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

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

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.

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