• No products in the cart.

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.

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 Analystor 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.

MSc tuition cycle


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):



  • 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)



  • 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)



  • 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



  • 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.



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.

Course Curriculum

No curriculum found !

Course Reviews


  • 1 stars0
  • 2 stars0
  • 3 stars0
  • 4 stars0
  • 5 stars0

No Reviews found for this course.

Copyright © 2016-18 Fusion Analytics World | The Leading Digital Platform for Research & Analytics