Python Programming for Data & AI Fundamentals

Duration: 2 days

Gain foundational Python programming skills with our concise course. You’ll learn essential concepts like syntax, data types, control flow, functions, and object-oriented programming through interactive lessons and hands-on exercises. Start building your own programs effectively.

These fundamentals also provide the building blocks for more advanced applications in artificial intelligence, Answer Engine Optimisation (AEO), and Generative Engine Optimisation (GEO), where Python remains the core language for AI development and optimisation workflows.

What you will learn:

Code basic programs to solve problems and automate tasks.
Understand key concepts like variables, data types, control flow, and functions.
Apply Python across data science, web development, and automation.
Enhance problem-solving and analytical skills through practical exercises.
Establish a strong foundation for advanced Python learning.

Programme Outline

Module 1: Python Basics & Environment Setup

Topics: Python syntax, variables, data types, Jupyter Notebook interface.
Use Case: Writing a basic calculator, generating automated greetings, and formatting output.
Hands-On: Code-along session using JupyterLab or Google Colab.

Module 2: Control Flow and Loops

Topics: `if` statements, `for` and `while` loops, and logical conditions.
Use Case: Automate a simple checklist validator or conditional data filter.
Hands-On: Mini challenge – Write a script to categorise customer age groups based on input data.

Module 3: Data Structures in Python

Topics: Lists, dictionaries, sets, tuples, list comprehension.
Use Case: Build a product catalog and query system using dictionaries and lists.
Hands-On: Create a small inventory lookup tool using nested dictionaries.

Module 4: Working with Files and CSV Data

Topics: File reading/writing, `csv` module, intro to `pandas`.
Use Case: Load and clean a sample dataset (e.g., sales or attendance records).
Hands-On: Load a CSV file, clean missing values, and calculate summary stats with `pandas`.

Module 5: Visualising Data with Python

Topics: Plotting with `matplotlib` and `pandas`.
Use Case: Show sales trends over time, or compare department productivity.
Hands-On: Generate a line chart and bar graph using sample data.

Training Methodology

This workshop blends interactive lectures, live code walkthroughs, and structured hands-on labs using browser-based tools such as Google Colab or Jupyter Notebooks—no installation required. Peer discussions and Q&A are integrated into each module, and participants will consolidate their learning through a final mini-project, such as creating an automated data report script from CSV to visualisation.