Program

EIT Digital Data Science 1st Year Master Program

(update is on going till feb. 2019)

The Master 1 year consists of two semesters and allows you to earn (at least) 60 ECTS (European Credits Transfer System). Each semester provides 30 ECTS. The year average mark / result is only done on the modules in each semester that account for the per-semester 30 ECTS.

Each student must accomplish the requested Innovation and Entrepreneurship (I&E) courses and summer school that the EIT label mandates. The total is 24 ECTS, out of the 60 ECTS of the whole year.

To this aim, the course modules listed below, and the mandatory summer school that the Master School Office organizes centrally will allow you to earn the 60 ECTS that you need. When a module encompasses more than one course, the average mark of the courses in the module must be >=10 over 20, in order to collect the corresponding ECTS.

To allow the maximum of flexibility in order to accomodate strong differences between students background (e.g. Bachelor in Statistics compared to Bachelor in Computer Science/Engineering), we have a quite long list of electives. Personal guidance is provided to help  students make their choice at the begining of the first semester.

1st Year Master Program (From 2018-19)

Semester 1 technical major courses

 
Name of the Module (block) Total
number of ECTS
Data science 1 6
Subject Coeff. Shared with
Modelisation & optimisation in machine learning 3 MAM4
Technologies for massive data 3 MAM5/SI5
Elective courses 1, that could be selected without time schedule conflicts (18-19 acad. year) 15
Subject Coeff. Shared with
Personal or in group project in Data science 3 N/A
Computational linguistics (Automatic natural language processing) (FR) 3 M1 Info.
Parallelism 3 M1 Info.
Computer networks 3 M1 Info.
Relational Databases (FR) 3 MAM4
Data science industrial experiences and challenges 3 MAM5/SI5 SD
Peer to peer 3 MAM5/SI5
Data mining for networks 3 MAM5/SI5
Blockchain and privacy 3 MAM5/SI5
Virtualized cloud computing 3 MAM5/SI5
Large scale distributed systems 3 MAM5/SI5
Analysis and indexing of images & videos in big data systems 3 MAM5/SI5 SD
Middleware for the Internet of Thing 3 MAM5/SI5
Content distribution in wireless networks 3 MAM5/SI5
 
 

Semester 2 technical major courses

Name of the Module (block) Total number of ECTS
Data science 2 9
Subject Coeff. Shared with
Data valorization 3 MAM4 SD
Computer vision and machine learning 3 MAM4 SD
Temporal series 3 MAM4
Elective courses 2, that could be selected without time schedule conflicts (18-19 acad. year) 6
Subject Coeff. Shared with
Personal or in group project in Data science (can be continuation of sem. 1) 3 N/A
Not yet defined (in progress, till time schedule and conflicts get known): possibilities of these M1 Info shared courses are among Comm&Concurrency, Combinatorial optimization, Graphs, Software engineering, winter school on complex networks 3 M1 Info.
Almost defined (in progress, as time schedule and conflicts are almost all known): possibilities of these MAM4 shared courses are among: IA/Optimisation (English/French) 3 MAM4
 


MAM4 means 4th year of Maths Appliquées and Modelisation (MAM) department of Polytech Nice Sophia. SI5 means 5th year of Sciences Informatiques (SI) of this same engineering school.  MAM5/SI5 SD corresponds to the 5th year' option track entitled "Sciences des Données" (SD). This SD track already starts from the MAM4 second semester curriculum (eg. MAM4 option SD). Of course, all courses listed are taught in English except if not specifically mentioned (FR).
Polytech MAM4 students that wish to get enrolled in the Master 1 DSC can also have a chance to take as optional French taught courses the following (supposing there is no time schedule conflict):
  • Semester 1: Interpolation numérique, Equations aux dérivées partielles, Processus stochastiques
  • Semester 2: Réalité augmentée (course of the MAM4-SD track)

I&E Minor courses

Minor courses are decomposed along the two semesters in the following blocks:

Semester 1:

I & E 1 Total 9 ECTS
Basics in I&E 3 coeff
Entrepreunership (SKEMA Business course DS4H EUR shared) 3 coeff
Business Dev. Lab Part1 3 coeff

Semester 2:

I & E 2 Total 6 ECTS
Digital business (SKEMA Business course DS4H EUR shared) 3 coeff
Digital IP and Law (Law Faculty DS4H EUR shared) 3 coeff
I & E 3 Total 9 ECTS
Business Dev. Lab Part 2 5 coeff
Summer school (globally organised EIT Digital) 4 coeff


Overall, the Basics in Innovation and Entrepreunership accounts for 6 ECTS, and the Business Development Lab  for 8 ECTS.


Former Course plans (up to 2017-2018) : Semester 1, September-February (30 ECTS)

Module name Module content: courses list Total number of ECTS of the module
Communication and management skills (part of I&E) 3 courses: French as a Foreign Language (or English for French speaking students), Scientific communication, Mini Business Development Lab 6 ECTS
Networked and Large Scale Systems  2 courses depending on student's background among Web of Data, Semantic Web, Introduction to Networking (part 1 of), Algorithms for networking (with a Data Analysis lab) (part 2 of), Network security  (part 3 of),  Content distribution in Wireless networks, Virtualized Cloud technologies, Large Scale Distributed systems 4 ECTS
Algorithmic and Applications in Data management 2 courses: Combinatorial and Graph techniques (part 2 of); Basics of probability and statistics (part 3 of). Depending on student's background, also Analysis and Indexing of images and videos or Semantic Web can be selected. 4 ECTS
Data Analytics 2 courses, each 3 ECTS: Technology for Big Data; Data Mining 6 ECTS
I&E  principles (part of I&E) 3 9hours long courses on: Technological entrepreneur ideation, Entrepreneurial finance, Business and marketing, 4 ECTS
Options (choose 3 courses, each of 2 ECTS) Cryptography and network Security (part 3 of); Greedy algorithms in IA/Problem solving (part 1 of); Winter school on complex networks;  Large Scale Distributed systems;  Personal project 1, Personal project 2 (can be combined); Knowledge Engineering 6 ECTS
Bonus courses Analysis and indexing of images and videos in big data systems; Virtualized and cloud computing; Content distribution in Wireless Networks 
can be accounted if they have not yet been used in the above courses blocks.		 2 ECTS each;
Each course mark>=10, +0.25 on the semester average mark;
mark in [12,14[ +0.5; mark >=14 +0.75

Semester 2, February-June (30 ECTS)

Distributed systems Concurrent, parallel, distributed systems 4 (not 6) ECTS
Data Analysis Statistical machine learning (short version), Data Valorization  4 ECTS
Data Science Selected Research seminars in Data Science on the campus premises (homeworks are research papers synthesis, or specific practical works, eg. on Tableau), and from Web Science 4 ECTS
Optional courses Depending on student's background:  Machine learning for computer vision (part 1 of), Computer Graphics (part 4 of); Advanced OO programming (C++ part of); or  (in French) Programming distributed /parallel applications 2 ECTS
Study and research in Business in Data Science (part of I&E) Business Development Lab (standard spec, see Course 1), including technical POC solution for the industrial selected use case (7 ECTS)
Data Science business oriented application courses  (standard spec, see Course 2,  and more details here) and research seminars (on data search and storage, living labs, legal aspects of data usage) (5 ECTS)		 12 ECTS
EIT Digital Summer School Organized centrally by EIT Digital master school 4 ECTS
Bonus courses

Software programming and object oriented engineering (can be replaced by a research and development personal project with sufficient engineering and development efforts)
 

 2 ECTS each