Data and Decision Sciences🔗
The Data and Decision Sciences program is a Master-level specialization in Data Science, modern Artificial Intelligence and Decision Support at ISAE-SUPAERO.
Syllabus🔗
The program is composed of 240h of classes, shared between the following modules:
-
Foundations in Decision Making (FSD301, TC)
60 hours covering statistics, optimization, and decision theory -
Algorithms in Machine Learning (FSD311, AML)
80 hours on supervised, unsupervised, deep, and reinforcement learning -
Data Engineering (FSD312, DE)
50 hours on data storage, computation, and distribution -
Digital economy and data usage (FSD313, ENUD)
15 hours on business models in the digital economy and on data privacy -
In-depth modules
15 hours in parallel modules on various data science topics -
Hackathon (FSD314)
3 days (20 hours) working on real-world data science competitions -
Seminars (FSD319)
Seminars from professional experts, training sessions, and challenges
This program is complemented by a 6 months internship in data science.
Foundations in Decision Making (FSD301, TC)🔗
- Statistics
- Graph Optimization
- Combinatorial Optimization
- Stochastic Optimization
- Decision Theory
Algorithms in Machine Learning (FSD311, AML)🔗
- Statistical Foundations of Machine Learning
- Unsupervised and Supervised Learning Algorithms
- Deep Learning
- Reinforcement Learning
Data Engineering (FSD312, DE)🔗
- Database Management Systems
- Cloud computing and virtualization
- GPGPU computing
- Distributed computing with Kubernetes and Dask
Digital Economy and Data Use (FSD312, ENUD)🔗
- Business models in the digital economy
- Data security and privacy issues
In-depth Modules🔗
Parallel classes shared with the Decision Systems program. The classes offered in SDD cover:
- Business Intelligence (Problem modeling, data understanding and visualization)
- Computer Vision
- Reinforcement Learning
Hackathon (FSD314)🔗
A 3 day hackathon with external partners on real-world challenges in Data Science.
Seminars (FSD319)🔗
Seminars given throughout the year on data science topics by academics and professional experts, challenges like Kaggle competitions, and more.