1. Descriptive and Predictive analytics (48 hours)
Introduction to analytics.
Statistical methods to support data analysis and learning.
Optimization models for data analysis and learning problems: regression models.
Data preprocessing, model validation, feature selection and extraction.
Clustering and Principal Component Analysis.
Outliers detection. 
Performance metrics for learning models.

Overview of models for supervised and unsupervised learning:
Logistic Regression, Decision Trees.
Support Vector Machines, Neural Networks

Learning from imbalanced data
Introduction to Time Series Analysis.

2. Prescriptive analytics: Decision Optimization (48 hours)
Introduction to Mathematical Programming: linear, non-linear and integer linear programming models.
Integer programming models for fundamental combinatorial optimization problems:
Assignment Problem; Set Covering, Packing and Partitioning Problems; Minimum Spanning Tree Problem; Traveling Salesperson Problem (TSP). Formulations of logical conditions and fixed costs.
Uncapacitated Facility Location; Uncapacitated Lot Sizing; Discrete Alternatives; Disjunctive Formulations.

Geometry of Integer Programming.
Optimality, Relaxation and Bounds.
Polyhedra: dimension, representations, valid inequalities, faces, vertices and facets. Good and Ideal formulations.

LP based branch-and-bound algorithm:
Preprocessing, primal heuristics and branching strategies.
Cutting Planes algorithms. Automatic Reformulation: Gomory's Fractional Cutting Plane Algorithm.
Examples of strong valid inequalities: cover and clique inequalities.
Branch-and-cut algorithm.
Applications
Constrained Spanning Tree Problems; Constrained Shortest Path Problem; Clustering problems; Multicommodity Flows; Symmetric and Asymmetric Traveling Salesperson Problem; Vehicle Routing Problem; Steiner Tree Problem.

Integration of predictive and prescriptive techniques in industrial applications.