Overview

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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.

Syllabus

Transition
- This topic, you will start to learn to recognise Python code and the differences between spreadsheets and programming languages. Before you start, make sure you have signed up to GitHub Codespaces.
Data Frames
- This topic, you will learn about Data Frames, which provide a structured and intuitive way to handle, manipulate, and analyse data. They also integrate with libraries like Pandas that allow for efficiency when working with data. They are extremely versatile and support a range of different tasks.
Data wrangling
- This topic we look at two new pandas features: 'loc', 'iloc', and masks. 'loc' and 'iloc' allow us to look row-first instead of column-first, which is actually the more natural way to operate with tabular data. Masks will allow us to select a set of rows we are interested in based on values in other columns.
Data manipulation
- This topic you will learn about the different types of empty data you will encounter in a notebook, how to clean data in a repeatable and transparent way, and how to join multiple tables together. You will learn the difference between 'null', 'None', and 'NaN', use 'fillna' and its friends. You will also start on the joining journey.
Visualisations
- This topic you will learn about using plots to visualise your data. You will learn about the different kinds of plots, their uses, and how to improve how plots look. There are many ways to achieve the same outcome, but in this microcredential we look at one method. While there are many ways to generate plots, in this microcredential we will learn the simplest and most general tool which you can transition to very complex analysis as your skills grow.
Exploration
- This topic is about consolidating everything you know and putting it together. You will apply your learning and complete a linear regression and an end-to-end visualisation.