Data Science - Theory to practice

Learning objectives

The goal of the course is to have a broad introduction on data
science and artificial intelligence techniques. The course is split into
three parts:
- Introduction to Data Science, in which we learn the why data
is the value and what are the existing challenges that needs mining
of the data.
- Unsupervised learning, in which we study the concept and
some of the related algorithms: hierarchical clustering, kmeans,
dbscan, hdbscan, etc.
- Supervised learning, in which we study the concept and
some of the related algorithm: regression (linear and logistic),
decision trees, Naïve Bayes, SVM, random forest
- Text analysis (supervised and unsupervised) in which we will
review the specificities of text analysis
Each course is followed by practical work using R and/or python
In cooperation with Total.