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.
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.
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.