DSC Program
Program for EIT Digital Master 2 DSC track
The DSC track consists of two semesters and allows you to earn 60 ECTS (European Credits Transfer System).
The first semester consists of core and optional courses. In addition, each student must complete a Personal Research and Development Project in either one of the local partner institutions (CNRS I3S, CNRS LJAD or INRIA), or by our industrial partners (Orange Labs, SAP research labs, Amadeus, ..., or by the Sophia-Antipolis SMEs ecosystem) for a total of 6 ECTS credits. Subjects are proposed to students during October, so that the project can also start early: approx 1 day per week, plus a four weeks almost full-time period (between Period 1 and 2; End of february: oral defense, delivery of an intermediate report before Christmas, and one final report at the end) should be allocated to the project development.
Version of 2017-2018
Semester 1, October-February (30 ECTS)
Mandatory courses (6 ECTS)
The list of mandatory courses from the 2016-17 academic period is such that it is as close as possible to the Gestion Multimedia pour les Données Massives (Data Science - Science des Données) track of the Master 2 IFI / Master 2 SSTIM / 5th year of study in Gestion Multimedia pour les Données Massives / SD of the applied mathematics and computer science departments of Polytech Nice-Sophia Antipolis engineering school. Mandatory courses are split in two periods (quarters), but constitutes a single module of 6 ECTS (within which all courses marks are averaged and if it is reaching an overall mark of 10 / 20, then the ECTS are gained).| Period 1 Mandatory courses 2017-18 | |
|---|---|
| Panorama of Big Data technologies | Coefficient 2 |
| Compression, analysis and visualization of multimedia content | Coefficient 2 |
| Period 2 Mandatory courses 2017-18 | |
|---|---|
| Analysis and indexation of images and videos in big data systems (from shallow to deep learning) | Coefficient 2 |
Project Fin d'Etudes (6 ECTS)
| Project Fin d'Etudes | |
|---|---|
| Personal research and/or development project in Data Science (examples of subjects, SD/Web sections) | 6 ECTS |
Optional courses (for a total of 12 ECTS)
| Optional courses list from 2017-18 | Topic | Coeff |
|---|---|---|
| Statistical machine learning (Period 2) | Data modeling and analysis | 4 |
| Statistical computational methods = CART and random forests for high-dimensional data (Period 1 and 2) | Data modeling and analysis | 4 |
| Data Science industrial seminars (IBM, Amadeus taught course, CECAZ on Big Data and Analytics, and some Jupyter notebook for Kaggle labs/projects) | Data modeling and analysis | 2 |
| Panorama of Data mining and machine learning techniques | Data modeling and analysis | 2 |
| Management of multimedia (streamed) data (Period 2) | Data processing supporting technologies | 2 |
| Data mining for networks (new course) (Period 2) | Application of data science, in particular on multimedia content and data on the web | 2 |
| Web of Data (Period 1) | Application of data science, in particular on multimedia content and data on the web | 2 |
| Semantic Web (Period 2) | Application of data science, in particular on multimedia content and data on the web | 2 |
| Sécurité des applications web (Period 1, in French) | Application of data science, in particular on multimedia content and data on the web | 2 |
| Blockhain and privacy (partially new) (Period 2) | Data processing supporting technologies | 2 |
| Peer to Peer (Period 1) | Data processing supporting technologies | 2 |
| Virtualized infrastructure for cloud computing (Period 2) | Data processing supporting technologies | 2 |
| Large Scale Distributed Systems (Period 2) | Data processing supporting technologies | 2 |
| Knowledge Engineering (Period 1) | Application of data science, in particular on multimedia content and data on the web | 2 |
| Middleware for the Internet of Things (Period 2) | Data processing supporting technologies | 2 |
| Advanced image processing (Period 1) | Application of data science, in particular on multimedia content and data on the web | 2 |
| Réalité virtuelle (Period 2, in French) | Application of data science, in particular on multimedia content and data on the web | 2 |
| Interagir dans un monde 3D (Period 1, in French) | Application of data science, in particular on multimedia content and data on the web | 2 |
| Ingénierie 3D (Period 1, in French) | Application of data science, in particular on multimedia content and data on the web | 2 |
| French as a Foreign Language (beginner or intermediate). On top of program. (Period 1) | 0 |
Innovation and Entrepreunership module (6 ECTS)
Besides, the student must develop a mandatory Innovation and Entrepreneurship (I&E) work of 6 ECTS, as mandated by EIT Digital I&E common specification of masters. This work is coached by the UNS local coordinator in I&E and spans the whole October-February period. The goal is to reuse on-line I&E material from Sakai, common to all EIT Digital masters, and apply this to selected business cases. These business cases are most of the time proposed by the various EIT Digital Action Lines partners. More details here.Semester 2, March-August (30 ECTS)
Internship
This internship can be done either in our partner research institutions teams at I3S, LJAD, INRIA even if for EIT Digital students, industrial internships are the preferred choice. We provide support and guidance to this aim. Including for outside the Nice - Sophia-Antipolis Telecom valley ecosystem. The evaluation of the internship work encompasses three aspects: work achieved as measured by the internship supervisor, written thesis submitted at the end of August and evaluated by the university supervisor, oral defense organized early September (can happen in visio conference mode) and evaluated by a jury of professors, to which the supervisor is warmly invited to participate.
Version from 2018-2019
The difference with previous version is on the list of the mandatory courses for 6 ECTS, and the list of electives to choose from for 12 ECTS.| Period 1 Mandatory courses 2018-19 | |
|---|---|
| Panorama of Big Data technologies | Coefficient 2 |
| Compression, analysis and visualization of multimedia content | Coefficient 2 |
| Period 2 Mandatory courses 2018-19 | |
|---|---|
| Analysis and indexation of images and videos in big data systems (from shallow to deep learning) | Coefficient 2 |
Optional courses (12 ECTS)
| Optional courses list from 2018-19 | Topic | Coeff |
|---|---|---|
| Statistical machine learning (Period 2) | Data modeling and analysis | 4 |
| Statistical computational methods = CART and random forests for high-dimensional data (Period 1 and 2) | Data modeling and analysis | 4 |
| Data Science: industrial seminars (IBM, Amadeus taught course, CECAZ on Big Data and Analytics, and some Jupyter notebook for Kaggle labs/projects) | Data modeling and analysis | 2 |
| Panorama of Data mining and machine learning techniques | Data modeling and analysis | 2 |
| Management of massive data (mostly network streaming technologies) | Application of data science, in particular on multimedia content and data on the web | 2 |
| Data mining for networks (Period 2 (new course)) | Application of data science, in particular on multimedia content and data on the web | 2 |
| Web of Data (Period 1) | Application of data science, in particular on multimedia content and data on the web | 2 |
| Semantic Web (Period 2) | Application of data science, in particular on multimedia content and data on the web | 2 |
| Sécurité des applications web (Period 1, in French) | Application of data science, in particular on multimedia content and data on the web | 2 |
| Blockhain and privacy (Period 2) | Data processing supporting technologies | 2 |
| Peer to Peer (Period 1) | Data processing supporting technologies | 2 |
| Virtualized infrastructure for cloud computing (Period 2) | Data processing supporting technologies | 2 |
| Large Scale Distributed Systems (Period 2) | Data processing supporting technologies | 2 |
| Knowledge Engineering (Period 1) | Application of data science, in particular on multimedia content and data on the web | 2 |
| Middleware for the Internet of Things (Period 2) | Data processing supporting technologies | 2 |
| Advanced image processing (Period 1) | Application of data science, in particular on multimedia content and data on the web | 2 |
| Réalité virtuelle (Period 2, in French) | Application of data science, in particular on multimedia content and data on the web | 2 |
| Interagir dans un monde 3D (Period 1, in French) | Application of data science, in particular on multimedia content and data on the web | 2 |
| Ingénierie 3D (Period 1, in French) | Application of data science, in particular on multimedia content and data on the web | 2 |
| French as a Foreign Language (beginner or intermediate). On top of program whenever timeschedule allows it. (Period 1) | 0 |
Version from 2019-2020
The slight difference with previous versions is on the list of the mandatory courses for 6 ECTS, and the list of electives to choose from for 12 ECTS.In the block of mandatory courses of 6 ECTS:
- Compression, analysis and visualization of multimedia content course is replaced by a Data Science (IBM, Amadeus taught course, CECAZ on Big Data and Analytics, and some Jupyter notebook for Kaggle labs/projects) course
- Analysis and indexation of images and videos in big data systems (from shallow to deep learning) course is replaced by a reworked content for Management of massive data (streaming technologies).