Introduction to data visualisation

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General Information

This course is designed as a companion course to the Introduction to Data Visualisation theory courses provided by the Analysis Function, which adheres to the most up to date AF guidelines. It introduces a range of Data Visualisation techniques and practices within the programme language Python, utilising the well known visualisation packages matplotlib and seaborn. Throughout, adherence to good practice guidelines will be followed and enforced, as well as strategies to reduce code repetition by setting elements of the design of visualisations to the global environment (so that they are reusable).

Course Materials

The course materials come in several formats:

  • HTML pages such as the one you are reading now

  • Data we will use during the course. It’s highly recommended you create a project with a ‘data’ folder and download all the required datasets before starting the course

You can also navigate to the course Github Repository and clone or fork the website structure for yourself. If you are new to programming and version control, we recommend you remain on the website to gain the best experience.

Learning Outcomes

  • To evaluate the capabilities of different visualisation tools and techniques to identify the most appropriate plot for the message you are aiming to communicate.
  • To apply visualisation techniques to produce a variety of plots that assist with the exploration of datasets.
  • To create static plots ready for publication that follow good practice and accessibility guidelines.
  • To implement clean code principles that reduce repetitions by setting design elements as variables.
  • To examine and critically evaluate plots to draw out meaningful insight from data.

Software Requirements/ Packages

  • Python (Version 3.7 or higher)

  • Optional - Anaconda (it is a Python distribution that simplifies package management and deployment. It comes pre-installed with most of the necessary data analysis libraries and tools.)

The main packages we will be using for this course are:

  • pandas – Version 1.0.5

  • numpy - Version 1.18.5

  • matplotlib – Version 3.2.2

  • seaborn - Version 0.10.1

ENSURE THAT YOU ARE USING THE SAME VERSION OF THE PACKAGE TO AVOID POTENTIAL ERRORS CAUSED BY VERSION DISCREPANCIES!
Use the .__version__ attribute to check your version.

Course Overview

Chapter 1 - Introduction to data visualisation

This chapter introduces key principles of effective data visualisation, provides best practices, and share resources on visualisation guidelines and course datasets.

Chapter 2 - Plotting overview

This chapter provides an overview of various plotting techniques, exploring different chart types and their applications for effective data representation.

Chapter 3 - Different type of plots

This chapter delves into the diverse range of plot types available for data visualisation, including bar charts, line graphs, scatter plots, histograms, and many more. It highlights the unique strengths for each plot type. Best practices fpr creating these plots are also covered to ensure clarity and impact.

Chapter 4 - Additional plots

This chapter focuses on additional plot types, including violin plots, dought chart, and pie chart. It explains their unique features, appropriate use cased, and best practices for effectively visualising data with these specialised chart types.

Chapter 5A - Presenting tables in toyplot

This chapter explores the creation and customization of tables using Toyplot, a python-based data visualisation library. It covers techniques for displaying tabular data effectively, including formatting and styling.

Chapter 5B - Presenting tables in matplotlib

This chapter explores how to incorporate tables into visualisation using Matploylib. It covers the process of creating, customizing, and presenting tables for enhancing clarity and improving the communication of complex data in a visual format.

Chapter 6 - Case Study

This chapter presents a hands-on case study using the Ames dataset, where you will apply data manipulation techniques and visualisation methods to real-world data. Through this exercise you will gain practical experience in cleaning, transforming and visualizing data.

Chapter 7 - Additional case study

In this chapter, we will be trying to reproduce where possible the visualisations shown