Chief Digital Officer Le Cercle Les Echos Data ScienceTech Institute Sebastien Corniglion

MSc in Applied Data Science & Big Data
Nice Sophia-Antipolis Campus – Paris Campus

The most applied
0-year
full-time MSc

Enterprise-Level
0
Certifications

A total of
0hrs
of tuition

Tuition fees*
0
on-campus

Tuition fees*
0
Online education

Discover the programme structure * Autumn 2017 entry – Tuition Fees

Programme information

This 1-year Applied MSc programme, with its two entries in Autumn and Spring, is designed to open your career to these Big Data Analytics jobs all industries are looking for.

And in France, it definitely makes Data Science “the sexiest job of the XXIth century”! (Harvard Business Review, Oct. 2012)

Classes are given in English from the:

  • end of September to beginning of April for the Autumn entry;
  • beginning of March to mid-October for the Spring entry;

on a full-time basis (5 hours/day) along with an “Engineering Project” (see below) and followed by a 6-month work placement.

In this Applied MSc programme, you will:

  • learn how to understand the analysis, design, implementation & monitoring of IT & Big Data architectures;
  • get familiar with machine and deep learning algorithms with an industrial approach to applied mathematics;
  • learn how to deploy Big Data architectures and Machine Learning results into corporate systems and get familiar with data visualisation;
  • get awareness of the legal consequences of data handling, with a pinch of ethical thinking regarding the consequences of mining (big) data.
  • be trained to and take two Enterprise-Level Certifications examinations:
    • Amazon AWS *Cloud-Computing DSTI Chair*
      Preparation for AWS Certified Solutions Architect – Associate
    • SAS Institute *The SAS ecosystem DSTI Chair*
      Preparation to SAS Enterprise Miner certification

Classes for this Applied MSc programme are offered on a full-time basis from Data ScienceTech Institute campuses (around 5hrs a day).
If you are already employed by a French company and taking a sabbatical, tuition fees may be covered by the “Congé Individuel de Formation” scheme (see with your HR department).

The 600 hours of tuition are shared with the Executive MSc cohorts, as this Applied MSc programme constitutes their first year.

As a DSTI Applied MSc student and upon completion of the programme (600 hours + Engineering Project + Work Placement), you may wish to continue your specialisation studies. In this case, you will have the opportunity to join the Executive MSc programme of your choice, either MSc Executive Big Data Analyst or MSc Executive Data Scientist Designer according to your profile, career prospects and expectations.

If you are NOT a citizen and passport holder of a European Economic Area (EEA) country, Andorra or Monaco, you will be required to apply for a long-stay student visa. Please carefully read the requirements on our “Visa Procedure” page.


This Applied MSc programme programme is composed of all the following modules*, which are actual hours of class presence (personal work is expected on top of these):

Information Systems
0hrs

  • Architecture (IS1)
  • Amazon AWS “Cloud-Computing DSTI Chair” **
    Preparation for AWS Certified Solutions Architect – Associate
    Big Data and Machine Learning on Amazon AWS
  • Software Engineering (IS2)
  • Analysis and Design of Complex Information Systems
    Relational Model – E/R modelling – LAPAGE method
    Applied UML – Composition Method
  • Databases (IS3)
  • The Extraction – Transform – Load Lifecycle into MapReduce/Hadoop with a focus on data quality – Part 1
    Advanced RDBMS techniques and their ETL processes and tools using MSSQL Server
  • Semantic Web (IS4)
  • Integrating Semantic Web technologies in Data Science developments
    Representing and querying web-rich data (RDF, SPARQL), Introducing Semantics in Data (RDFS, Ontologies), Tracing and following data history (VOiD, DCAT, PROV-O)
Applied Data Science & Big Data
0hrs

  • Foundations (DSBD1)
  • Applied Mathematics for Data Science
    Calculus – Linear Algebra – Trigonometry & Complex Numbers
  • Algorithmics for Data Science – Optimisation in Python and MATLAB
    Refreshers – Combinatorics and Complexity – Graph-based modelling & algorithms
  • Machine and Deep Learning (DSBD2)
  • Foundations of Statistical Analysis and Machine Learning
    Probabilities and distribution – Tests – Inference – Regression – Clustering
  • Advanced Statistical Analysis and Machine Learning
    CART and Random Forests and applications to Map/Reduce – Features Selection & Engineering
    Models Comparison & Competition
  • Introduction to Deep Learning with Torch
    Data representation and distributed representations, Universal Interpretation Theorem, Probabilistic Interpretation, backpropagation and stochastic gradient descent using Torch
  • SAS Institute “The SAS ecosystem DSTI Chair” **
    Preparation to SAS Enterprise Miner certification
    SAS and Hadoop – Bayesian Statistics using SAS – SAS Visual Analytics
  • MapReduce Ecosystem (DSBD3)
  • The Extraction – Transform – Load Lifecycle into MapReduce/Hadoop with a focus on data quality – Part 2
    Map/Reduce theory and the Hadoop ecosystem using Cloudera
    ELT approches and tools for Hadoop (Impala)
Business & Industrial Applications
0hrs

  • Marketing (BIA1)
  • Integrating predictive models in CRM Systems: applications in MS Dynamics CRM
    Custom Developments, PMML, SAS® Solution for CRM
  • Communication (BIA2)
  • Exchanging with & Presenting to non-IT literates:
    Another form of Business English

    Popularising science – Business Writing, Reporting and Presentation coaching – Negotiation roleplays
  • Project Management (BIA3)
  • A guide to scientific publication & Operational Research
    “Reading” a scientific paper, understanding the strong link between Big Data, Data Science, software engineering and research, “operationalising” a paper
  • Finance (BIA4)
  • Modelling and predicting market fluctuations: High-frequency trading applications
    Stochastic approaches (Brownian Motion, Black-Scholes, Monte-Carlo, Markov Chains) to financial modelling
  • Modelling complex and risky economic systems with system dynamics
    Causal loop diagrams, Stock and flow diagrams, Non-linear algrebraic equations, Chaos Theory
Ethics & Law
0hrs

  • Data regulations (L1)
  • Data ownership and protection
    Private Data – Corporate Data

* Please note that course content and support technologies may vary when delivered according to job market needs and under the supervision of Data ScienceTech Institute Scientific Advisory Board.
** Provided you are not subject to any Sanction Programmes of the United States of America which would affect your rights to take these classes and/or examinations.

ENGINEERING PROJECT
0hrs

All students will be assigned an engineering project near the start of the programme. Students will conduct the project throughout the year until their classes finish and they go to their work placement or advanced project. This Engineering Project aims to apply all the knowledge and skills acquired in the different classes and to use DSTI professors as mentors and coaches throughout the year. Please note the Engineering Project may be given by a DSTI Professor affiliated Research Lab where Applied MSc students would act as Research Engineer Assistants.

Once the classes are finished, our Applied MSc students can choose between going for a work placement or working on an engineering project and dissertation. DSTI strongly encourages On-Campus students towards industrial placement and engineering projects for online education students ones.

Work Placement
0 months

On-Campus students are strongly encouraged to choose the 6-month work placement option (805hrs, 35hrs/week) and immerse themselves into a data science industrial environment. Finding a work placement opportunity is a student’s responsibility. DSTI provides active help, advice and support throughout its industrial and academic partners network however.


Advanced Engineering Project
0hrs*

Online education students will be tutored by a DSTI Professor in selecting a data science engineering problem for a given industrial application and write an engineering proposal, covering state-of-the-art literature, to propose a solution. On-Campus students may also choose this engineering project as an alternative to a work placement.

* 540hrs is an evaluated time, which accounts for the 200hrs already spent during the classes time and equivalent to four months of legal full-time work in France (35hrs per week), of requirement commitment to perform the project.


The following tuition fees are in effect for the OCTOBER 2017 entry and are subject to change for future years. Tuition fees for the MARCH 2018 entry will be published in October 2017.

MSc in Applied Data Science & Big Data Entry Tuition Fees
ON-CAMPUS STUDENTS AUTUMN 2017 12800 €
ONLINE STUDENTS AUTUMN 2017 7680 €

Non-EU and mature students (>28 y/o) may be required by the French Government to pay an extra 250€ for gaining medical coverage (social security). This obligation and extra cost is only collected by DSTI on behalf of and for the French Government.

Once admitted, you will be required to pay 10% of the programme fees for On-Campus students and 20% for Online Education, minus the 100€ already paid for your admission in order to secure your enrolment (wire transfer, debit/credit card).
This payment can only be refunded in case of exceptional circumstances for not enrolling the programme: visa not issued for international students or proven sudden change of financial situation.

The remaining fees can then be arranged in up to 5 bi-monthly instalments throughout the programme.

If you get a employer willing to sponsor you during a sabbatical, we will deal directly with your HR department regarding the fees proportion eligible to be covered in lieu.


Applicants with a 3-year
BA, BSc or BEng degree

3 years of work experience

Applicants with a 4-year
BA, BSc-BEng or MA-MSc-MEng degree

Work experience

Applicants with a 5-year
MA, MSc – MEng or Chartered Engineers

No particular conditions