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
Recommended for Teachers
Lesson materials
None.
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 |
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.
Lesson plan on SURF
[{{{surfLink}}} WikiWijs page]