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Computer Science
Academy Africa

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WORKSHOP TRACKS 

Python Fundamentals (Beginner Track)

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Overview 
This track introduces fundamental programming skills for complete beginners. Participants develop computational thinking, learn to write Python code, and solve problems using logical reasoning. By the end, participants will be able to independently build write Python programs and will have a strong foundation for more advanced tracks such as Data Science, Machine Learning, and Software Engineering. No prior programming experience required.

What you'll learn           

  • Python syntax and basic programming concepts

  • Variables, data types, and expressions

  • Conditional logic (if/else statements)

  • Loops and iteration

  • Functions and modular programming

  • Data structures (string, lists, dictionaries)

  • Problem-solving techniques

  • Reading and writing into files

  • Introduction to Python libraries (Numpy and Matplotlib)

       How You’ll Learn

  • Live coding sessions with guided problem solving

  • Hands-on exercises to practise and apply concepts

  • Support from instructors and tutors throughout the workshop

Weekly Structure

Week 1–2: Participants attend live instruction sessions combined with guided coding exercises, and support from instructors and tutors throughout. The focus is on practicing each concept with problems and building programming confidence.

Week 3: Participants work in groups on a programming project, applying what they have learned to a practical problem. The track concludes with a presentation where they demonstrate their code, explain their approach, and answer questions.

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Example Projects

  • Simple calculator application

  • Basic student grading system

  • Personal budget tracker

Learning Outcomes

  • Understand core programming concepts

  • Write functional Python programs

  • Solve basic computational problems

Example Projects

  • Simple calculator application

  • Basic student grading system

  • Personal budget tracker
     

Learning Outcomes

  • Understand core programming concepts

  • Write functional Python programs

  • Solve basic computational problems

Python for Software Engineering

Overview

This track focuses on software development principles and writing maintainable code. Participants move beyond basics into structured programming, object-oriented design, and software engineering practices used in real-world development.

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What you'll learn 

  • Git basics and branching in Git

  • Functional programming 

  • Object-oriented programming

  • Git collaboration features (pull requests, code reviews, forking)

  • Software engineering principles

  • Data structures and algorithms fundamentals

  • Modular and reusable code design

  • Introduction to testing and debugging

  • Modularizing Python

  • Python error handling 

  • Testing in Python

  • CI with GitHub

  • Using WebAPIs in Python

  • Advanced Python topics

  • Python Software Architectures (Flask, FastAPIs)

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       How You’ll Learn

  • Hands-on coding workshops

  • Code reviews and feedback sessions

  • Team-based tasks

 

Weekly Structure

Week 1–2: Participants work through structured programming tasks during live sessions, applying concepts to progressively more complex problems with instructor and tutor support.

Week 3: Participants design and build a complete software project in teams, making decisions about structure and implementation. They present their system, explain how it works, and justify their design choices.

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Example Projects

  • Library management system

  • Task management application

  • Event booking system
     

Learning Outcomes

  • Understand core software engineering practices for organising and writing code

  • Apply object-oriented programming to design and develop programs

  • Build structured Python applications using classes, functions, and modules

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Python for Data Science & 
Machine Learning 

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Overview
This combined track teaches how to work with real-world data and build machine learning models. Participants learn how to clean, analyse, visualise data, and build predictive models.

What you'll learn 

  • Data manipulation with Pandas

  • Numerical computing with NumPy

  • Data cleaning and preprocessing

  • Data visualisation (Matplotlib)

  • Exploratory data analysis (EDA)

  • Supervised learning (regression, classification)

  • Unsupervised learning (clustering)

  • Model training and evaluation

  • Metrics (accuracy, precision, recall)

  • Feature selection and preprocessing

  • Introduction to basic statistics for data analysis

  • Introduction to model bias and overfitting

  • Practical use of Scikit-learn

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       How You’ll Learn

  • Exercises using real datasets

  • Step-by-step guided instructions

  • ​Hands-on model building

  • ​Data analysis and interactive visualisation tasks


​Weekly Structure

  • Week 1–2: Participants work with real-world datasets, focusing on data handling, visualisation, and applying core machine learning methods, with guidance from instructors and tutors.

  • Week 3: Participants analyse a real-world dataset and build a predictive model in groups. They evaluate model performance and present their findings, explaining both their approach and results.
     

Example Projects

  • Education or health data analysis

  • Environmental data trends

  • Housing price prediction
     

Learning Outcomes

  • Apply machine learning methods to solve real-world problems using data

  • Build and evaluate models using appropriate metrics

  • Communicate results clearly through visualisations and reports.

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