Applied MSc in Data Engineering for Artificial Intelligence


apprenticeship study mode available


graduated students


students are satisfied with the programme


virtually 100% permanent employment within the 6 months after the internship

Updated on : 12/02/2022

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.

David WORMS - CEO of Adaltas

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.​

Bill SCHMARZO - CTO, IoT & Analytics Hitachi Vantara

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


780 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

780 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.


830 hours
Designed for those who want to study and work at the same time. Study online or on campus and acquire work experience in 24 months with our sandwich course.

SPOC – asynchronous mode

780 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.

Study pace

Accelerated programme

15 months
Choose to study on the Nice Sophia-Antipolis campus, in Paris or online. You will attend 9 months of classes (about 5 hours per day), followed by a mandatory 6-month internship.

Nominal programme

2 years
Designed for those who want to follow our programmes in 2 years, online (in France) or on our campuses:
• Year 1: 6-month course + 4 to 6 months of internship.
• Year 2: 6-month course + 6-month mandatory internship or apprenticeship (subject to conditions).


2 years
Designed for those who want to study and work at the same time. Study online (in France) or on campus and acquire work experience in 24 months with our sandwich course.


Every Thursday at 1 p.m., we hold group information sessions with our admissions director to answer all your questions.


  • 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

780-hours programme – 90 ECTS:

    • 730 hours of classes including 75 hours of DSTI Warm Up
    • 50 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):

Cloud-Computing – Amazon AWS

(50 hrs)

Preparation to AWS Certified Solutions Architect – Associate Certification

Web Engineering

(25 hrs)

Web development basis through HTML, CSS, JavaScript for Front End. An introduction to MVC programming with ASP.NET for backend and an overview of API development.

Software Engineering Part II

(25 hrs)

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

Software Engineering Part I

(25 hrs)

Object-Oriented Design, Design Patterns with Java programming

Cloud Computing – Microsoft Azure

(25 hrs)

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

Python Machine Learning Labs

(25 hrs)

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

Semantic Web technologies for Data Science developments

(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

(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

(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

(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

(5 hrs)

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

Big Data Ecosystem by Adaltas

(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

(25 hrs)

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

IT Project Management – PMP-PMI and Agile Approaches

(25 hrs)

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

CRM Data Management*

(25 hrs)

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

DevOps by Adaltas

(50 hrs)

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

Data Laws & Regulations – Philosophies, Geopolitics & Ethics

(25 hrs)

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


(25 hrs)

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

Warm up (75 hrs - 6 ECTS)

Fundamental applied mathematics

(10 hrs)

• Logical operators and quantifiers. Set theory. Applications, order and equivalence relations.
• Natural integers, relative numbers. Combinatorics: permutations, arrangements, combinations.
Factorial. Binomial formula.
• Real numbers. Accumulation points and theorem of bolzano-weierstrass.
• Real sequences. Limits. Monotonous and adjacent sequences. Accumulation value and cauchy
• Real functions of a real variable. Limits. Continuity. Derivability. Sense of variations. Mean value
theorem, rolle’s theorem. Rule of L’hospital. Inverse function and its derivative. Notion of convexity.

Introduction to Data Management

(5 hrs)

• Fundamentals of information systems analysis & design
• Functional dependency: the grounds for “keys”
• Relation model & relational algebra: the foundations of relational Databases
Management Systems (RDBMS) & SQL

Introduction to Computer Engineering

(5 hrs)

• Fundamentals of Computer Architecture & Operating Systems
• A journey from the Turing Machine to (not so) modern architectures, fundamentals of
hardware architectures
• What happens when you power-on a computer: a journey from the BIOS/UEFI via the kernel to the shell
• The illusion of multi-tasking: an engineering feat resting on operating systems

Computer Systems Labs

(10 hrs)

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

Excel Basics

(5 hrs)

• First steps with Excel
• Data types
• Charts and graphics

Data structure and applied Machine Learning using Python & R

(20 hrs)

• An introduction to data structures
• Data Structures for algorithmics: a benchmark on Python & R
• An introduction to data simulation
• Design structures for fitting well known libraries (glmnet, xgboost, sci-kit learn)

Introduction to AI Awareness

(5 hrs)

• Data science, ai, big data, cloud computing, machine learning, etc.: An honest and genuine review of meaning of terms behind the buzz
• The learning processes in living organisms
• The learning process using a computer
• The challenges for usable and deployable AI

Introduction to Computer Science

(2,5 hrs)

• Fundamentals of algorithmics and data structure design
• When a computer scientist does not need a computer: the curious case of the “algorithmic language”

Introduction to Networks

(2,5 hrs)

• Fundamentals of networks layers, routing networks layers, protocols,
address spaces and associated service with TCP/IP
•From the analogue to packetised networking: a journey from the PSTN
to the Internet
•The TCP/IP network suite – fundamentals by example

Clean IT

(10 hrs)

• Python in Google Colab
• Python local setup
• Python virtual environment
• IDE setup: Visual Studio Code
• Jupyter notebook overview
• R local setup
• Github
• Git locally
• Git with IDE (Visual Studio Code)web

Cambridge English

Cambridge English classes

(50 hrs)

English language fundamentals: vocabulary (general and specialised), grammar, conjugation,
and syntax, both oral and written comprehensions.
Evaluation: Linguaskill General Certification Exam assessing all four language skills - speaking, writing, reading and listening.

Applied Mathematics for Data Science

(25 hrs)

Calculus – Linear Algebra – Trigonometry & Complex Numbers

Big Data Processing with R

(25 hrs)

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

Artificial Neural Networks*

(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

(25 hrs)

Descriptive Statistics - Probability Theory – Applications using R

Deep Learning

(25 hrs)

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

* 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.

Types of evaluation

In order to obtain their degree, students must validate all the assessments required throughout the programme.

The evaluation procedures are as following:

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

Mandatory full validation

  • All our “Applied MSc” programmes can only be completed as “full validation”.
    For a “partial validation”, please refer to our ” continuous education “ offer.



DSTI students are required to have a Windows PC (Apple Macs are not an option) laptop with the following minimum specifications:

1.CPU: Intel Core i5 minimum (or AMD equivalent)

2.RAM: 8GB minimum, 16GB strongly recommended

3.Storage: 512GB minimum, 1TB strongly recommended. SSD is best, but expensive. A good and common alternative is a dual-drive system with 128GB/256GB SSD + 512 or 1TB magnetic. If magnetic only, it has to be a 7200rpm minimum, 5400rpm are too slow.

4.Graphic Card (GPU): an NVIDA GPU is a plus, but it’s not a mandatory requirement.

5.Operating System: any edition of Microsoft Windows, as DSTI will provide a Windows 10 Professional licence key once the classes are starting

6.Do not pay for MS Office 365, DSTI will also provide a licence once the classes are starting.

Without complying to these requirements, DSTI will not be able to provide its IT support.

Ability in English required

All classes are conducted exclusively in English, therefore a good level of proficiency in this language is required (equivalent to English level C1 on the European scale “CEFR”).

It is not necessary to include proof of language proficiency with your application. However, your level of English will be assessed at the admission interview to ensure that you are able to understand and follow the desired programmes. 

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

Regarding the apprenticeship study mode, all tuition fees are paid by the host company.

Accelerated course in 15 months:


Unique fee for all rhythms and modes of study

  • Full-time, apprenticeship, asynchronous
  • Sophia-Antipolis and Paris campuses (on-campus), synchronous online (off-campus), asynchronous online (SPOC)
  • Self-funded student fees are inclusive of VAT
  • These fees are valid for corporate & funding bodies, plus 20% VAT (see detailed fees)

Nominal 2-year course:

8,675* per year

Unique fee for all rhythms and modes of study

  • Full-time, apprenticeship
  • Sophia-Antipolis and Paris campuses (on-campus), synchronous online (off-campus)
  • Self-funded student fees are inclusive of VAT
  • These fees are valid for corporate & funding bodies, plus 20% VAT (see detailed fees)