Short Graduate Program in Data Science and Business Analytics

Linear programming
The simplex algorithm
Sensitivity analysis and duality
Integer programming and its applications
Branch-and-bound and branch-and-cut algorithms
Basic notions and algorithms in non-linear programming
Convexity KKT Conditions and Optimality
Gradient- and Newton-based algorithms
Heuristics and metaheuristics

1) distributed and parallel computation

2) Supervised learning

3) Non-supervised learning

4) Reinforcment learning

5) Recommendations system

6) Sequential decision making

Basic principles in inference and statistical modeling
Linear models
Generalized linear models
Models for longitudinal data and correlated data
Introduction to survival analysis

Theme 1 - Sequential programming algorithmic analysis.
Theme 2 - Parallel computing with shared memory (using threads).
Theme 3 - Synchronous parallel computing without shared memory (MPI).
Theme 4 - Distributed computing.

Health care (assignment scheduling localization collection and distribution diagnostics planning)
Sports (performance optimization performance analysis risk and reliability analysis organization of sport events scheduling routing)
Energy (transmission distribution and use global techno-economic models optimization of large problems non-convex optimization)
Environment (multi-criteria and multi-objective optimization data envelopment analysis stochastic optimization robust optimization)
Public affairs and national defense (localization and coverage prevention and detection assignment fairness)
Humanitarian aid and crisis management (logistics networks routing transportation distribution and storage simulation heuristics decision support systems)
Transportation and distribution (routes schedules on-demand transportation learning simulation-based optimization)
Human resource management (scheduling coverage modelling of collective agreements rotating schedules heuristics and metaheuristics)
Academic management (planning scheduling graph coloring heuristics integrated systems)
Customer relationship management (big data recommendation systems segmentation revenue management bi-level optimization)
Production and supply chains (diet issues supply networks competition hierarchy reverse logistics game theory).

1) Probabilistic modeling
2) Monte-Carlo simulation
3) Decision trees
4) Expected utility theory
5) Multiple-criteria analysis
6) Analytical hierarchy process

1 : Introduction to mathematical modelling
2 : Implementation of an optimization model using a dedicated software
3 : Linear models
4 : Network models
5 : Integer linear models and linearization techniques
6: Applications (e.g. location problems distribution problems scheduling problems etc.)
7 : Optimization models with multiple objectives
8 : Stochastic programming models
9 : Dynamic programming models

The nature of text data
Preprocessing: tokenization and lemmatization
Bag-of-words topic models and naive classification
N-gram language models (Markov models)
Hidden Markov models and part-of-speech tagging
Distributed representations and vector semantics
Recurrent neural language models LSTMs and language generation
Transformers and masked language modeling
Encoder models and semantic search
Encoder-decoder models text summarization and translation

- Basics of supervised and unsupervised learning.
- Dimensionality reduction techniques (principal components and factor analysis).
- Hierarchical and non-hierarchical cluster analysis.
- Basics of predictive modeling: Sample division cross-validation bootstrap.
- Parametric models for predictive modeling.
- Association rules (market basket analysis).
- Basic recursive partitioning methods: CART random forest variable importance boosting trees.
- Other topics: Support vector machines nearest neighbor methods basic survival analysis missing data.

Derivative-based / derivative-free stochastic optimization
Markov decision processes and dynamic programming
Approximate dynamic programming
Reinforcement learning
Neural networks and deep reinforcement learning
Two-stage / Multistage stochastic programming
Policy design for sequential decision-making problems