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Data Visualisation with Python: Seaborn and Scatter Plots

Discover how to create visualisations and show understanding of data visualisation theory with the Python programming language.

Data Visualisation with Python: Seaborn and Scatter Plots
  • Duration4 weeks
  • Weekly study4 hours
  • 100% onlineTry this course for free
  • Included in an ExpertTrackCourse 2 of 3
  • Get full ExpertTrack access$39/month

This course will teach you how to bring big data sets to life through data visualisation using the powerful Python programming language.

Explore the best data visualisation tools to become a programming expert

On this course, you’ll look at Python for beginners in data analytics. Python is one of the most widely used, and easiest to use programming languages, powering the back-ends of some of the world’s biggest online companies, including Google, Dropbox and Instagram.

You’ll learn how a Python programmer uses data to create graphical representations that can be easily analysed and examined.

Learn how to use Seaborn in Python

This course will also introduce you to Seaborn, a data-visualisation library in Python. Seaborn combines aesthetic appeal with the powerful technical insights of the programming language.

You’ll learn how to identify a scatter plot, line plot, and other relational plots, as well as how to understand the differences between them.

Understand quantitative and categorical variables

What are quantitative and categorical variables used for in Python? You’ll see how the programming language visualises categorical data (which has a fixed length) and quantitative data (which can be measured).

You’ll also find out how to categorise plots and other quantitative variables of data visualisation.

Examine uncertainty in data and visualisation workflows

The final section of the course will teach you the basics of uncertainty within visualisations. You’ll examine uncertainty in data, point estimate intervals, and confidence bands.

Using your new knowledge, you’ll be able to confidently display uncertainty in data and walk through creating a workflow of a visualisation based on exploring a dataset.

Syllabus

  • Week 1

    Introduction to Seaborn and visualising quantitative variables

    • Welcome to the course!

      Get introduced to information about the optional project tasks, and an overview of Week 1.

    • Introduction to Seaborn

      In this activity, you'll learn about the architecture and objects of the Seaborn package.

    • Visualising quantitative variables

      Learn the key aspects of rational plots and subplots by exploring the various customisation options in Seaborn.

    • Wrap-up

      Now it's time to reflect on your learning throughout the week, and look to what's next!

  • Week 2

    Visualising categorical and quantitative variables and customising Seaborn plots

    • Introduction

      Welcome to Week 2! Let's start by introducing the week's topics and outcomes.

    • Visualising categorical and quantitative variables

      After exploring scatter plots, line plots, and other relational plots, you will now practice plotting categorising plots and quantitative variables of data visualisation using Seaborn.

    • Customising Seaborn plots

      In this activity, you'll learn how to customise Seaborn plots, specifically how to the style, colour, and add labels and titles to them.

    • Wrap-up

      To complete the week, let’s recap the key points covered so far.

  • Week 3

    Highlighting data and using colour in visualisations

    • Introduction

      Welcome to Week 3! Let's start by introducing the week's topics and outcomes.

    • Highlighting your data

      In this activity, you will learn how to highlight and annotate various parts of the data in bar, scatter, and other types of plots.

    • Using colour in visualisations

      Here, you'll explore the colour theory of data visualisation.

    • Wrap-up

      Now it's time to reflect on your learning throughout the week and look to what's next!

  • Week 4

    Showing uncertainty and visualisation workflow

    • Introduction

      This week, you'll be introduced to display errors and uncertainties in your data, and exploration workflow using a marathon data set.

    • Showing uncertainty

      Explore how to display errors and uncertainties in your data on a plot using Python.

    • Visualisation workflow

      Delve deeper with visualisation by looking at a real data exploration workflow using a marathon data set in data analytics throughout this activity.

    • Wrap-up

      To complete the week and course, let’s recap the key points covered so far.

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  • Learn the latest in your chosen industry or subject.
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  • Create a shareable certificate link for your CV and LinkedIn.
  • Impress employers with learning outcomes you can add to your CV.
  • Make your career dreams a reality.

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What will you achieve?

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

  • Explain the architecture and objects of Seaborn Package
  • Create Static Visualisations using Seaborn
  • Customise the Seaborn plots
  • Apply design and visualisation best practices to static plots

Who is the course for?

This course is designed for professionals looking to grow their confidence in using Python to produce exploratory and explanatory visualisations and build dashboards, as well as better communicate their insights.

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.

Who developed the course?

FutureLearn

FutureLearn is jointly owned by The Open University and The SEEK Group and has been providing online courses for learners around the world over the last eight years.

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About this ExpertTrack

Learn to leverage Python libraries to conduct data modeling and build compelling visualisations.

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