Administrative information

Title	Presenting statistical data
Duration	150
Module	D
Lesson Type	Practical
Focus	Ethical research in practice
Topic	Literature

Keywords

Data cleaning, data representation, visualisation.

Learning Goals

Understand and demonstrate the data representation for AI and ML modelling
Apply a range of plotting techniques to visualise data
Be able to check for fairness, handle outliers and missing data

Expected Preparation

Learning Events to be Completed Before

Obligatory for Students

Optional for Students

None.

References and background for students

Introduction to Descriptive Statistics - University of Sydney

Recommended for Teachers

Introduction to Descriptive Statistics

Lesson Materials

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

Instructions for Teachers

Review the outline of the lesson plan for instructions to students. This exercise is suited for the dataset that contains a mix of numerical and categorical data types. Data representation methods vary in datasets containing images, text, audio, etc.

Outline


Duration	Description	Concepts
15 min	Provide a description of tasks for the practical.	Review of descriptive statistics for a dataset containing numerical and categorical data
70 min	Plotting and basic cleaning of data	Missing data, good data practices, centre and spread, basic plotting, data encoding
70 min	Visualisation and Initial Investigation	Google facets, fairness check, biases
15 min	Conclusion, questions and answers	Summary

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