Python for AI & Data

Format: Instructor-Led (Online, Synchronous)
Duration: 8 Weeks • 32 Hours

Overview

Python is one of the most in-demand skills across data and AI roles. In this course, learners will use it to solve analytical problems and uncover insights from data—building the foundational programming skills and analytical thinking needed for careers such as Data Analyst, Business Analyst, or Junior Software Engineer. Through hands-on labs, realistic datasets, and a capstone project, learners will gain practical experience with core Python libraries including Pandas, NumPy, SciPy, and Scikit-Learn. They will write clean, reproducible code, create effective visualisations, and experiment with simple machine learning workflows.

Outcomes

Install and configure Python, Jupyter, and core libraries to prepare an analytical workspace
Write and debug clean, structured code to automate data tasks
Ingest, clean, and transform datasets using Pandas and NumPy
Apply statistical and computational techniques to identify and interpret patterns
Create visualisations using Matplotlib and Seaborn to communicate insights clearly
Train and evaluate basic machine learning models using Scikit-Learn
Implement debugging, refactoring, and version-control practices using Git and GitHub
Design and deliver a complete, reproducible data analysis project demonstrating professional analytical standards

Prerequisites

None — there are no formal prerequisites for this course
Comfort with basic computer operations (organising files, navigating browsers, using online tools) will increase speed in learning new concepts
No prior programming experience required

System Requirements

Operating System: Windows 10+, macOS 10.14+, or Linux (Ubuntu 20.04+)
Python: Version 3.9 or higher
Anaconda/Miniconda: Recommended for environment management
Jupyter Notebook: For interactive coding and documentation
Git: For version control
GitHub Account: For repository hosting and submission
Code Editor: VS Code recommended (optional)
RAM: Minimum 8GB recommended
Storage: At least 5GB free space for Python environment and datasets
Internet: Stable connection required for synchronous sessions

Module Repos

Module Name	Associated Lab	Tool(s) Used
Module 1: Introduction to Python and Jupyter Notebooks	Setting Up Your Python Environment	Python 3, Jupyter Notebook, Git, GitHub
Module 2: Core Python Syntax, Variables and Logic	Core Python Syntax, Variables and Logic	Python 3, Jupyter Notebook, Git, GitHub
Module 3: Functions and Data Structures	Creating Reusable Functions	Python 3, Jupyter Notebook, Git, GitHub
Module 4: File Formats and Data I/O	Loading and Processing Data with NumPy	Python 3, Jupyter Notebook, Pandas, CSV, JSON, Excel, Parquet
Module 5: Pandas Fundamentals – Data Wrangling	Cleaning and Transforming Data in Pandas	Python 3, Jupyter Notebook, Pandas, Git, GitHub
Module 6: NumPy for Numerical Computing	Optimising Calculations with NumPy Arrays	Python 3, Jupyter Notebook, NumPy
Module 7: Scientific Computing with SciPy	Statistical Testing with SciPy	Python 3, Jupyter Notebook, SciPy, Pandas
Module 8: APIs and Web Scraping	Retrieving Data via APIs and Web Scraping	Python 3, Jupyter Notebook, Requests, BeautifulSoup, Pandas
Module 9: Data Validation and Quality Assurance	Validating and Reporting Data Quality	Python 3, Jupyter Notebook, Pandas, NumPy, Git, GitHub
Module 10: Exploratory Data Analysis (EDA)	Exploring and Summarising Data	Python 3, Jupyter Notebook, Pandas, Matplotlib
Module 11: Statistical Analysis and Inference	Performing Regression and Correlation Analysis	Python 3, Jupyter Notebook, Pandas, SciPy, StatsModels
Module 12: Data Visualisation with Matplotlib and Seaborn	Visualising Data with Matplotlib and Seaborn	Python 3, Jupyter Notebook, Matplotlib, Seaborn, Pandas
Module 13: Time Series and Forecasting	Analysing and Forecasting Time Series	Python 3, Jupyter Notebook, Pandas, NumPy, Matplotlib
Module 14: Introduction to Machine Learning with Scikit-Learn	Building and Evaluating a Baseline Model	Python 3, Jupyter Notebook, Scikit-Learn, Pandas, NumPy
Module 15: Model Deployment and Automation	Saving and Loading Models for Automation	Python 3, Jupyter Notebook, Scikit-Learn, Pandas, NumPy, Git, GitHub
Module 16: Capstone Project	Applied Python Analytics Project	Python 3, Jupyter Notebook, Pandas, NumPy, Matplotlib, Scikit-Learn, Git, GitHub