This course introduces Python programming for data analysis, providing a practical foundation for working with structured data and developing data-driven insights. You will begin by learning to recognise and understand Python code, explore the differences between spreadsheets and programming languages, and use GitHub Codespaces as your development environment.

As you progress through the course, you will learn how to work with DataFrames and use the Pandas library to organise, manipulate, and analyse tabular data. You will explore techniques for selecting and filtering data using `loc`, `iloc`, and masks, allowing you to work efficiently with specific rows and subsets of data. You will then develop essential data-cleaning skills by identifying and handling different forms of missing data, including `null`, `None`, and `NaN`. Using Pandas functions such as `fillna`, you will learn how to clean data in a repeatable and transparent way. You will also learn how to join multiple tables together to prepare more complete datasets for analysis. The course also introduces data visualisation in Python. You will explore different types of plots, understand when to use them, and learn how to improve visualisations to communicate data clearly and effectively. Finally, you will bring these skills together in an end-to-end data analysis. You will apply Python, Pandas, data manipulation, cleaning, joining, and visualisation techniques, and complete a linear regression to explore relationships within data. By the end of the course, you will be able to use Python and Pandas to work with structured data, select and filter information, clean and combine datasets, create meaningful visualisations, and apply fundamental analytical techniques to practical data problems.