EIT Digital Data Science 1st Year Master Program (2017-18 version)
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. You can gather additional ECTS per semester, and a bonus on the semester average mark in this case. 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 allowed to fill some of the modules with different courses taken from either proposed as optional courses or as bonus courses lists. The idea behind having a bonus courses list is to allow a student to study all the by default proposed courses, plus additional courses in order to take the most from the M1 study program.
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)
|EIT Digital Summer School||Organized centrally by EIT Digital master school||4 ECTS|
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|
1st Year Master Program (2018-19 provisionary version)
Each mandatory course is 3 ECTS. The provenance (curriculum) is also indicated.
MAM4 means 4th year of Maths Appliquées and Modelisation (MAM) department of Polytech Nice Sophia. SI4 means 4th year of Sciences Informatiques (SI) of this same engineering school. MAM5 or SI5 corresponds to the 5th year, which conjointly offer a speciality entitled "Sciences des Données" (SD). This SD speciality already starts from the MAM4 second semester curriculum (eg. MAM4 option SD). DS4H is for the EUR Graduate school, in which new minor courses are included. Of course, all courses listed here and below are taught in English even if not specifically mentioned, except those taken from the Master 1 Informatique curriculum which are by default taught in French if not explicitly mentioned as being taught in English (EN).
To complete a semester whose total is 30 ECTS, the student must choose up to the total of ECTS indicated (below, e.g. 12), and on the right hand side, is the number of ECTS of each course.