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Data Analytics


This is an applied statistics course focusing on data analysis. The course begins with an overview of how to organise, perform, and write-up data analyses. The course starts with a theoretical part on the how to mine very large datasets to get valuable data to analyse. Then it covers some of the most popular and widely used statistical methods to analyse the data, like linear regression, principal components analysis, cross-validation, and p-values. Instead of focusing on mathematical details, the lectures are designed to help you apply these techniques to real data using the R statistical programming language, interpret and visualise the results, and diagnose potential problems in your analysis.



  • Required book: Jure Leskovec, Anand Rajaraman and Jeffrey David Ullman. Mining of Massive Datasets (2nd edition). Cambridge University Press, 2014.
  • Suggested (but not required): Paul Teetor. R Cookbook. O' Reilly, 2011.



Crestani F.

Course director

Bahreinian S. A.


Additional information

Academic year
Master of Science in Informatics, Core course, Lecture, 1st and 2nd year

Master of Science in Management and Informatics, Elective course, Lecture, 1st and 2nd year

PhD programme of the Faculty of Informatics, Elective course, Lecture, 1st and 2nd year (4 ECTS)