Administrative information

Title	Preparation and Exploration
Duration	60
Module	A
Lesson Type	Lecture
Focus	Practical - AI Modelling
Topic	Data preparation methods

Keywords

Data Preparation,Data Cleaning,Data Transformation,Data Normalization,Data Integration,Data Reduction,

Learning Goals

To be able to chose the most suited data preparation method based on the case
prepare data in practice (handle missing values, create new derived features)
Data enrichment
Ethical: anonymisation and problems with this (identification possible in indirect ways) - again, there should be some examples out there
Imputation – mention that it can introduce bias and that this needs to be kept in mind
New feature creation – loss of proper semantics
Ethical: remove bias from the dataset
Parallels and differences between sampling of data in statistics and acquisition of data (including big data) for ML and AI

Expected Preparation

Learning Events to be Completed Before

Obligatory for Students

Optional for Students

References and background for students:

Recommended for Teachers

Lecture: Understanding data

Lesson Materials

Data preparation methods - UNINA.pptx on sharepoint

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

Instructions for Teachers

You can base this class around the slides.

Outline


Duration (min)	Description	Concepts
5	Outline	Data preparation methods: what's the point?
5	Problems / Preprocessing	What problems can the data have, cleaning, purification
5	Data Preparation	Cleaning, transformation, integration, normalization, imputation, noise identification
5	Data Preparation in detail	Forms of data preparation
10	Data Cleaning in detail	Fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset
10	Data Transformation in detail	Converting data from one format to another, best practices.
5	Data Normalization in detail	Data normalization best practices.
5	Data Integration in detail	Data integration best practices.
5	Data Reduction in detail	Data Reduction best practices.
10	Data preparation in practice	Filtering, missing values, duplicates,
5	Concluding remarks	Emphasizing the importance of data preparation.

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