Administrative Information
| Title | Lab session: Data Preparation |
| Duration | 180 |
| Module | A |
| Lesson Type | Practical |
| Focus | Practical - AI Modelling |
| Topic | Data preparation methods |
Keywords
filtering,missing values,duplicates,Data Preparation,Data Cleaning,Data Transformation,Data Normalization,Data Integration,Data Reduction,
Learning Goals
- To prove to be able to use different data preparation techniques
- is able to identify basic statistics of all features in a given dataset
- is able to calculate basic statistics per group
Expected Preparation
Learning Events to be Completed Before
Obligatory for Students
None.
Optional for Students
None.
References and background for students
None.
Recommended for Teachers
None.
Lesson materials
- [ DataPrepExp notebook]
Instructions for Teachers
This learning event consist of laboratory tasks that shall be solved by the students with the help of the leading instructor.
Outline/time schedule
| Duration (min) | Description | Concepts |
|---|---|---|
| 5 | Outline | Overall goal: document how you struggle with data during preparation |
| 14 | Dataset | Census/reconstruction |
| 20 | Data Preparation | filtering, missing values, duplicates, |
| 20 | Data Cleaning example | Fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset |
| 20 | Data Transformation example | Converting data from one format to another, best practices. |
| 20 | Data Normalization example | Data normalization best practices. |
| 25 | Data Integration example | Data integration best practices. |
| 25 | Data Reduction example | Data Reduction best practices. |
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.