MSc in Applied Data Science & Big Data

Applied MSc in Data Science
Discover the heart of Artificial Intelligence
Nice Sophia-Antipolis Campus – Paris Campus

The most applied
full-time MSc


A total of
of tuition

Tuition fees

Tuition fees
Online education**

*On-Campus self-funded students are normally exempt of French/EU VAT collection
**Off-Campus fees are exclusive of any local taxes (e.g. VAT, Sales Tax, etc.) which may apply in the students’ country of residence

Discover the programme structure Autumn 2018 entry – Tuition Fees

Programme information

This 6-month of classes and 6-month internship Applied MSc programme, with its two entries in Autumn and Spring, is a programme designed to bring students to the scientific heart of Data Science and Artificial Intelligence.

This programme is “depth-first” in applied mathematics and their implementation, led by Professors from the “French School of Mathematics”. It aims to give students a deep understanding of the main scientific grounds for artificial intelligence techniques, centred on modelling and then implementing rather than surveying data science APIs & frameworks.

Prospective students seeking for an IT-focused, “breadth-first” through programming frameworks, should be looking at our Applied MSc in Data Engineering programme instead.

Read our infographic to make the right choice between the Applied MSc in Data Science and Data Engineering.

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 “Engineering Projects” (see below) and followed by a 6-month work placement.

In this Applied MSc programme, you will:

  • Sharpen your applied mathematics for data science and artificial intelligence;
  • Focus your learning curve on understanding the heart of artificial intelligence algorithms;
  • Operationalise your scientific skills with the analysis, design, implementation & monitoring of IT & Big Data architectures;
  • Combine science and technology in application courses & projects requiring both, for dealing with industry-grade data science;
  • Get awareness of IT project management and 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:

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 currently in employment and/or can benefit from third-party financing due to lifelong education rights, please get in touch with us to check what could be organised.
We are kindly reminding you that our Applied MSc programme is delivered in full-time only. As such, you must be available to follow all classes and projects.
Should you be looking for a part-time programme allowing you to work and study, make sure to check the SPOC MSc in Applied Data Science & Big Data page.

As a DSTI Applied MSc student and upon completion of the programme (600 hours + Engineering Projects + Work Placement + Industrial Certifications), you may wish to continue your specialisation studies. In this case and upon graduation, you will have the opportunity to join our Advanced MSc in Artificial Intelligence programme.

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

Study Mode Available

2 Enterprise-Level Certifications

Amazon AWS *Cloud-Computing DSTI Chair*
Preparation for AWS Certified Solutions Architect – Associate

AWS Certified Solutions Architect - Associate

SAS Institute *The SAS ecosystem DSTI Chair*
Preparation for SAS Enterprise Miner certification

Data ScienceTech Institute SAS Gold Partner Logo

DSTI Student’s Testimonials

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

  • Distributed Architecture (IS1)
  • Amazon AWS “Cloud-Computing DSTI Chair” **
    Preparation for AWS Certified Solutions Architect – Associate
  • Software Engineering (IS2)
  • Computer Science & IT
    Classic Design & Programming
    Object-Oriented Design & Programming
  • Data Management (IS3)
  • Data Wrangling with SQL
    Advanced SQL queries, dynamic SQL, stored procedures & triggers
  • Semantic Web for Data Science
    Representing and querying web-rich data (RDF, SPARQL), Introducing Semantics in Data (RDFS, Ontologies), Tracing and following data history (VOiD, DCAT, PROV-O)
Core Data Science & Artificial Intelligence

  • Foundations (DSBD1)
  • Applied Mathematics
    Calculus – Linear Algebra – Trigonometry & Complex Numbers
  • Continuous Optimisation (Mathematics)
    Critical points, multiple variables function optimisation, gradient methods, constraint-based optimisation with Lagrange Multiplier
  • Metaheuristics Optimisation (Computer Science)
    Applied Numerical Optimisation and MSDO – Constrained and unconstrained, linear and non-linear, genetic algorithms (Particle Swarm Optimisation, Simulated Annealing, etc.)
  • Data Science & Artificial Intelligence (DSAI2)
  • 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
  • Artificial Neural Networks
    Data representation and distributed representations, Universal Interpretation Theorem, Probabilistic Interpretation, backpropagation and stochastic gradient descent
  • SAS Institute “The SAS ecosystem DSTI Chair” **
    SAS /Base & SAS/STAT
    Preparation for SAS Certified Predictive Modeler Using SAS Enterprise Miner 14
Applied Data Science & Artificial Intelligence

  • Practical Data Science & Artificial Intelligence (ADSAI1)
  • Survival Analysis using R
    Probabilistic description of Survival data, parametric, non-parametric and semi-parametric (Cox model) statistical methods. Applications to Big Data with penalised Cox regression
  • Deep Learning with Python
    Introduction to PyTorch, – Deep Learning, Neural Architectures and their applications – Neural Network training on a GPU (practice).
  • Time and behaviour modelling (ADSAI2)
  • Time-Series Analysis using SAS
    Forecasting Using SAS Software: A Programming Approach (SAS/ETS)
  • Agent-Based Modelling for population behaviour
    Modelling objectives, model types, matching modelling approaches to studies objectives, ODD protocol, ABM objectives and components
  • Distributed Computing for Data Science (ADSAI3)
  • The Hadoop & Spark ecosystem
    HDFS, scheduling & resources management
    Workflow management & ETL, Dataflow management, Scalable Enterprise Serial Bus
    Realtime processing, Machine Learning, Data Exploration & Visualisation
Management, Ethics & Law

  • Data regulations (MEL1)
  • Data ownership and protection laws and regulation
    Private Data – Corporate Data
    EU Data Protection Act, GDPR, US-EU Data Transfers regulations
  • Project Management (MEL2)
  • Project Management: PMP-PMI and Agile Approaches
    PMBOK (PMI) – Agile Approaches – Kanban

* 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 engineering projects attached to courses of programme. Students will conduct projects throughout the year until their classes finish and they go to their work placement . These Engineering Projects aim to apply all the knowledge and skills acquired in the different classes and to use DSTI professors as mentors and coaches throughout the year. Some of these projects may come from applied research work done by our Professors affiliated to a research lab.

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

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 expressed exclusive of any taxes & are in effect for the Autumn 2018 entry and are subject to change for future years. Tuition fees for the Spring 2019 entry will be published in Autumn 2018.

Off-campus students tuition fees may be subject to the local taxes of your country of residence. On-Campus self-funded students are normally exempt of French/EU VAT collection.
In any case, please get in touch with us to check which scenario applies.

Applied MSc in Data ScienceEntryTuition Fees (exclusive of any potential taxes)
ON-CAMPUS STUDENTSAutumn 201813,500€
ONLINE STUDENTSAutumn 20187,500€
SPOC STUDENTSAutumn 201813,500€

When on-campus, 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 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. Any refund due to visa refusal is exempt if the refusal if due to financial reasons / insufficient funding.

The tuition Fees are paid in 3 instalments, every two months in advance (w/exc. of the initial 10% (on-campus) or 20% (off-campus) down payment for securing your enrolment).

Should an employer be willing to sponsor your Applied MSc matriculation during a sabbatical, we would deal directly with your HR department regarding the fees proportion eligible to be covered in lieu. Please note that French/EU VAT collection may apply to fees paid by your sponsor.

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