MSc in Applied Data Science & Big Data

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

The most applied
0-month
full-time MSc

Enterprise-Level
0
Certifications

A total of
0hrs
of tuition

Tuition fees
0
on-campus*

Tuition fees
0
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 2017 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 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 “Engineering Projects” (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:

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 our International Executive MSc in Applied Data Science & Big Data programme, delivered as a Small Online Private Course (SPOC).

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 Information Systems & Artificial Intelligence for Big Data Engineering 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.

MSc tuition cycle

Programme entries

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


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
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  • 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 Information Systems
    Refreshers in Computer Science: fundamentals of algorithmics & data structures using C & C++
    Analysis methodology, E/R model & LAPAGE method for data model design
  • Databases (IS3)
  • Data Wrangling backed with MS SQL Server
    Advanced SQL queries, stored procedures & triggers (T-SQL)
    Combining SQL and .Net code for complex ETL (SSIS)Dynamic Reporting and BI (SSRS)
  • 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
    Combinatorics and Complexity – Graph-based modelling & algorithms for discrete optimisation
    Introduction to continuous optimisation
  • 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
  • Artificial Neural Network and introduction to Deep Learning
    Data representation and distributed representations, Universal Interpretation Theorem, Probabilistic Interpretation, backpropagation and stochastic gradient descent
  • SAS Institute “The SAS ecosystem DSTI Chair” **
    Preparation for SAS Certified Predictive Modeler Using SAS Enterprise Miner 13
    SAS and Hadoop – SAS Visual Analytics – Base SAS
  • MapReduce Ecosystem (DSBD3)
  • The Hadoop & SPARK ecosystem
    HDFS, scheduling & ressources management
    Workflow management & ETL, Dataflow management, Scalable Entreprise Serial Bus
    Realtime, Machine Learning, Data Exploration & Visualisation
Business & Industrial Applications
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  • Marketing (BIA1)
  • Time-Series Analysis using SAS
    Forecasting Using SAS Software: A Programming Approach (SAS/ETS)
    Forecasting Using SAS Forecast Server Software
  • Operations (BIA2)
  • 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
  • Project Management (BIA3)
  • IT Project Management: PMP-PMI and Agile Approaches
    PMBOK (PMI) – Agile Approaches – Kanban
  • Social Sciences (BIA4)
  • Agent-Based Modelling for population behaviour
    Modeling objectives, model types, matching modelling approaches to studies objectives, ODD protocol, ABM objectives and components
  • Modelling complex and chaotic 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 laws and regulation
    Private Data – Corporate Data
    EU Data Protection Act, GDPR, US-EU Data Transfers regulations

* 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 PROJECTS
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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
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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 expressed exclusive of any taxes & 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.

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.

MSc in Applied Data Science & Big Data Entry Tuition Fees (exclusive of any potential taxes)
ON-CAMPUS STUDENTS AUTUMN 2017 12,800€
ONLINE STUDENTS AUTUMN 2017 7,680€

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, 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 10 monthly instalments throughout the programme.

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