• FutureLearn logo

Data Visualisation with Python: Matplotlib and Visual Analysis

Discover how to produce visual data analytics and business insights with the powerful Python programming language.

Two people looking at a tablet screen
  • Duration

    4 weeks
  • Weekly study

    4 hours

Many organisations can collect and analyse data effectively, but not all are able to transform these insights into effective decision-making that results in organisational value. That’s where data visualisation comes in.

This Python online course will supercharge your data visualisation skills for both exploratory and explanatory purposes, using the commonly used programming language.

Learn how to use Python for business analysis

Python is used across all industries, from healthcare to finance, and in different fields of business analytics. It’s also one of the simplest programming languages to learn.

You’ll learn how to use Python through the use of its robust graphic libraries to bring insights to life and tell stories that help decision-making.

Explore different types of data visualisation

This course will introduce you to design fundamentals, allowing you to identify and critique components of effective visualised data, charts and the visualisation of complex relationships.

You’ll also get to design powerful visualisations using spreadsheet tools.

Create plots in Python using Matplotlib and time series data

Matplotlib is a powerful Python library for creating plots and charts.

You’ll be introduced to the library and time series data, one of the most commonly used data types. You’ll also master the basics of creating and customising plots using Python code, including custom colours, markers and styles.

Learn how to understand quantitative comparisons and statistical visualisations

Visualisations can be used to compare data in a quantitative manner. You’ll explore the different methods used in the creation of quantitative visualisations.

You’ll find out how to plot bar charts, histograms and scatter plots using Python’s plotting library, Matplotlib.


  • Week 1

    Introduction to visualisation and visualisation design fundamentals

    • Welcome to the course!

      Introduction to the course, information about the optional project tasks, and an overview of Week 1.

    • Introduction to visualisation

      Here you'll explore some basics around visualisations, and we'll list Python visualisation libraries that you will use in this course.

    • Visualisation design fundamentals

      In this activity, you'll get to explore some of the useful principles and techniques used to design data visualisations.

    • Wrap-up

      Now it's time to summarise and reflect on your learning this week and look ahead to what's next.

  • Week 2

    Designing charts and visualising complex relationships

    • Introduction

      Welcome to Week 2! Let's first introduce the week's topics and outcomes.

    • Designing charts

      In this activity, we’ll look at some of the common chart types and learn a few of the do’s and don’ts for designing them.

    • Visualising complex relationships

      Here you'll learn about various design considerations for creating charts and visualising complex relationships.

    • Wrap-up

      Let's wrap up what we covered this week and reflect on the learning so far.

  • Week 3

    Introduction to Matplotlib and plotting time series data

    • Introduction

      Welcome to Week 3! Let's first introduce the week's topics and outcomes.

    • Introduction to Matplotlib

      Learn about the Matplotlib library, an important stepping stone towards building great visualisations using Python.

    • Plotting time series data using Matplotlib

      In this activity, you'll practice the skills associated with plotting time series data in Python using Matplotlib and Jupyter Notebook.

    • Wrap-up

      Let's wrap up what we covered this week and reflect on the learning so far.

  • Week 4

    Quantitative comparisons, statistical visualisations, and sharing visualisations

    • Introduction

      Welcome to Week 4! Let's first introduce the week's topics and outcomes.

    • Quantitative comparisons and statistical visualisations

      Learn how to plot bar charts, histograms, and scatter plots using the plotting library of Python: Matplotlib.

    • Sharing visualisations

      In this activity, you'll apply skills to share data visualisation created in Python using Matplotlib.

    • Wrap-up

      Let's wrap up what we covered this week and the course, and then reflect on the learning so far. You will also have the chance to complete the final quiz for this course.

Learning on this course

On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

What will you achieve?

By the end of the course, you‘ll be able to...

  • Identify and critique components of effective visualisations, charts and visualisation of complex relationships
  • Design effective visualisations using spreadsheet tools
  • Understand the architecture and objects of the Matplotlib Package
  • Create Static Plots using Matplotlib

Who is the course for?

This course is designed for professionals who would like to grow their confidence in using Python to produce exploratory and explanatory visualisations and build dashboards to communicate insights.

  • A professional working with data on a regular basis or have a fundamental understanding of data analytics but want to become more employable or progress in their career.
  • A business analyst or junior data analysts looking to further develop their data visualisation skills using Python.
  • An individual with existing programming capabilities looking to enter the data analytics field.

What software or tools do you need?

During this course we’ll be using Tableau Public and Excel. If you don’t have Excel, you might find this online version useful. We recommend you use a computer to access these elements.

Who will you learn with?

I truly believe that leveraging the correct technologies in the appropriate way can take us towards a more sustainable economy. My focus is on converging M&E Engineering with Data Science/Analysis.

Alastair has over 15 years of experience in data science, across business and academia as a researcher, speaker, mentor, consultant and software engineer.

Who developed the course?


FutureLearn is a leading social learning platform and has been providing high quality online courses for learners around the world over the last ten years.

In collaboration with


Endorsers and supporters

endorsed by

Coventry University logo

Learning on FutureLearn

Your learning, your rules

  • Courses are split into weeks, activities, and steps to help you keep track of your learning
  • Learn through a mix of bite-sized videos, long- and short-form articles, audio, and practical activities
  • Stay motivated by using the Progress page to keep track of your step completion and assessment scores

Join a global classroom

  • Experience the power of social learning, and get inspired by an international network of learners
  • Share ideas with your peers and course educators on every step of the course
  • Join the conversation by reading, @ing, liking, bookmarking, and replying to comments from others

Map your progress

  • As you work through the course, use notifications and the Progress page to guide your learning
  • Whenever you’re ready, mark each step as complete, you’re in control
  • Complete 90% of course steps and all of the assessments to earn your certificate

Want to know more about learning on FutureLearn? Using FutureLearn

Get a taste of this course

Find out what this course is like by previewing some of the course steps before you join: