Python for Data Analytics – Beginner to Advanced

Overview

Learn Python from scratch and become a Data Analytics Professional. This course is designed for beginners and advanced learners who want to master Python for Data Analysis, Visualization, and Real-world Projects.

You will learn:

✅ Python Basics
✅ Data Analysis using Pandas
✅ Data Visualization
✅ NumPy for Data Processing
✅ Real-world Data Analytics Projects
✅ Excel + Python Integration
✅ SQL + Python Integration
✅ Data Cleaning & Data Transformation

🎯 Course Outcomes

After completing this course students will:

✔ Understand Python programming
✔ Analyze data using Python
✔ Work with real-world datasets
✔ Create dashboards & reports
✔ Become Data Analyst Ready

Curriculum

23 Sections
154 Lessons
10 Weeks

Expand all sectionsCollapse all sections

Introduction
1
- 1.1
  Intro
Get Started With Python
11
- 2.1
  1. What is Python
- 2.2
  2. Installing Python
- 2.3
  3. Python Interpretore
- 2.4
  4. Code Editor
- 2.5
  5. Your First Python Program
- 2.6
  6. Python Extentions p
- 2.7
  7. Formatting Python Code
- 2.8
  8. Running Python Code
- 2.9
  9. Python Implementation
- 2.10
  10. How Python Code is Executed
- 2.11
  11. Quiz
  10 Minutes0 Questions
Primitive Types
10
- 3.1
  1. Variables
- 3.2
  2. Variable Names
- 3.3
  3. Strings
- 3.4
  4. Escape Sequences
- 3.5
  5. Formatted Strings
- 3.6
  6. String Methods
- 3.7
  7. Numbers
- 3.8
  8. Working With Numbers
- 3.9
  9. Type Conversion
- 3.10
  10. Quiz
  10 Minutes0 Questions
Control Flow
14
- 4.1
  1. Comparison Operators
- 4.2
  2. Conditional Statements
- 4.3
  3. Ternary Operator
- 4.4
  4. Logical Operator
- 4.5
  5. Short-Circiit Evaluation
- 4.6
  6. Chaining Comparison Operator
- 4.7
  7. Quiz
  10 Minutes0 Questions
- 4.8
  8. For Loops
- 4.9
  9. For…Else
- 4.10
  10. Nested Loops
- 4.11
  11. Iterables
- 4.12
  12. While Loop
- 4.13
  13. Infinite Loop
- 4.14
  14. Excercise
Functions
13
- 5.1
  1. Defining Functions
- 5.2
  2. Arguments
- 5.3
  3. Type of Functions
- 5.4
  4. Keyword Arguments
- 5.5
  5. Default Argument
- 5.6
  6. xargs
- 5.7
  7. Xxargs
- 5.8
  8. Scope
- 5.9
  9. Debuggin
- 5.10
  10. VSCode Coding Tricks – Windows
- 5.11
  11. VSCode Coding Tricks – Mac
- 5.12
  12. Exercise
- 5.13
  13. Solution
Data Structures
23
- 6.1
  1. Lists
- 6.2
  2. Accessing Items
- 6.3
  3. List Unpacking
- 6.4
  4. Loop Over Lists
- 6.5
  5. Adding Or Removing Items
- 6.6
  6. Finding Items
- 6.7
  7. Shorting Lists
- 6.8
  8. Lambda Functions
- 6.9
  9. Map Function
- 6.10
  10. Filter Function
- 6.11
  11. List Comprehensions
- 6.12
  12. Zip Function
- 6.13
  13. Stacks
- 6.14
  14. Queuse
- 6.15
  15. Tuples
- 6.16
  16. Swapping Variables
- 6.17
  17. Arrays
- 6.18
  18. Sets
- 6.19
  19. Dictionaries
- 6.20
  20. Dictionary Comprehensions
- 6.21
  21. Generator Expressions
- 6.22
  22. Unpacking Operator
- 6.23
  23. Exercise
Exceptions
7
- 7.1
  1. Exceptions
- 7.2
  2. Handling Exceptions
- 7.3
  3. Handling Differen Exceptions
- 7.4
  4. Cleaning Up
- 7.5
  5. The With Statemenet
- 7.6
  6. Raising Exceptions
- 7.7
  7. Cost Of Raising Exceptions
Classes
22
- 8.1
  1. Classes
- 8.2
  2. Creating Classes
- 8.3
  3. Constructors
- 8.4
  4. Class vs Instance Attributes
- 8.5
  5. Class Vs Instance Methods
- 8.6
  6. Magic Methodes
- 8.7
  7. Comparing Objects
- 8.8
  8. Performing Arithmatic Operations
- 8.9
  9. Making Custom Containers
- 8.10
  10. Private Members
- 8.11
  11. Properties
- 8.12
  12. Inheritance
- 8.13
  13. The Object Class
- 8.14
  14. Method Overriding
- 8.15
  15. Multi-Level Inheritance
- 8.16
  16. Multi Inheritance
- 8.17
  17. A Good Example of Inheritance
- 8.18
  18. Abstract Base Classes
- 8.19
  19. Polymorphism
- 8.20
  20. Duck Typing
- 8.21
  21. Extending Built-in Types
- 8.22
  22. Data Classes
Modules
8
- 9.1
  1. Creating Modules
- 9.2
  2. Compiled Python Files
- 9.3
  3. Module Search Path
- 9.4
  4. Packages
- 9.5
  5. Sub-packages
- 9.6
  6. Intra-package References
- 9.7
  7. The dir Function
- 9.8
  8. Executing Modules as Scripts
Python Introduction
4
- 10.1
  Introduction to Python
- 10.2
  Introduction to Python
- 10.3
  Install Python
- 10.4
  Python First Program
Python Basics
3
- 11.1
  Variables & Data Types
- 11.2
  Operators
- 11.3
  Input & Output
Python Control Statements
3
- 12.1
  If Else
- 12.2
  Loops
- 12.3
  Break & Continue
Python Functions
3
- 13.1
  Functions Basics
- 13.2
  Parameters & Arguments
- 13.3
  Lambda Functions
Python Data Structures
4
- 14.1
  Lists
- 14.2
  Tuples
- 14.3
  Sets
- 14.4
  Dictionary
Python Libraries Introduction
3
- 15.1
  What is Libraries
- 15.2
  Install Libraries
- 15.3
  Import Libraries
NumPy for Data Analytics
4
- 16.1
  NumPy Introduction
- 16.2
  NumPy Arrays
- 16.3
  NumPy Operations
- 16.4
  NumPy Functions
Pandas for Data Analytics
5
- 17.1
  Pandas Introduction
- 17.2
  DataFrames
- 17.3
  Series
- 17.4
  Series
- 17.5
  Read CSV / Excel
Data Cleaning
3
- 18.1
  Missing Data
- 18.2
  Duplicate Data
- 18.3
  Data Formatting
Data Analysis
3
- 19.1
  Data Filtering
- 19.2
  Sorting Data
- 19.3
  GroupBy
Data Visualization
3
- 20.1
  Matplotlib
- 20.2
  Seaborn
- 20.3
  Charts
Excel + Python
3
- 21.1
  Read Excel File
- 21.2
  Export Excel
- 21.3
  Automation
SQL + Python
3
- 22.1
  Connect SQL Database
- 22.2
  Fetch Data
- 22.3
  Analyze SQL Data
Real-World Projects
4
- 23.1
  Sales Data Analysis
- 23.2
  Marketing Data Analysis
- 23.3
  Student Data Analysis
- 23.4
  Business Dashboard

Instructor

Prince Iqubal

Prince Iqubal is a Digital Marketing, Website Design & IT Specialist with over 15 years of experience and Founder of Samantus, empowering learners with practical, industry-ready skills.

₹14,000₹10,000

Course Features

Lectures 154
Quizzes 3
Duration 10 weeks
Skill level All levels
Language English
Students 1809
Assessments Yes

Python for Data Analytics – Beginner to Advanced

Overview

🎯 Course Outcomes

Curriculum

Instructor

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