Administrative information

Title	Exploratory Data Analysis
Duration	60
Module	A
Lesson Type	Lecture
Focus	Technical - Foundations of AI
Topic	Exploratory Data Analysis

Keywords

Data exploration,T-SNE,PCA,

Learning Goals

Learner knows the basic chart types and knows when to use them.
Learner can use visualisations to investigate a variables distribution .
Learner can check for dependencies between variables by using visualisation.
Learner is able to visualise a high dimensional dataset using PCA and T-SNE.

Expected Preparation

Learning Events to be Completed Before

None.

Obligatory for Students

Read the blog Using T-SNE in Python to Visualize High-Dimensional Data Sets (alternative)
Read chapter 4 of Python Data Science Handbook

Optional for Students

None.

References and background for students:

None.

Recommended for Teachers

On visualising high dimensional datasets: Using T-SNE in Python
Chapter 4 of Python Data Science Handbook

Lesson Materials

03_Exploratory data analysis

The materials of this learning event are available under CC BY-NC-SA 4.0.

Instructions for Teachers

This lecture focuses on data visualisation as part of the Exloratory Data Analysis (EDA) process. Hence it does not cover data visualisation for, for example, story telling and presentations.

Topics to cover

Introduction to data visualisation (5 min)
- Goals of data visualisations (EDA, storytelling)
Which chart to use for which problem (15 min)
- Chart types and their usage
- How to pick the right chart
- Do's and Dont's
Check for (in)dependent variables (10 min)
Visualising high dimensional data (20 min)
- PCA
- T-SNE

More information

Click here for an overview of all lesson plans of the master human centred AI

Please visit the home page of the consortium HCAIM

Acknowledgements

The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.

The materials of this learning event are available under CC BY-NC-SA 4.0

The HCAIM consortium consists of three excellence centres, three SMEs and four Universities