Master Spécialisé en Ingénierie de données et Big Data

  • BAC+3 en ingénierie de données ou en sciences informatiques
  • Master en sciences informatiques ou équivalent
  • Etudes de dossier
  • 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:
  • Directeur de projet informatique
  • Directeur des études informatiques
  • Urbaniste – architecte fonctionnel du SI
  • Administrateur de bases de données
  • Project Management officer
  • Dataminer – Datascientist
  • Chief Data Officer
  • Data Protection Officer
  • Consultant informatique décisionnelle – big data

Data Management

  • 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
    Machine learning
  • Foundations of Statistical
    Analysis & Machine Learning Distributions – Descriptive & Inferential Statistics – Classification & Regression Trees

Data Science

  • 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
  • Operational Methodologies
  • 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++
  • 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

  • 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