operating systems, and handling exceptions with finesse. Furthermore, learners will get hands-on experience with package installation, scheduling tasks in Python, and managing databases using SQLite. The course wraps up with insights on running Python programs via command prompt and Jupyter Notebook, ensuring learners are well-equipped for real-world applications. Learning Outcomes Acquire foundational knowledge and setup skills in Python programming. Master conditional branching for effective problem-solving in code. Complete a practical coding project to solidify Python skills. Learn essential Python operations, including string handling and file management. Explore and apply advanced Python concepts for real-world applications. Why choose this Python Programming for Non Programmers Level 5 course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Unlock career resources for CV improvement, interview readiness, and job success. Who is this Python Programming for Non Programmers Level 5 course for? Beginners eager to learn Python from scratch. Non-technical professionals desiring to add coding skills to their portfolio. Educators keen to integrate Python into their teaching methodologies. Businesspersons interested in understanding coding fundamentals for tech-based solutions. Enthusiasts exploring programming as a new hobby or career path. Career path Entry-Level Python Programmer: £25,000 - £40,000 Python-Enabled Data Analyst: £28,000 - £45,000 Python Automation Engineer: £30,000 - £50,000 Technical Support Analyst with Python Skills: £22,000 - £35,000 Python Web Developer: £26,000 - £42,000 Quality Assurance Analyst with Python Expertise: £24,000 - £38,000 Prerequisites This Python Programming for Non Programmers Level 5 does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Python Programming for Non Programmers Level 5 was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Unit 01: Introduction to Python Programming Section 01: Course Introduction 00:02:00 Unit 02: Getting Started with Python Section 01: Software Installation 00:02:00 Section 02: Hello World Program 00:06:00 Section 03: Input and Output 00:07:00 Section 04: Calculating Average of 5 Numbers 00:03:00 Unit 03: Conditional Branching with Python Section 01: If Loop In Python 00:06:00 Section 02: Program Using If Else part 1 00:03:00 Section 03: Program Using If Else part 2 00:08:00 Section 04: Program for Calculator 00:02:00 Section 05: Program Using For Loop 00:08:00 Section 06: For Table 00:05:00 Section 07: For loop and Mathematical Operator in Python 00:04:00 Section 08: Factorial of Number Using Python 00:06:00 Section 09: Program Using While 00:05:00 Section 10: While Loop Example 00:07:00 Section 11: Tasks for Practice 00:02:00 Unit 04: Importing external/internal library in python Section 01: Importing Library in Python 00:07:00 Unit 05: Project Rock Paper and Scissors Section 01: Rock Paper and Scissor Game 00:06:00 Unit 06: Strings Operation in Python Section 01: Program Using String part 1 00:05:00 Section 02: Program using String 2 00:06:00 Section 03: Program Using String 3 00:06:00 Section 04: Program Using String part 4 00:03:00 Unit 07: Date and time in Python Section 01: Use of Date and Time part 1 00:05:00 Section 02: Use of Date and Time part 2 00:05:00 Unit 08: File Handling, read and write using Python Section 01: File Handling Part 1 00:08:00 Section 02: File Handling Part 2 00:07:00 Unit 09: Data Storage Structures, Tuple, List and Dictionary Section 01: Tuple in Python Part 1 00:10:00 Section 02: Tuple in Python Part 2 00:07:00 Section 03: Using Lists part 1 00:07:00 Section 04: Using List part 2 00:12:00 Section 05: Using Lists part 3 00:06:00 Section 06: Using Lists part 4 00:08:00 Section 07: Using Lists part 5 00:02:00 Section 08: Use of Dictionary Part 1 00:04:00 Section 09: Use of Dictionary Part 2 00:05:00 Section 10: Use of Dictionary Part 3 00:08:00 Section 11: Use of Dictionary Part 4 00:07:00 Unit 10: Writing user functions in Python Section 01: Function in Python Part 1 00:06:00 Section 02: Function in Python Part 2 00:05:00 Section 03: Function in Python Part 3 00:04:00 Section 04: Function in Python Part 4 00:07:00 Section 05: Function in Python Part 5 00:08:00 Unit 11: Sending mail Section 01: Send Email 00:09:00 Unit 12: Import Tricks in Python Section 01: Import Study part 1 00:07:00 Section 02: Import Study part 2 00:03:00 Unit 13: Import Operating System and Platform Section 01: Importing OS 00:06:00 Section 02: Import Platform 00:05:00 Unit 14: Exceptions handling in python Section 01: Exception in Python part 1 00:11:00 Section 02: Exception in Python part 2 00:07:00 Section 03: Exception in Python part 3 00:05:00 Unit 15: Installing Packages and Scheduling In Python Section 01: Installing Packages using built in package manager 00:08:00 Section 02: Scheduler in Python 00:05:00 Unit 16: Data Base In Python using sqlite Section 01: Data Base 1 00:08:00 Section 02: Data Base 2 00:09:00 Section 03: Data Base 3 00:08:00 Section 04: Data base 4 00:07:00 Section 05: Data Base 5 00:06:00 Unit 17: Running Program from Command Prompt and jupyter Notebook Section 01: IDE_1 00:05:00 Section 02: IDE_2 00:07:00 Unit 18: Conclusion Section 01: Conclusion 00:02:00 Resources Resources - Diploma in Python Programming 00:00:00 Assignment Assignment - Diploma in Python Programming 00:00:00 Recommended Materials Workbook - Diploma in Python Programming 00:00:00

Embark on a comprehensive journey into the world of MySQL with a focus on its applications in Data Science and Analytics. This course is structured to take you from the fundamentals to advanced topics in MySQL Server. Covering SQL basics, data manipulation and definition, control and analytic functions, and database management, you'll acquire the essential skills for harnessing MySQL's power in data-driven decision-making. Learning Outcomes: Establish a strong foundation in SQL and MySQL. Set up and configure SQL Server for efficient data handling. Master SQL's Data Manipulation, Definition, and Control Language. Create and optimize SQL queries for data analysis. Perform advanced data analytics using SQL. Understand the power of GROUP BY and JOIN statements. Implement data constraints and views for data integrity and security. Develop proficiency in stored procedures, data import/export, and database backup/restore. Why buy this Learn MySQL from Scratch for Data Science and Analytics?  Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Learn MySQL from Scratch for Data Science and Analytics you will be able to take the MCQ test that will assess your knowledge. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This Learn MySQL from Scratch for Data Science and Analytics course is ideal for Aspiring Data Scientists and Analysts. Database Administrators and Developers. Students and professionals seeking to enter the field of Data Science. Anyone looking to enhance their SQL and MySQL skills for data-related roles. Prerequisites This Learn MySQL from Scratch for Data Science and Analytics was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path Data Analyst: £25,000 - £50,000 per year. Database Administrator: £30,000 - £60,000 per year. SQL Developer: £30,000 - £60,000 per year. Data Scientist: £40,000 - £80,000 per year. Business Intelligence Analyst: £35,000 - £65,000 per year. Course Curriculum Learn MySQL from Scratch for Data Science and Analytics Section 01: Getting Started Introduction 00:02:00 How to get course requirements 00:01:00 Getting started on Windows, Linux or Mac 00:01:00 How to ask great questions 00:01:00 FAQ's 00:01:00 What is Source Code? 00:09:00 Section 02: SQL Server setting up Section Introduction 00:01:00 MySQL Server Installation 00:14:00 Connect MySQL Server Instance 00:06:00 MySQL Workbench overview 00:11:00 Download and Restore Sample Database 00:08:00 Section 03: SQL Database basics Section Introduction 00:01:00 Overview of Databases 00:09:00 Creating Database 00:07:00 SQL Data Types 00:04:00 Column Data Types on Workbench 00:07:00 Creating Table 00:04:00 Overview of Primary and Foreign Key 00:03:00 Primary Key 00:06:00 Foreign Key 00:12:00 Creating Temporary tables 00:12:00 EER - Enhanced Entity Relationship Diagrams 00:04:00 Section 04: SQL DML (Data Manipulation Language) Section Introduction 00:01:00 Insert statement 00:07:00 Update statement 00:06:00 Delete statement 00:03:00 Section 05: SQL DDL (Data Definition Language) Section Introduction 00:01:00 CREATE table statement 00:08:00 DROP statement 00:03:00 ALTER statement 00:05:00 TRUNCATE statement 00:03:00 COMMENT in query 00:02:00 RENAME table 00:03:00 Section 06: SQL DCL (Data Control Language) Create Database user 00:03:00 GRANT permissions 00:06:00 REVOKE permissions 00:04:00 Section 07: SQL Statement Basic Section Introduction 00:01:00 SQL Statement basic 00:03:00 SELECT Statement 00:03:00 SELECT DISTINCT 00:02:00 SELECT with column headings 00:01:00 Column AS statement 00:02:00 DASHBOARD Analytics 00:06:00 Section 08: Filtering Data rows SELECT WHERE Clause - theory 00:03:00 SELECT WHERE Clause - practical 00:06:00 Section 09: Aggregate functions for Data Analysis Sum() 00:06:00 Min()-Max() 00:03:00 Section 10: SQL Data Analyticstatements Order By statement 00:05:00 SELECT TOP 3 records 00:02:00 BETWEEN command 00:06:00 IN operator 00:03:00 Search Data usingLIKE cards 00:05:00 Section 11: SQL Group by statement Section Introduction 00:01:00 Group by - theory 00:04:00 Data Analytics with Group By 00:04:00 HAVING statement 00:03:00 Section 12: JOINS Overview of Joins 00:02:00 What are Joins 00:02:00 Inner join 00:07:00 Left outer join 00:02:00 Right outer join 00:02:00 Union 00:03:00 CERTESIAN Product or Cross Join 00:03:00 Query Exercise 00:01:00 Solution for Query Exercise 00:01:00 Section 13: SQL Constraints Section introduction 00:01:00 Check constraint 00:09:00 NOT NULL constraint 00:03:00 UNIQUE constraint 00:06:00 Section 14: Views Creating Views 00:03:00 Data Analytic Views from multiple tables 00:03:00 Section 15: Advanced SQL Functions Section Introduction 00:01:00 Timestamp 00:03:00 Extract from timestamp 00:03:00 Mathematical scalar functions 00:03:00 String functions3 00:07:00 Advanced functions 00:04:00 Sub Queries 00:03:00 SELECT with calculations 00:05:00 Section 16: SQL Stored procedures Create stored procedure 00:06:00 Stored procedure with parameter 00:03:00 Drop Procedure 00:01:00 Section 17: Import & Export data Section Introduction 00:01:00 Import .csv file 00:04:00 Export Data to .csv file 00:02:00 Section 18: Backup and Restore Database Section Introduction 00:01:00 Creating Database backup 00:02:00 Restoring Database backup 00:02:00

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Overview 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. 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. After completing the course you will receive a certificate of achievement. This certificate will help you create an impressive resume. So join today! How will I get my certificate? 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. Who is This course for? 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. Requirements 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. Career Path 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 Data Analyst Data Scientist Data Manager Business Analyst And much more! Course Curriculum 90 sections • 90 lectures • 10:19:00 total length •Course Overview & Table of Contents: 00:09:00 •Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types: 00:05:00 •Introduction to Machine Learning - Part 2 - Classifications and Applications: 00:06:00 •System and Environment preparation - Part 1: 00:04:00 •System and Environment preparation - Part 2: 00:06:00 •Learn Basics of python - Assignment 1: 00:10:00 •Learn Basics of python - Assignment 2: 00:09:00 •Learn Basics of python - Functions: 00:04:00 •Learn Basics of python - Data Structures: 00:12:00 •Learn Basics of NumPy - NumPy Array: 00:06:00 •Learn Basics of NumPy - NumPy Data: 00:08:00 •Learn Basics of NumPy - NumPy Arithmetic: 00:04:00 •Learn Basics of Matplotlib: 00:07:00 •Learn Basics of Pandas - Part 1: 00:06:00 •Learn Basics of Pandas - Part 2: 00:07:00 •Understanding the CSV data file: 00:09:00 •Load and Read CSV data file using Python Standard Library: 00:09:00 •Load and Read CSV data file using NumPy: 00:04:00 •Load and Read CSV data file using Pandas: 00:05:00 •Dataset Summary - Peek, Dimensions and Data Types: 00:09:00 •Dataset Summary - Class Distribution and Data Summary: 00:09:00 •Dataset Summary - Explaining Correlation: 00:11:00 •Dataset Summary - Explaining Skewness - Gaussian and Normal Curve: 00:07:00 •Dataset Visualization - Using Histograms: 00:07:00 •Dataset Visualization - Using Density Plots: 00:06:00 •Dataset Visualization - Box and Whisker Plots: 00:05:00 •Multivariate Dataset Visualization - Correlation Plots: 00:08:00 •Multivariate Dataset Visualization - Scatter Plots: 00:05:00 •Data Preparation (Pre-Processing) - Introduction: 00:09:00 •Data Preparation - Re-scaling Data - Part 1: 00:09:00 •Data Preparation - Re-scaling Data - Part 2: 00:09:00 •Data Preparation - Standardizing Data - Part 1: 00:07:00 •Data Preparation - Standardizing Data - Part 2: 00:04:00 •Data Preparation - Normalizing Data: 00:08:00 •Data Preparation - Binarizing Data: 00:06:00 •Feature Selection - Introduction: 00:07:00 •Feature Selection - Uni-variate Part 1 - Chi-Squared Test: 00:09:00 •Feature Selection - Uni-variate Part 2 - Chi-Squared Test: 00:10:00 •Feature Selection - Recursive Feature Elimination: 00:11:00 •Feature Selection - Principal Component Analysis (PCA): 00:09:00 •Feature Selection - Feature Importance: 00:06:00 •Refresher Session - The Mechanism of Re-sampling, Training and Testing: 00:12:00 •Algorithm Evaluation Techniques - Introduction: 00:07:00 •Algorithm Evaluation Techniques - Train and Test Set: 00:11:00 •Algorithm Evaluation Techniques - K-Fold Cross Validation: 00:09:00 •Algorithm Evaluation Techniques - Leave One Out Cross Validation: 00:05:00 •Algorithm Evaluation Techniques - Repeated Random Test-Train Splits: 00:07:00 •Algorithm Evaluation Metrics - Introduction: 00:09:00 •Algorithm Evaluation Metrics - Classification Accuracy: 00:08:00 •Algorithm Evaluation Metrics - Log Loss: 00:03:00 •Algorithm Evaluation Metrics - Area Under ROC Curve: 00:06:00 •Algorithm Evaluation Metrics - Confusion Matrix: 00:10:00 •Algorithm Evaluation Metrics - Classification Report: 00:04:00 •Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction: 00:06:00 •Algorithm Evaluation Metrics - Mean Absolute Error: 00:07:00 •Algorithm Evaluation Metrics - Mean Square Error: 00:03:00 •Algorithm Evaluation Metrics - R Squared: 00:04:00 •Classification Algorithm Spot Check - Logistic Regression: 00:12:00 •Classification Algorithm Spot Check - Linear Discriminant Analysis: 00:04:00 •Classification Algorithm Spot Check - K-Nearest Neighbors: 00:05:00 •Classification Algorithm Spot Check - Naive Bayes: 00:04:00 •Classification Algorithm Spot Check - CART: 00:04:00 •Classification Algorithm Spot Check - Support Vector Machines: 00:05:00 •Regression Algorithm Spot Check - Linear Regression: 00:08:00 •Regression Algorithm Spot Check - Ridge Regression: 00:03:00 •Regression Algorithm Spot Check - Lasso Linear Regression: 00:03:00 •Regression Algorithm Spot Check - Elastic Net Regression: 00:02:00 •Regression Algorithm Spot Check - K-Nearest Neighbors: 00:06:00 •Regression Algorithm Spot Check - CART: 00:04:00 •Regression Algorithm Spot Check - Support Vector Machines (SVM): 00:04:00 •Compare Algorithms - Part 1 : Choosing the best Machine Learning Model: 00:09:00 •Compare Algorithms - Part 2 : Choosing the best Machine Learning Model: 00:05:00 •Pipelines : Data Preparation and Data Modelling: 00:11:00 •Pipelines : Feature Selection and Data Modelling: 00:10:00 •Performance Improvement: Ensembles - Voting: 00:07:00 •Performance Improvement: Ensembles - Bagging: 00:08:00 •Performance Improvement: Ensembles - Boosting: 00:05:00 •Performance Improvement: Parameter Tuning using Grid Search: 00:08:00 •Performance Improvement: Parameter Tuning using Random Search: 00:06:00 •Export, Save and Load Machine Learning Models : Pickle: 00:10:00 •Export, Save and Load Machine Learning Models : Joblib: 00:06:00 •Finalizing a Model - Introduction and Steps: 00:07:00 •Finalizing a Classification Model - The Pima Indian Diabetes Dataset: 00:07:00 •Quick Session: Imbalanced Data Set - Issue Overview and Steps: 00:09:00 •Iris Dataset : Finalizing Multi-Class Dataset: 00:09:00 •Finalizing a Regression Model - The Boston Housing Price Dataset: 00:08:00 •Real-time Predictions: Using the Pima Indian Diabetes Classification Model: 00:07:00 •Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset: 00:03:00 •Real-time Predictions: Using the Boston Housing Regression Model: 00:08:00 •Resources - Data Science & Machine Learning with Python: 00:00:00

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