Program objectives
The first year of the program is made of two core semesters of
broad-spectrum Informatics classes. It gives students the
necessary skills to tackle in-depths subjects in the second
year of the program.
The specialty EID2 MSc focuses on data science and machine
learning. The program is particularly suited for students who
have completed a Bachelor’s degree (or equivalent) in one of
the fields of computer science, mathematics or statistics, and
wish to pursue a career in data science and analytics.
The EID2 MSc is designed to produce graduates with the
knowledge and skills to:
• Select, apply and evaluate machine
learning, business analytics and data mining techniques which
are focused on discovering knowledge that can be acted on to
add value to a company.
• Bring both an in-depth theoretical
understanding, and the practical hands-on experience, to a
data science and machine learning project including
implementing novel and emerging techniques.
• Keep abreast of current research and
business analytics related topics.
Program overview
The cornerstone of this Master's program in Informatics lays
on a tight link bridging the university's research and
teaching vocations. This strong connection is realized through
elective orientation and discovery classes allowing students
to define their professional goals. In the second year of the
Master's program, part of the third semester offers several
concentrations each with their specific curriculum, while
still keeping a common core of foundational, discovery and
cultural “TUs” (Teaching Units).
The program for the EID2 MSc is built on a foundation of core
and elective courses. This program joins courses with a
Computer Science main theme, those with a Statistical data
analysis, Data Science, Machine Learning, Advanced Databases,
Data Mining, Business Analytics, and Data Warehousing main
theme, and those with cultural courses. The electives courses
may be chosen, in consultation with the student's advisor, to
meet the interdisciplinary and the speciality
distribution requirements. The fourth semester is devoted to
an internship specific to each concentration and that can
focus on either research careers or engineering careers.
Performance assessment
• Year-long tests and final exams
• M1 (first year) thesis and M2 (second
year) internship: written report and oral defense.
Admission requirements
• M1: students can be admitted into the
first year (M1) or second year (M2) of the program. In general
however, admission is selective into the first year to any
student holding a Bachelor's degree in informatics.
• M2: to be admitted in the second year of
the Master's program, students must pass the first year of a
Master's degree in IT. Admission is granted by the president
of the University upon the recommendation of the program's
director. The program's director requests the opinion of a
jury on each student's ability to perform in the second year
of the program. For students coming from other Master's
programs (mathematics and IT, statistics, Applied IT for
Business Management…), admission may be granted upon review of
the applicant's background in Informatics.
Career placement
• Jobs: graduates from the Master's degree
in Informatics generally find employment as:
- Data
scientists
- Data miners
- Project Managers in decision-making
- Designers of specialized software
tools
- Research and development engineers
- Consulting experts in
decision-making
- Researcher (with a PhD) in the
fields of machine learning, data science, decision-making.
• Fields: in high-tech areas of
Aeronautics, Automotive, Telecommunications, Automatisms,
Robotics, Energy, Laboratories, Banks, Insurance companies,
service and applications IT, Retail.
Further education
After graduating from the Master's program, students can get
into PhD programs with research teams within the LIPN and
within other partnering labs such as the LAMSADE (Université
Paris 9), the LIP6 (Sorbonne Université), the LRI and the
LIMSI (Université Paris-Saclay) or any other university or
industry lab, as well as with the IFSTTAR, INRIA, INRA,
CENAGREF, IRD and IGN. Funding opportunities for PhD research
are available with Cifre scholarships.
M1: Semester 1 (S1)
First week: Review (10 h of Algebra and 10 h of Analysis)
Foundational TU (1)
• Advanced data structure (4 ECTS
credits)
• Fundamentals of programming (5 ECTS
credits)
• Database engineering (5 ECTS credits)
3 Discovery TUs
• Algorithmic geometry (4 ECTS)
• Dynamic systems specification
(4 ECTS credits)
• Exploratory data
analysis (4 ECTS credits)
• IT networks (4 ECTS credits)
• Knowledge representation (4 ECTS
credits)
• Security and information
theory (4 ECTS)
• Transition systems &
model checking (4 ECTS credits)
Transversal TU
• English (2 ECTS credits)
• Communication and Writing Techniques (2
ECTS credits)
• Free (Sport, International Mobility,
Associative Activity) (Bonus)
M1: Semester 2 (S2)
Foundational TU (2)
• Compilation (5 ECTS credits)
• Distributed Java programing (5 ECTS
credits)
3 Orientation TUs
• Algorithmics on words (4 ECTS
credits)
• Constraint programming (4
ECTS credits)
• Distributed systems (4 ECTS
credits)
• Internet of Things (4 ECTS)
• Matrix factorization methods
for data mining (4 ECTS credits)
• Operational research (4 ECTS
credits)
• Research initiation (4 ECTS)
• System administration (4
ECTS credits)
Transversal TUs
• English (2 ECTS credits)
• Communication and Writing Techniques (2
ECTS credits)
• Project leading and managing (4 ECTS
credits)
• Free (Sport, International Mobility,
Associative Activity) (Bonus)
M2: Semester 3 (S3)
Module (DSML): Data Science and Machine Learning
• Statistical
multidimensional data analysis (3 ECTS)
• Statistical
learning (3 ECTS)
• Digital Data
Science (3 ECTS)
• Neural
Networks and Deep Learning (3 ECTS)
Module (DEBI): Data Engineering and Business Intelligence
• Advanced Databases (3 ECTS)
• Data Mining (3 ECTS)
• Data Warehousing (3 ECTS)
• Business Intelligence (3 ECTS)
Transversal TU
• English (2 ECTS credits)
• Innovation (2 ECTS credits)
• Soft skills (2 ECTS)
• Free (Sport, International Mobility,
Associative Activity) (Bonus)
M2: Semester 4
2 Deepening TUs
• Abstraction and refinement (3 ECTS)
• Data flow and programming (3 ECTS)
• Decision-making support (3 ECTS)
• Grids and cloud computing (3 ECTS)
• Human-Machine Interaction (3 ECTS)
• Infinite, timed and hybrid systems (3
ECTS)
• Learning visual representations (3 ECTS)
• Learning, constraints, planning (3 ECTS)
• Quantum computing (3 ECTS)
• Social network analysis (3 ECTS)
• Speech analytics (3 ECTS)
• Textual data processing (3 ECTS)
• User eXperience design (3 ECTS)
Internship
The fourth semester is targeted to the writing of a
dissertation during an internship in either a laboratory or a
company.
• Industry/Laboratory internship (24 ECTS
credits)
