PART 1: DATA ANALYSIS AND CLUSTERING

Coding with Python: overwiew of the main concepts and functions.

Types of data (categorical, ordinal, boolean, etc.), structured datasets (in tabular format), standardization of variables

Descriptive Analysis and Plots, Dimensionality Reduction, Principal Component Analysis, Biplot

Types and characteristics of missing data, imputation methods

The concept of distance/similarity, Clustering Analysis.

Examples of applications, seminars

PART 2: SUPERVISED DATA MODELING

Mono and multivariate linear regression, Dummy Variables, parameter estimation, interaction and confounding

Logistic regression, classification, Odds Ratio.

Classification metrics: accuracy, sensitivity, specificity, precision, F1 score, AUC

Feature selection, regularization methods in regression (e.g. LASSO, ElasticNet)

Fundamental concepts of machine learning and survival analysis

Examples of applications, seminars