Practical: Data Preparation and Exploration

Practical: Data Preparation and Exploration

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

Obligatory for Students

None.

Optional for Students

None.

References and background for students:

None.

Recommended for Teachers

None.

Lesson Materials


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

HCAIM Consortium

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Auteur
HCAIM Consortium
Laatst gewijzigd
2024-05-15 11:06:17
Licentie

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Aanvullende informatie over dit lesmateriaal

Van dit lesmateriaal is de volgende aanvullende informatie beschikbaar:

Toelichting
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Eindgebruiker
leerling/student
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Studiebelasting
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Gebruikte Wikiwijs Arrangementen

HCAIM Consortium. (z.d.).

Acknowledgement

https://maken.wikiwijs.nl/198386/Acknowledgement

HCAIM Consortium. (z.d.).

Lecture: Duty Ethics

https://maken.wikiwijs.nl/198966/Lecture__Duty_Ethics

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