💻 Week 03 - Class Roadmap (90 min)

2023/24 Winter Term

Welcome to our third seminar/lab class of DS101W.

This week is all about data visualisation. We will discuss what data visualisation is about, why it is important and how to do it well. You will once again be shown a Python demonstration (this time for data visualisation), with an opportunity for you to do some light coding.

Step 01 - Discussion without prompt (~10 min)

• Pair up with the neighbours in your table (groups of 3-4) and discuss the following:
  • What do you think data visualisation is?
  • What is it useful for? Why do we do it?
  • Do you have examples of data visualisation/plots in mind?
  • Any bad practices/challenges that come to mind?
• Share your insights with the whole class

Step 02 - Discussion with prompts (~20 min)

• Now, look at the following materials:

  1 - Article 1

  2 - Article 2

  3 - Article 3

  4 - xkcd comic 1

  5 - xkcd comic 2

  6 - xkcd comic 3

  7 - xkcd comic 4

• Discuss them with your neighbours

• Share your insights with the wider class

🍵 Break (~10 min)

🐍 Step 03 - Visualisation Activity (~50 min)

• You should have a Google Colab account already set-up from last week’s class. If that is not the case, please follow the instructions from the week 2 class roadmap to set-up a Google Colab account.
• Your class teacher will demonstrate how one could use Python to do data visualisation.
• You will do this activity in pairs.
  
• First, read an article on inflation:Link.
• Using the dataset provided by your class teacher, follow the instructions in the class to create a visualisation plot.
• Critically analyse any findings you observe in your plots, and share with the class.

Note

Take some time later this week to read and practice some programming. You can find links on the 📔 Syllabus page and at the end of this week’s slides. While you are not directly assessed on programming skills, programming skills are a core part of being a data scientist so it’d be a useful skill to acquire and would also give you more insights and understanding of the inner mechanics of the algorithms we study in this course (and beyond!).

We might also require you to recall the names of tools and functions we explored this week in your summative assessments.