MSc in Applied Data Science & Big Data

Applied MSc in Data Engineering
Nice Sophia-Antipolis Campus – Paris Campus

The other most applied
0-month
full-time MSc

Enterprise-Level
0
Certifications

A total of
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of tuition

Tuition fees
0
on-campus*

Tuition fees
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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 designed to open your career to these Big Data Engineering jobs all industries are looking for.

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:

  • Learn how to understand the analysis, design, implementation & monitoring of IT & Big Data architectures;
  • Leverage the most prevalent programming languages and their libraries for applied machine and deep learning;
  • Learn how to architect and deploy highly distributed data and computation clusters such as Hadoop, SPARK or Microsoft Orleans;
  • Discover the DevOps world and set up continuous integration architecture;
  • Be trained to and take two Enterprise-Level Certification examination:

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 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

Cloudera Certified Data Engineer
Preparation for Cloudera Certified Data Engineer

Cloudera Certified Data Engineer

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

AWS Certified Solutions Architect - Associate

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)
:

Data Management
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  • Data Bases
  • Relational Databases Management Systems
    Using MySQL & Microsoft SQL Server: stand-alone and cluster deployments, integration in software, ETL, persistence frameworks
  • Advanced SQL for Data Wrangling
    Complex joins & subqueries, stored procedures & triggers
  • NoSQL databases
    Key-value store, Document store, Graph database , hybrid approaches with Apache Cassandra
  • Big Data
  • The Hadoop Ecosystem
    HDFS, MR, YARN, SPARK
  • Data Pipeline
    Classic ETL solutions – Cloud-based solutions with AWS Data Pipeline & AWS Kinesis – Open-source solution with Apache Kafka & Beam
Data Science
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  • Machine learning
  • Foundations of Statistical Analysis & Machine Learning
    Distributions – Descriptive & Inferential Statistics – Classification & Regression Trees
  • Machine Learning with Python
    Language fundamentals & common frameworks for machine learning: NumPy, SciPy, scikit-learn
  • Machine Learning with R
    Language fundamentals, recursive and functionnal programming, data frames, common machine learning packages
  • Deep Learning
  • Deep Learning on GPU
    Recurrent Neural Networks, LSTM, Residual Networks
Distributed & Performance Programming
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  • Programming langages for Data Engineering
  • C & C++ for Distributed Computing
    Portable and scalable large-scale parallel applications using OpenMP & OpenMPI
  • Java & Scala programming
    Java for Map Reduce in Hadoop & Scala for SPARK
  • Microsoft .NET for Distributed Computing
    Task Parallel Library – Asynchronous programming – Orleans framework for distributed systems
  • Scientific Programming
    Fundamentals in Fortran & MATLAB, Fortran for R packages, MATLAB with C/C++
Operational Methodologies
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  • Information Systems
  • Design of Information Systems
    Algorithmics approaches to relational data modelling and object-oriented programming
  • DevOps
  • Software Engineering Project Management & Quality
    PMBOK (PMI) – Agile Approaches – Kanban – Quality Metrics – Unit & Integration testing
  • DevOps & Continuous Integration
    The DevOps toolbox: Nagios, Consul, Docker, Ansible, GitHub – Levaraging Visual Studio for DevOps – Continuous Integration with Jenkins & Kubernetes
  • Cybersecurity
  • Cybersecurity
    System Security Design Patterns – Network security – Data at-rest and in-transit encryption – Code safety – Application to blockchain technologies
Cloud & IT
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  • Cloud Computing
  • Amazon AWS & Microsoft Azure
    Preparation to AWS Certified Solutions Architect – Associate Certification – Comparative overview of Microsoft Azure
  • IT Fundamentals
  • Semantic Web
    Representing and querying web-rich data (RDF, SPARQL), Introducing Semantics in Data (RDFS, Ontologies), Tracing and following data history (VOiD, DCAT, PROV-O)
  • IT Foundations for Data Engineering
    Computer Architecture – Operating Systems & Virtualisation – Networking


* 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 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 EngineeringEntryTuition 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