Program

EIT Digital Data Science 1st Year Master Program

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 (blocks) 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. 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 at the module level.

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.

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.

Precise time schedule will be gradually published, at edt.polytech.unice.fr/0/invite, in Promotions/Emploi du Temps/Année, choose Master 1 EIT DIGITAL, then click week per week to see slots and rooms. All courses are in the Campus SophiaTech, Polytech Nice Sophia (PNS) buildings (Est E, or Ouest O buildings). Christmas break starts the Friday 20th of december 2019, and the semester resumes on the Monday 6th of January. If possible, we try not to put you exams on these Friday and Monday, and if possible, also no course. But we cannot garantee that. A welcome day organized by the Graduate school DS4H you belong to is scheduled on Sept. 13th whole day on campus + extra evening event in Nice. An other welcome meeting specific to EIT Digital is also going to be scheduled. An informal meeting with the head of the master is to happen probably the Sept 4th afternoon at Polytech for those that start the refresher in Maths/Stats course on the next day; a second one can be scheduled if needed later the next week. Most probably on the Monday 9th.

1st Year Master Program (From 2018-19 academic year)

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).
 

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; Courses Tuesday Morning(8 to 9), Labs Tuesday Morning (10 to 12) or Thursday Morning (10 to 12); Till Christmas break;
Technologies for massive data 3 MAM5/SI5 SD; Monday Morning, Period 1
 
Elective courses From Polytech offer, that could be selected without time schedule conflicts (19-20 acad. year) for a total of at least: 9
Subject Coeff. Shared with
Personal or in group project in Data science * 3 N/A, starting ASAP, early October till end of January
Refresher in Maths, Probas & Stats 3 Blocked full days: 5,6,9,11 of Sept 2019
Processus stochastiques (FR) 3 MAM4; Courses Tuesday Afternoon (13:30 to 14:30), Labs Thursday Afternoon(15 to 17) or Friday Morning(8 to 10); Till Christmas break;
Equations aux dérivées partielles (FR) 3 MAM4; Courses Tuesday Afternoon (17:30 to 18:30), Labs Thursday Morning (10 to 12) or Friday Afternoon (15 to 17); Till Christmas break;
Interpolation Numérique (FR) 3 MAM4; Courses Tuesday Morning (9 to 10), Labs Tuesdays Morning (10 to 12) or Tuesday Afternoon (15 to 17); Till Christmas break;
Data science seminars (IBM, Amadeus taught course, CECAZ on  Big Data and Analytics, and some Jupyter notebook for Kaggle labs/projects) 3 MAM5/SI5 SD; Friday Afternoon Period 1
Data mining 3 MAM5/SI5; Tuesday Morning Period 2
Data mining for networks 3 MAM5/SI5; Thursday Afternoon Period 2
Gestion de données multimedia (Management of massive data (mostly network streaming technologies)) 3 MAM5/SI5 SD; Friday Morning Period 2
Blockchain and privacy 3 MAM5/SI5; Thursday Morning Period 2
Virtualized cloud computing 3 MAM5/SI5; Monday Morning Period 2
Large scale distributed systems 3 MAM5/SI5; Friday Afternoon Period 1
Analysis and indexing of images & videos in big data systems 3 MAM5/SI5; Wednesday Morning Period 2
Compression, analysis and visualisation of multimedia content (update in progress with more Deep L) 3 MAM5/SI5; Monday Afternoon Period 1
Middleware for the Internet of Things 3 MAM5/SI5; Tuesday Morning Period 2
Content distribution in wireless networks 3 MAM5/SI5; Wednesday Morning Period 1
Evolving internet 3 MAM5/SI5; Friday Morning Period 1
Web of data (also online as Coursera EIT Digital course) 3 MAM5/SI5; Tuesday Morning Period 1
Semantic Web (prerequisite Web of data) 3 MAM5/SI5; Tuesday Afternoon Period 2
Ingénierie des connaissances 3 MAM5/SI5; Tuesday Afternoon Period 1
 
Elective courses From Master in Computer Science offer, that could be selected even if today (19-20 acad. year), not much information about time schedule conflicts except that exams happen just before or after Christmas break, for a total of at least: 6
Subject Coeff. Time schedule
Computational linguistics 3 ??
Advanced programming 3 ??
Mandatory project development if elected "Advanced programming" 3 ??
Traitement automatique du texte en IA (TATIA) (FR) 3 ??
Parallelism 3 ??
Computer networks 3 ??
Logic 3 ??
Resolution de problèmes: Introduction (FR) 3 ??
AI Game 3 ??
BD vers Big Data (partly FR) 3 ?? and partly online
 
 

* consult the "M2 EIT Digital DSC Alumni" section to have the list of topics chosen by former students that decided to develop a project

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 from early Feb till mid of May
Computer vision and machine learning 3 MAM4 SD from early Feb till mid of May
Temporal series 3 MAM4 from early Feb till mid of May
 
Elective courses From Polytech offer, that could be selected without time schedule conflicts (19-20 acad. year)  for a total of at least: 3
Subject Coeff. Shared with
Personal or in group project in Data science (can be continuation of sem. 1)* 3 N/A
Artificial Intelligence 3 MAM4/SI4 from early Feb till mid of May
Réalité augmentée (FR) 3 MAM4/SI4 SD from early Feb till mid of May
Optimisation(FR) 3 MAM4 from early Feb till mid of May
 
Elective courses From  Master in Computer Science offer, that could be selected without time schedule conflicts (19-20 acad. year) for a total of at least: 3
Subject Coeff. Time schedule
Communication & Concurrency 3 From mid Feb till early May
Graphs 3 From mid Feb till early May
Software Engineering 3 From mid Feb till early May
Combinatorial optimization 3 From mid Feb till early May
Sécurité (FR) 3 From mid Feb till early May
BD vers Big data avancé (FR) 3 From mid Feb till early May
Internet of the future 3 From mid Feb till early May

 

I&E Minor courses

From 2019-20, minor courses are decomposed along the two semesters in the following blocks:

Semester 1:

I & E 1 Total 9 ECTS
Basics in I&E (spread in the empty slots, till end of january) 3 coeff
Digital business (SKEMA Business course DS4H EUR shared), Thursday morning or Friday morning (TBC) 3 coeff
Business Dev. Lab Part1 (spread in the empty slots, till end of january) 3 coeff

Semester 2:

I & E 2 Total 6 ECTS
Entrepreunership (SKEMA Business course DS4H EUR shared) Thursday morning or Friday morning (TBC) 3 coeff
Digital IP and Law (Law Faculty DS4H EUR shared) Thursday morning or Friday morning (TBC) 3 coeff
I & E 3 Total 9 ECTS
Business Dev. Lab Part 2 (spread in the empty slots, from early march till 21st of June) 5 coeff
Summer school (globally organised EIT Digital in the July-August period) 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