Free Course · Beginner → Advanced · Ibadan, Nigeria

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.

45Lessons

7Phases

FreeAlways

Start Lesson 01 → View All 45 Lessons

analysis.py

# Lesson 29 — Pandas Data Analysis

import pandas as pd

import matplotlib.pyplot as plt

df = pd.read_csv('scores.csv')

# Clean and analyse

df.dropna(inplace=True)

avg = df['score'].mean()

print(f"Average: {avg:.2f}")

df.plot(kind='bar')

plt.show()

About This Course

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

tools_needed.txt

Python 3.x

The language runtime

VS Code

Python extension included

Jupyter Notebooks

For data science phases

No prerequisites

This is a standalone course

Start at lesson 01

pip packages

numpy · pandas · matplotlib · sklearn

Covered in lessons

Course Content

45 Lessons · 7 Phases

Phase 1 — Python Basics (01–06)

01Introduction to Python: What It Is, How to Set It Up, and How to Use It 02Python Comments and Variables 03Python Data Types, Numbers, Casting, and Booleans 04Python Strings: From Basics to Formatting Mastery 05Python String Methods, Operators, Arithmetic & Assignment Operators 06Python Operators: Comparison, Logical, Identity, Membership, Bitwise & Precedence

Phase 2 — Data Structures & Conditions (07–14)

07Python Lists: Create, Access, Change, Add & Remove 08Python Lists Advanced: Looping, Comprehensions, Sorting, Copying & Methods 09Python Tuples 10Python Sets — The Collection That Hates Duplicates 11Python Dictionaries — From Zero to Real-World Mastery 12Python Dictionary Methods & If/Elif/Else Conditions 13Advanced Conditionals — Shorthand If, Nested If, Match 14Python While Loops, For Loops, and the range() Function

Phase 3 — Functions, OOP & Files (15–23)

15Python Functions, Arguments, *args / **kwargs, and Scope 16Python Decorators, Lambda Functions, Recursion, and Generators 17Iterators, Modules, Dates, and Math in Python 18JSON, RegEx, PIP, Error Handling, and String Formatting in Python 19Python None, User Input, Virtual Environments, and Arrays 20Python OOP: Classes, Objects, __init__, and self 21Python OOP — Class Properties, Methods, Inheritance & Polymorphism 22Python Encapsulation, Inner Classes, and File Handling 23Python File Write, Create, and Delete

Phase 4 — NumPy & Data Science (24–31)

24Introduction to NumPy: Arrays, Indexing, and Slicing 25NumPy Deep Dive: Data Types, Shape, Reshape, Iterating, and Joining 26NumPy Array Operations: Splitting, Searching, Sorting, Filtering & Random 27NumPy Random Distributions, Permutations, Seaborn & Universal Functions 28NumPy ufuncs — Arithmetic, Rounding, Logs & Summations 29Introduction to Pandas — Data Analysis with Python 30Pandas — Analyzing DataFrames and Cleaning Data 31Pandas Correlations, Plotting & Introduction to Matplotlib

Phase 5 — Visualisation & Machine Learning (32–35)

32Matplotlib: Markers, Lines, Labels, Grids, Subplots, Scatter & Bar Charts 33Matplotlib Histograms, Pie Charts & Machine Learning Statistics 34Percentiles, Distributions, Normal Distribution & Linear Regression 35Python Machine Learning — Regression, Scaling, Train/Test & Decision Trees

Phase 6 — DSA & Algorithms (36–39)

36Python DSA — Lists, Searching & Bubble Sort 37Sorting Algorithms: Selection, Insertion, Quicksort, Counting & Radix Sort 38Merge Sort, Stacks, Queues, Linked Lists & Hash Tables 39Trees, Binary Trees, Binary Search Trees, AVL Trees, and Graphs

Phase 7 — Databases & Reference (40–45)

40Python and MySQL — Databases, Tables, Drop Table & Inserting Data 41Python MySQL — Querying, Filtering, Sorting, Modifying & Joining Data 42Python and MongoDB — Databases, Collections & Finding Data 43Python MongoDB — Querying, Sorting, Updating & Deleting Documents 44Python Built-in Reference: Functions, String, List, Dict, Tuple & Set Methods 45Python How-Tos, Examples, Exercises, Study Plan & Interview Prep

How It Works

Learn at your own pace.

01

No Prerequisites

Python is the ideal first language. Start at lesson 01 with zero coding experience.

02

Run Every Example

Open a terminal and type every code snippet as you read — Python rewards practice.

03

Install Packages

When you reach NumPy (lesson 24), the lessons will guide you through pip install.

04

Complete Lesson 45

The final lesson is a full reference, interview prep guide and study plan.