Data ScienceTech Institute - MSc Executive Big Data Analyst

MSc Executive Big Data Analyst
Nice Sophia-Antipolis Campus – Paris Campus


The most innovative
0-year
MSc

Enterprise-Level
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Certifications

A total of
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hours

AVG Tuition/year*
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on-campus

AVG Tuition/year*
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online education

Discover the programme structure * Autumn 2017 entry – Tuition Fees

Programme information

This 2-year Executive MSc programme for opening your career to these Big Data Analytics jobs all industries are looking for.

And in France, with Sophia-Antipolis being the largest technology park in continental Europe and Paris the European capital of tech-startups, you will definitely have a lot of data to play with!

This two-year programme is organised like this (see chart on the right):

  • 1st year: Full-time classes followed by an internship
  • 2nd year: Full-time classes followed by an internship

In this Executive MSc programme, you will learn how to:

  • understand and use Information Systems for Big Data;
  • become an expert in the machine learning process using the most prevalent tools found in organisations;
  • integrate Big Data results into your specific day-to-day and corporate systems, manage a Big Data project, along with risk management;
  • get an overall understanding of the legal outcomes of your work, along with a global ethical thinking about 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
    • The IBM SPSS Ecosystem
      Preparation to IBM Certified Associate – SPSS Modeler Data Mining certification
    • SAS Institute *The SAS ecosystem DSTI Chair*
      Preparation to SAS Business Analyst certification

Classes at Data ScienceTech Institute are offered on a full-time basis for the first and second year (around 5hrs a day).
If you are already employed by a French company, tuition fees may be covered by the “Congé Individuel de Formation” scheme (see with your HR department).

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 do carefully read the requirements on our “Visa Procedure” page.


The first year of this Executive MSc programme is composed of all the following modules* in full-time, 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 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
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  • 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
    Stochastics 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 1
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All students will be assigned an engineering project nearby the start of the first year. 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 Executive MSc students would act as Research Engineer Assistants.

Once their first year classes are finished, our Executive 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
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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 second year of this Executive MSc programme is composed of all the following modules* in Full-Time, which are actual hours of class presence (personal work is expected on top of these):

Advanced Information Systems
0hrs

  • Security (AIS1)
  • Standard mechanisms of IT security in a corporate environment
    Microsoft Active Directory Fundamentals, Firewalling, Single-Sign-on techniques
Big Data Tools
0hrs

  • Reporting & Business Intelligence (BDT1)
  • Reporting and BI: foundations of data visualisation
    Overview of Tableau, SAP Lumira, SAS Visual Analytics, MS SQL Server Reporting and Excel
  • Statistical analysis software (BDT2)
  • Advanced Excel for Statistical analysis
    Data Integration and cleaning, PivotTables and Solver for regression analysis
  • Hands-on week 1: Leveraging SAS Base & Stats
    Preparation to the SAS Business Analyst Certification **
  • Machine and Deep Learning Enterprise Tools (BDT3)
  • The IBM SPSS Ecosystem **
    Use-cases of IBM SPSS main data science customers’ activities
    Preparation to the IBM SPSS Modeler Certification
    IBM DB/2 and SPSS integration with IBM SPSS Collaboration and Deployment Services
  • Hands-on week 2: Leveraging IBM SPSS
    SPSS Statistics Level 1 v2 Programme
  • Hands-on week 3: Leveraging Built-in Machine Learning Tools
    Using Oracle Data Miner & MSSQL BI
  • Hands-on week 4: Modelling and Generating Data out of Complex Systems
    Applications using Wolfram Mathematica
  • Machine and Deep Learning Open-Source Tools (BDT4)
  • Open-source week 1: Machine Learning applications with R
    The R Machine Learning & Statistical Learning toolbox
  • Open-source week 2: Machine Learning applications with Python
    The sci-kit learn toolbox
  • Cloud and Machine Learning week
    Machine Learning applications with Amazon AWS Machine Learning & MS Azure Machine Learning
Advanced Business & Industrial Applications
0hrs

  • Project Management (ABI1)
  • IT Project Management: PMP-PMI and Agile Approaches
    PMBOK (PMI) – Agile Approaches – Kanban
  • Risk management (ABI2)
  • Dealing with legacy yet well-performing IT: cloud computing has not yet killed the mainframe
    Working in collaboration with an IBM Mainframe ecosystem:mainframe OS, programming style and methodologies. Overview of COBOL, PL/I and the Job Control Language (JCL).
  • Business & Management Specifics (ABI3)
  • Data and models integration
    Latest techniques in Data and Model exchanges for ML integration in Corporate Systems
  • Geo-targeting: combining clustering, GIS and prediction to reach a customer
    Applying Machine Learning techniques on spatial and operationnal data
  • An introduction to Sentiment Analysis for CRM customer feedback
    Social Networks data collection techniques, text-mining for sentiment analysis, data integration
  • Distributed storage and processing of large unstructured datasets (ABI4)
  • From the HADOOP ecosystem: datawarehousing with Hive & Machine Learning with Mahout
  • Specific numerical methods for scientific data processing (ABI5)
  • Latest techniques for Feature Selection, Feature Extraction, multifactor dimensionality reduction & Self Organising Map
Ethics & Law
0hrs

  • Business Ethics (L2)
  • Private and sensitive data management – Customer Profiling
    Legal frameworks for handling directly or indirectly acquired data, opt in, customers’ (and citizen) acceptance rates for profiling, regulation authorities, most particularly in Europe and in the USA
  • Commercial Law (L3)
  • French
    European Union
    International
    Pitfalls and loopholes to avoid and best practises to acquire.
    Contract over multiple legal jurisdictions
  • Open-Data (L4)
  • Legal framework, usability (and re-) of public open-data in commercial products, sustainability
  • eReputation (L5)
  • Legal frameworks and possible defence mechanisms for corporate eReputation

* 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 2
0hrs

All students will be assigned an engineering project nearby the start of the second year, which could also be continuation of the first-year project, if possible. 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 Executive MSc students would act as Research Engineer Assistants.

Once their second year classes are finished, our Executive 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 work placement and Engineering Project for Online Education students. Online Education students will pursue a second Engineering Project or carry on their previous one should it be possible.

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 Executive Big Data Analyst Entry First Year Second Year Total Average(AVG) / Year
ON-CAMPUS STUDENTS AUTUMN 2017 12800 € 7000 € 19800 € 9900 €
ONLINE STUDENTS AUTUMN 2017 7680 € 4200 € 11880 € 5940 €

Non-EU and mature students (>28 y/o) may be required by the French Government to pay an extra 250€ per annum 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 bi-monthly instalments throughout the programme (5 per year).


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