Master

High Performance Data Analytics


Overview


The rapid growth of the Internet has led to the collection of vast amounts of data. Since analyzing these data requires a tremendous computing power, High Performance Data Analytics employs various parallelization techniques in order to reduce the analysis execution time. These parallelization techniques can then by applied to various domains such as High Performance Computing, Artificial Intelligence, and Big Data.

This track’s program covers the whole software stack of today’s high performance data analytics applications, from the internals of operating systems, and the foundation of parallel programming to High Performance Computing applications and AI techniques. The 2 years program is composed of mandatory courses that focus on core topics (operating systems, parallel programming, …) and optional courses that target either HPC applications (numerical simulation and modeling, …) or AI applications (Machine learning, data mining, …) The program has a focus on research, and aims to integrate students into research labs through projects and the master’s thesis, in order to prepare them for a PhD.

Language of instruction: French / English
ECTS: 120
Oriented: research
Duration: 2 years
Courses Location: Palaiseau


Educational objectives


The objective of this track is to master the foundations of parallel computing that allow to analyze massive amounts of data.

The core courses include architecture, operating systems, parallel programming, and an overview of domain applications such as high-performance computing, artificial intelligence or big data. A specific application domain can then be investigated further more with advanced courses.

The program also aims at developing the skills required to pursue a research career through research seminars and research-oriented projects.


Program structure


Obligatory courses:

  • Introduction to Architectures and Operating Systems (48 h, 4 ECTS)
  • Operating Systems for multicore (48 h, 4 ECTS)
  • Parallel and Distributed Algorithms (48 h, 4 ECTS)
  • Database management systems (48 h, 4 ECTS)
  • Introduction to machine learning (48 h, 4 ECTS)
  • Systems for big data (48 h, 4 ECTS)
  • A 6-month internship in a research environment (30 ECTS)
  • Reading group (48 h, 4 ECTS)
  • HPDA projects

Optional courses:

  • Advanced programming of multi-core architectures (48 h, 4 ECTS)
  • Architecure for big data (24 h, 2,5 ECTS)
  • Cloud computing infrastructure (48 h, 4 ECTS)
  • Infrastructures and platforms for distributed computing (24 h, 2,5 ECTS)
  • Large scale machine learning (24 h, 2,5 ECTS)
  • Programming with GPUs for deep learning (24 h, 2,5 ECTS)
  • Machine learning II (48 h, 4 ECTS)
  • Data visualization (48 h, 4 ECTS)
  • Stochastic simulation and Monte-Carlo methods (48 h, 4 ECTS)
  • Operational research: mathematical aspects and application (48 h, 4 ECTS)
  • Implementation of numerical methods (48 h, 4 ECTS)
  • Effective implementation of the finite element method (48 h, 4 ECTS)
  • Modeling and simulation of neutral particle transport (48 h, 4 ECTS)
  • Solving diffraction problems by integral equations (48 h, 4 ECTS)
  • Plasma and astrophysical system modeling (48 h, 4 ECTS)
  • Advanced numerical methods and high-performance computing (48 h, 4 ECTS)
  • Embedded Electronic Systems (24 h, 2,5 ECTS)
  • MPSOC Multiprocessors on a chip (24 h, 2,5 ECTS)

Laboratories involved



Career prospects


  • A graduate student from the master program will first acquire the skills to pursue a Ph.D. degree in Computer Science in a highly ranked University, including the IP Paris partner schools.
  • A graduate student will also acquire the background to join the R&D departments of the most innovative IT companies.

Institutional partners


  • École polytechnique
  • ENSTA Paris
  • Telecom Paris
  • Telecom SudParis

Admissions


Application guidelines for a master’s program at IP Paris

Academic Prerequisites

  • Year 1: Candidates for the Master program should hold at least a Bachelor degree in computer science or in applied mathematics and have an outstanding academic record.
  • Year 2: Admission can be granted directly in the 2nd year (M2), provided that necessary prerequisites have been met. Any student (from France or abroad) who has completed at least the first year of a Master program in computer science may apply.

Language prerequisites

  • Fluent french
  • English

Application timeline

Deadlines for the Master application sessions are as follows:
– First session: February 28, 2020
– Second session: April 30, 2020
– Third Session (optional): June 30, 2020 (only if there are availabilities remaining after the 2 first sessions)
Applications not finalized for a session will automatically be carried over to the next session.

You shall receive an answer 2 months after the application deadline of the session.
You can check your application status by logging in your candidate space.


Tuition fees


National Master: Official tuition fees of the Ministry of Higher Education, Research and innovation (2019-2020, EU students: 243 euros / Non-EU students: 3770 euros)


Contact


François TRAHAY
Email
Profile