Data analysis [IHDCB333]

Learning outcomes

This course is designed to enable students to solve simple data analysis problems using appropriate algorithms. More specifically, with the necessary creativity and rigour, they will be able to • make a first univariate and bivariate analysis (histograms, scatter plots) • pre-processing data (outliers, missing values, dimension reduction) • manage various types of data (numerical, textual, sequential, spatial) • In-depth data analysis (clustering, classification) • communicate results (graphs, reporting) He/she should also be able to demonstrate an understanding of the different concepts covered (see content), i.e. express in his/her own words the theory and tools seen in the course and explain in which context they are useful.

Objectives

The aim of the course is to introduce the student to data analysis, so that he/she can solve simple problems using appropriate algorithms. The following concepts will be gradually introduced: data preprocessing, univariate and bivariate analysis, data types, clustering, classification and reporting. Particular attention will be given to the proper use of each concept and to illustrating them with examples. The student will have the opportunity to develop creativity and rigour through exercise sessions. The course and the exercise sessions will also give the student the opportunity to discover data analysis tools