Research

Overview

The world is producing previously unimaginable amounts of data every second. This data could help to understand and improve our society, to predict and prevent, to combat diseases and generally improve life. Extracting valuable information and creating knowledge from the massive and heterogeneous data require skills in statistical modelling, machine learning algorithms, as well as computer science. The synergy of these academic fields, oriented towards their application, is the guiding idea of the Master for Smart Data Science at ENSAI.

ENSAI is part of the network of prestigious higher-education establishments in France known as Grandes Ecoles, or specialized graduate schools. ENSAI trains its students to become qualified, high-level specialists in information processing and analysis. The graduates of this Master will be capable of creating and implementing methodologies and algorithms for analyzing large flows of data arriving from different sources, of using statistical tools and machine learning algorithms to identify correlations, effects, patterns and trends in data, and of formalizing predictions. As such, they will be qualified for data scientist and artificial intelligence jobs in industry, marketing, banking and insurance, media, or further pursuing a PhD.

This Master’s program is composed of 1 semester of coursework at ENSAI, followed by a four to six-month paid internship in France or abroad within the professional world, academia, or research laboratories.

Since this program welcomes students with varying academic levels and skills in Computer Science, Applied Mathematics and Statistics, preliminary coursework is put in place to bring all students to the same scientific level in these fields, concerning their existing training, knowledge, and skills.

Most of the lectures are intended to take place at ENSAI. Some of the lectures are scheduled online using Microsoft Teams.

Courses

Program
Teaching hours
Class Project Lab Tutorial Total
Credits
Program
Teaching hours
Class Project Lab Tutorial Total
Credits