Python
Programming
From variables and loops to NumPy, Pandas, Matplotlib, machine learning, algorithms and databases — 45 lessons taking you from absolute zero to data science professional.
From zero to data science.
Python is the world's most beginner-friendly programming language — and also the most powerful for data science, automation and machine learning. This single course takes you from your first variable all the way to building ML models and querying databases.
Designed by Babatunde Awoyemi, Techbase Consultant Services, Ibadan. 45 lessons, 7 phases, completely free.
- Variables, data types, strings, lists, tuples, sets and dicts
- Functions, OOP, decorators, lambdas and generators
- File handling, error handling, modules and packages
- NumPy arrays, Pandas DataFrames and data cleaning
- Matplotlib and Seaborn for data visualisation
- Machine learning with scikit-learn
- Data structures, algorithms, MySQL and MongoDB
45 Lessons · 7 Phases
Phase 1 — Python Basics
01–06- 01 Introduction to Python: What It Is, How to Set It Up, and How to Use It
- 02 Python Comments and Variables
- 03 Python Data Types, Numbers, Casting, and Booleans
- 04 Python Strings: From Basics to Formatting Mastery
- 05 Python String Methods, Operators, Arithmetic & Assignment Operators
- 06 Python Operators: Comparison, Logical, Identity, Membership, Bitwise & Precedence
Phase 2 — Data Structures & Conditions
07–14- 07 Python Lists: Create, Access, Change, Add & Remove
- 08 Python Lists Advanced: Looping, Comprehensions, Sorting, Copying & Methods
- 09 Python Tuples
- 10 Python Sets — The Collection That Hates Duplicates
- 11 Python Dictionaries — From Zero to Real-World Mastery
- 12 Python Dictionary Methods & If/Elif/Else Conditions
- 13 Advanced Conditionals — Shorthand If, Nested If, Match
- 14 Python While Loops, For Loops, and the range() Function
Phase 3 — Functions, OOP & Files
15–23- 15 Python Functions, Arguments, *args / **kwargs, and Scope
- 16 Python Decorators, Lambda Functions, Recursion, and Generators
- 17 Iterators, Modules, Dates, and Math in Python
- 18 JSON, RegEx, PIP, Error Handling, and String Formatting in Python
- 19 Python None, User Input, Virtual Environments, and Arrays
- 20 Python OOP: Classes, Objects, __init__, and self
- 21 Python OOP — Class Properties, Methods, Inheritance & Polymorphism
- 22 Python Encapsulation, Inner Classes, and File Handling
- 23 Python File Write, Create, and Delete
Phase 4 — NumPy & Data Science
24–31- 24 Introduction to NumPy: Arrays, Indexing, and Slicing
- 25 NumPy Deep Dive: Data Types, Shape, Reshape, Iterating, and Joining
- 26 NumPy Array Operations: Splitting, Searching, Sorting, Filtering & Random
- 27 NumPy Random Distributions, Permutations, Seaborn & Universal Functions
- 28 NumPy ufuncs — Arithmetic, Rounding, Logs & Summations
- 29 Introduction to Pandas — Data Analysis with Python
- 30 Pandas — Analyzing DataFrames and Cleaning Data
- 31 Pandas Correlations, Plotting & Introduction to Matplotlib
Phase 5 — Visualisation & Machine Learning
32–35Phase 6 — DSA & Algorithms
36–39Phase 7 — Databases & Reference
40–45- 40 Python and MySQL — Databases, Tables, Drop Table & Inserting Data
- 41 Python MySQL — Querying, Filtering, Sorting, Modifying & Joining Data
- 42 Python and MongoDB — Databases, Collections & Finding Data
- 43 Python MongoDB — Querying, Sorting, Updating & Deleting Documents
- 44 Python Built-in Reference: Functions, String, List, Dict, Tuple & Set Methods
- 45 Python How-Tos, Examples, Exercises, Study Plan & Interview Prep
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Learn at your own pace.
No Prerequisites
Python is the ideal first language. Start at lesson 01 with zero coding experience.
Run Every Example
Open a terminal and type every code snippet as you read — Python rewards practice.
Install Packages
When you reach NumPy (lesson 24), the lessons will guide you through pip install.
Complete Lesson 45
The final lesson is a full reference, interview prep guide and study plan.