Applied MSc in Data Engineering for Artificial Intelligence

85%

graduated students

90%

students are satisfied with the programme

100%

of permanent employment within the 6 months after the internship

The Data Engineer expert is one of the most important job in the Big Data Industry

The Applied MSc in Data Engineering for Artificial Intelligence1 programme, with its two entries in autumn and spring, will provide you the essentials to explore the world of Big Data and to build data infrastructures. This programme is designed to open your career to these Big Data Engineering jobs all industries are looking for.

There is an important shortage of big data experts and companies in various sectors suffer from this. Data Engineering specialists are in high demand and enterprises know the real value of their roles. In France-Benelux, we have seen a big increase in the demand for skilled people in data engineering in both start-ups and world leading companies. For every data scientist there is a need for at least two experts in data engineering.

A data engineer role is a highly technical position and requires skills such as programming, mathematics and computer science.

3IA label

Once your studies and professional experience are completed, your achievements will be assessed by our Graduation Committee. If successful, you will be able to obtain the degree “Expert en Sciences des Données” (Experts in Data Science). DSTI is proud of its Applied MSc in Data Analytics, Applied MSc in Data Engineering for AI and Applied MSc in Data Science & AI to have been fully accredited at Master’s level by the French Government via the RNCP mechanism. The RNCP “Répertoire National des Certifications Professionnelles” is a government recognition mechanism dedicated to scrutinising programmes’ suitability for the work market. A RNCP title rewards specific needs in terms of skills and knowledge transfer for immediate employability, which is the heart of DSTI philosophy.

Data engineering experts are rare and in greater demand than data scientists.

Data is the energy that powers the digital transformation. The developers consume it in their applications. Data Analysts search, query and share it. Data Scientists feed their algorithms with it. Data Engineers are responsible for setting up the value chain that includes the collection, cleaning, enrichment and provision of data. Some of the Data Engineers’ missions are to manage scalability, ensure data security and integrity, be fault-tolerant, manipulate batch or streaming data, validate schemas, publish APIs, select formats, models and databases appropriated to their exhibitions. From this work, flow the trust and success of those who consume and exploit the data.
And while the Harvard Business Review may have declared: Data Scientist: The Sexiest Job of the 21st Century, it is the data engineering team that allows them to shine.

The salaries

In Europe, we report a Mid-Level average salary between €59,000 and €83,000. The average salary for the Director of the Data engineering department varies between €100,000 and €174,000. Salaries depend on experience, skills, and location.

Average trainee remuneration in France: €1,300 per month

Study modes

On-Campus

805 hours
Choose to study either on the campus of Nice Sophia-Antipolis or Paris. You will follow 9 months of courses (around 5hrs per day), followed by 5 to 6 months of internship.

Off-Campus - full-time

805 hours
Designed for those who want to study full time. Study online for a period of 9 months, followed by 5 to 6 months of internship.

SPOC – asynchronous mode

805 hours
Designed for working professionals who wish to acquire new skills. Study online and at your own pace, for a flexible period up to 36 months.

Objectives

  • IT & Big Data architectures

    Learn how to understand the analysis, design, implementation & monitoring of IT & Big Data architectures

  • DevOps

    Discover the DevOps world and set up continuous integration architecture.

  • Machine and Deep Learning

    Leverage the most prevalent programming languages and their libraries for applied machine and deep learning

  • Hadoop or SPARK

    Learn how to architect and deploy highly distributed data and computation clusters such as Hadoop or SPARK

Programme Structure

805-hour programme – 60 ECTS:

    • 740 hours of classes including 75 hours of DSTI Warm Up
    • 65 hours of support sessions

850-hour internship (5 to 6 months) – 30 ECTS

The Applied MSc in Data Engineering for AI programme programme is composed of all the following modules*, which are actual hours of class presence (personal work is expected on top of these):

  • Warm Up

  • Distributed & Performance IT
    10 ECTS
  • Data management
    8 ECTS
  • Operational Methodologies
    4-6 ECTS
  • Data Science
    4-8 ECTS

Applied mathematics & data structures

(5 days)

  • Mathematic fundamentals review, data structures for algorithmics on Python Modeling elements
    (all pre-calculus level)
  • Data Structures for algorithmics: a benchmark on Python & R
  • Design structures for fitting well known libraries (glmnet, xgboost, sci-kit learn)

Introduction to networks

(1 day)

  • Fundamentals of packet networking, routing networks layers, protocols, address spaces and associated service with TCP/IP

 

Introduction to IT systems

(3 days)

  • Fundamentals of Computer Architecture & Operating Systems

Introduction to computer science

(1 day)

  • Fundamentals of programming

Computer systems

(5 days)

  • Introduction to OS architecture
  • Introduction to DOS/Power shell command
  • Introduction to bash command /scripting
  • Use case on a Linux web server

Cloud-Computing – Amazon AWS
3 ECTS

(50 hrs)

Preparation to AWS Certified Solutions Architect – Associate Certification

Java & Scala programming
1 ECTS

(25 hrs)

Java for Map Reduce in Hadoop & Scala for SPARK

Software Engineering Part II
1 ECTS

(25 hrs)

Fundamentals of algorithmics & data structures using object-oriented programming – Applications in C++ & Python

Software Engineering Part I
1 ECTS

(25 hrs)

Review of programming and memory management fundamentals, introduction to Microsoft .NET environment – Applications in C & C#

Cloud Computing – Microsoft Azure
2 ECTS

(25 hrs)

Comparative overview of with Amazon AWS – Focus on Azure Services specific to data lakes and data pipelines

Python Machine Learning Labs
1 ECTS

(30 hrs)

Data structures – Cleaning & preparation – Pandas – Matplotlib – Scikit-learn – OpenCV – Python & Flask – Keras – Numpy

Semantic Web technologies for Data Science developments
1 ECTS

(25 hrs)

Representing and querying web-rich data (RDF, SPARQL) – Introducing Semantics in Data (RDFS, Ontologies) – Tracing and following data history (VOiD, DCAT, PROV-O)

Data Wrangling with SQL
1 ECTS

(25 hrs)

Fundamentals of Relational Model & Databases - Relational Algebra – Advanced SQL queries - Stored procedures & triggers (T-SQL), Dynamic SQL – Applications with Microsoft SQL Server

Datawarehousing & ETL
1 ECTS

(25 hrs)

Using Microsoft SQL Server: stand-alone and cluster deployments, design and implementation of a datawarehouse - Structing an Extract, Transform, Load process – Applications with Microsoft SQL Server

Data Pipeline Part I & II
2 ECTS

(50 hrs)

XML dataflow, DTD & Schemas, XLS Transformation, JSON & Transformations – Cloud-based solutions with Glue in AWS & AWS Kinesis – Open-source solutions with Apache Kafka & Beam

Document Databases – NoSQL
– Part 2
2 ECTS

(10 hrs)

Fundamentals of MongoDB Databases, Collection and Document – Advanced MongoDB queries, MongoDB aggregations, MongoDB data architecture – Applications with MongoDB and Robo3T

The Hadoop & Spark Ecosystem
2 ECTS

(50 hrs)

HDFS – Scheduling & resources management – Workflow management & ETL – Dataflow management - Scalable Enterprise Serial Bus – Realtime processing with SPARK – Machine Learning – Data Exploration & Visualisation

Graph Databases – NoSQL
– Part 1
1 ECTS

(25 hrs)

Preparation to Neo4j Certification – Modelling a graph-based problem, implementation with the Neo4j database

IT Project Management – PMP-PMI and Agile Approaches
1 ECTS

(25 hrs)

PMBOK (PMI) – Agile Approaches – Kanban – Quality Metrics

CRM Data Management*
2 ECTS

(25 hrs)

Preparation of certification "Microsoft Power Platform Functional Consultant (PL-200)"

DevOps & Continuous Integration
3 ECTS

(50 hrs)

The DevOps toolbox: Nagios, Consul, Docker, Ansible, GitHub – Continuous Integration with Jenkins & Kubernetes

Data Laws & Regulations – Philosophies, Geopolitics & Ethics
1 ECTS

(25 hrs)

EU & USA approaches – GDPR – Safe Harbour & Successors – Common Law vs Code Law

Cybersecurity
2 ECTS

(25 hrs)

System Security Design Patterns – Infrastructure security – Data at-rest and in-transit encryption – Code safety

* The student chooses between “CRM Data Management” or “Artificial Neural Networks”.

Applied Mathematics for Data Science
1 ECTS

(25 hrs)

Calculus – Linear Algebra – Trigonometry & Complex Numbers

Big Data Processing with R
2 ECTS

(25 hrs)

Import and manipulate very large datasets with R – Best data structures selection – Data Transformation – Visualisation – Exploring and modelling

Artificial Neural Networks*
2 ECTS

(25 hrs)

Perceptron’s layers, weights, biases – Hyperparameter – Activation and cost functions – Review of optimization algorithms – Backpropagation – Learning mechanism – Classification & regression – Applications in Python using TensorFlow

Foundations of Statistical Analysis & Machine Learning Part I
2 ECTS

(25 hrs)

Probabilities and distribution – Descriptive Statistics – Visualisation – Applications using R

Deep Learning
2 ECTS

(25 hrs)

Recurrent Neural Networks, LSTM, Residual Networks, Computer Vision & NLP – Deep Learning on GPU – Application using Python & PyTorch

* The student chooses between “CRM Data Management” or “Artificial Neural Networks”.

* 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/Applied Projects

All students will be assigned engineering projects included in the majority of the modules. 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.

Resources and tools available for students

  • Azure for Education : it aims to provide students with Microsoft software design, Microsoft developer tools, Cloud Computing Access and learning resources. Students will receive a $100 voucher.
  • Support 5/7 “Zendesk”
  • O’Reilly : full-time students will have free access for one year and SPOC students for three years.
  • AWS Educate
  • IBM Academic Initiative
  • SAS license : only for students enrolled in the SAS course.
  • Adaltas “Cluster Access” : only for students enrolled in the Hadoop & Spark and Data Pipeline courses.

Careers Perspectives

DSTI Warm Up - Be ready!

data sciencetech institute course

The Applied MSc programmes include preparatory courses to get you up to speed on skills required to start leveraging the different topics. For the Applied MSc in Data Engineering for Artificial Intelligence, the DSTI Warm Up lasts 3 weeks (75 hours) and can be followed on-campus or online.

Tuition Fees

Early Bird Discount

Waiver of 5% of the total tuition fee

Candidates applying to the full-time mode and admitted for the Autumn 2021 entry are eligible. The discount is only available for a limited time.

Apply Now
  • Students living in France
  • Students living outside of France
15,000

On-Campus

Choose to study on the campus of Sophia-Antipolis or Paris.
  • Full-time
  • Nice Sophia-Antipolis & Paris Campus
13,500

Off-Campus France

Study From home or anywhere
  • Full-time
  • Live classes & synchronous
15,000

SPOC

Study from home or anywhere at your own pace
  • At your own pace & asynchronous
  • Personalised learning path
15,000

On-Campus

Choose to study on the campus of Sophia-Antipolis or Paris.
  • Full-time
  • Nice Sophia-Antipolis & Paris Campus
11,250

Off-Campus International

Study From Home or anywhere
  • Full-time
  • Live classes & synchronous
15,000

SPOC

Study from Home or anywhere at your own pace
  • At your own pace & asynchronous
  • Personalised learning path