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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.

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Explore the best data visualisation tools to become a programming expert

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

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.

What topics will you cover?

  • Python
  • Seaborn Package
  • Static visualisations
  • Static plots: design and visualisation best practice

When would you like to start?

Start straight away and join a global classroom of learners. If the course hasn’t started yet you’ll see the future date listed below.

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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...

  • 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.

If you want to further your knowledge, you may be interested in these courses, from the same provider, that share the same subject and overall learning outcomes:

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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Buy this course

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