SkillsCourses

Data Analytics with R

2 Modules

1,5 Hours

EQF-Level: 5

About

Learning Outcomes

Modules

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About the course

Target group

Physicians, Technicians

Key words

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Course introduction

This course presents how to implement and run the Machine Learning approaches showed in the previous course. They are implemented using the open-source platform R. The lessons contain examples on sample dataset, that students can follow step by step in order to reproduce what shown and to apply the same approaches to their own data. 

Details to know

Downloadable certificate

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Assessment

8 Quizzes

Taught in English

Only available in English

Learning outcomes

Module 1 / Module 2 

  • Competence 
    • Ability to use open software R to perform ML analyses. 
  • Knowledge 
    • Understanding how to implement complexity reduction approaches for linear regression in R. 
    • Understanding how to implement k-NN and k-means approaches in R. 
    • Understanding likelihood-based inference and model-based clustering and how to implement them in R. 
  • Skills 
    • Using R to create, run and analyse linear regression models. 
    • Using R to create, run and analyse k-NN and k-means. 
    • Using R to create, run and analyse likelihood-based inference and model-based clustering. 

More detailed Learning Outcomes can be found in module introductions.

Simple applications

Module 1. Short manual of R tool for statistical analyses. Simple Machine Learning approaches with R.

Lessons

Introduction 1. R statistical tool – Example of linear regression in R 2. R session – Model complexity reduction in linear regression 3. R session – Supervised learning via k-NN 4. K-means with R

Advanced applications

Module 2. More complex Machine Learning approaches with R. 

Lessons

Introduction 1. Hierarchical Clustering with R 2. Analysis of the data from the Course on Databases 3. Analysis of the data from the Course on Databases 4. Extra tools: likelihood based inference; model based clustering Course evaluation

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Co-funded by the Erasmus+ programme of the European Union under Grant Agreement number 101056563.

Co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or EACEA. Neither the European Union nor the granting authority can be held responsible for them.