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DSTI Applied MSc in Data Science & Artificial Intelligence
Apprenticeship
apprenticeship study mode available
(in France only)
85%
graduated students
90%
students are satisfied with the programme
100%
virtually 100% permanent employment within the 6 months after the internship
Updated on : 05/22/2023
Parlons de Data Science & de l'intelligence artificielle !
Notre programme Applied MSc in Data Science & Artificial Intelligence 1 avec ses deux entrées en automne et au printemps,vous permettra d’acquérir une compréhension approfondie des principaux fondements scientifiques des techniques d’intelligence artificielle, centrée sur la modélisation puis la mise en œuvre plutôt que sur l’étude des API et des cadres de la science des données. Ce programme d’ Applied MSc est un “approfondissement” des mathématiques appliquées et de leur mise en œuvre, dirigé par des professeurs de l’École française de mathématiques”.
1 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 Science & AI and Applied MSc in Data Engineering for 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.
Le Data Scientist, un emploi classé comme le "plus sexy du 21e siècle".
Infographics
Le Data Scientist, un emploi classé comme le “plus sexy du 21e siècle”par la Harvard Business Review, a maintenant (heureusement) dépassé son stade de“hype” stage. En cette fin de premier quart du 21e siècle, , les employeurs savent bien ce qu’est un Data Scientist, c’est-à-dire un bon mathématicien appliqué capable de programmer un ordinateur pour des preuves de concept (PoC). Ces PoCs sont ensuite industrialisés par leurs collègues axés sur l’informatique les Data Engineers et habilement exploités à l’échelle de l’organisation par les Data Analysts pour servir les décideurs..
Bien qu’ils forment la plus petite population en demande d’emplois centrés sur les données (#1 Data Engineer,#2 Data Analyst, #3 Data Scientist), ls deviennent rapidement des postes très rémunérateurs parmi l’éventail depostes établis qui devraient connaîtreune demande croissante d’ici 2022 (“The Future of Jobs Report“, The World Forum).
Les salaires
En Europe, nous rapportons un salaire moyen de niveau intermédiaire compris entre € 55 000 et € 80 000.
Rémunération moyenne d’un stagiaire en France: 1,300€ par mois
Modes d'étude
Mode "campus"
Choisissez d'étudier sur le campus de Nice Sophia-Antipolis ou de Paris. Vous suivrez 6 mois de cours (environ 5h par jour), suivis de 6 mois de stage.
Off-Campus – full time
840 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.
En ligne (temps plein)
Conçu pour ceux qui veulent étudier à temps plein. Étudier en ligne pendant une période de 6 mois, suivie de 6 mois de stage.
SPOC - Asynchrone
Conçu pour les professionnels en activité qui souhaitent acquérir de nouvelles compétences. Étudiez en ligne et à votre propre rythme, pour une période flexible pouvant aller jusqu'à 36 mois.
Study pace
Ce programme Applied MSc programme est disponible dans les modes d'études suivants
Je suis un étudiant
Je recherche à compléter mon niveau actuel, avec des programmes niveau licence ou master.
J'ai moins de 30 ans.
Apprendre
Je suis un professionnel
Je cherche à améliorer mes compétences et/ou à réorienter ma carrière au niveau master.
J'ai 30 ans ou plus.
Upskill
Formation Initiale - Modes Disponibles
| Mode | Durée | On-Campus |
Online (Off-Campus) |
Stage |
|---|---|---|---|---|
|
2 ans |
Oui (fuseau horaire compatible) |
Optionel en 1re Année Obligatoire, 6 mois en 2e Année |
||
|
14 à 24 mois |
Oui (en France) |
N/A (apprentissage) |
Formation Continue - Modes Disponibles
| Mode | Durée | On-Campus |
Online (Off-Campus) |
Online Asynchrone |
Stage |
|---|---|---|---|---|---|
|
9 mois |
Oui (fuseau horaire compatible) |
Non |
Obligatoire, 6 mois
|
||
|
Flexible |
Occasionnellement, si la disponibilité de l'étudiant et le calendrier des cours le permet |
Occasionnellement, si la disponibilité de l'étudiant et le calendrier des cours le permet |
Oui |
6 mois, obligatoire (incluant la possibilité de le réaliser chez l'employeur du moment). |
COLLECTIVE INFORMATION MEETINGS
Every Thursday at 1 p.m., we hold group information sessions with our admissions director to answer all your questions.
Discover DSTI for a day of immersion in data!
You can come and discover the DSTI courses during one day, for this you just have to fill in the form below and we will contact you to agree on a date.
The Applied MSc in Data Science and AI in apprenticeship study mode (only in France)
Ce que j'apprend ?
Structure du programme
Programme de 700 heures – 60 ECTS
Stage de 850 heures (6 mois) – Le stage équivaut à 30 ECTS
Ce programme de MSc appliqué est composé de tous les modules* suivants, qui correspondent à des heures réelles de présence en classe(un travail personnel est attendu en plus de ceux-ci):
Applied Mathematics for Data Science
3 ECTS
(25 hrs)
Calculus – Linear Algebra – Trigonometry & Complex Numbers
Foundations of Statistical Analysis & Machine Learning Part II
4 ECTS
(40 hrs)
Tests – Estimators – Confidence Intervals – Inference – ANOVA – PCA – Simple Linear Regression – Applications using R
Continuous Optimisation
4 ECTS
(25 hrs)
Critical points – Multiple variables function optimisation – Gradiant methods – Constraint-based optimisation with Lagrange Multiplier – Applications using Python
Artificial Neural Networks*
4 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 and Machine Learning Part 1
3 ECTS
(25 hrs)
Descriptive Statistics - Probability Theory – Applications using R
Time-Series Analysis
3 ECTS
(25 hrs)
Mathematical foundations – Applications with R including using Neural Networks
SAS "The SAS Ecosystem DSTI Chair"
3 ECTS
(25 hrs)
Preparation to SAS BASE Certification - SAS Base programming, application with SAS STATS
Amazon AWS
“Cloud-Computing DSTI Chair”
4 ECTS
(50 hrs)
Preparation to AWS Certified Solutions Architect – Associate Certification
Software Engineering Part II
3 ECTS
(25 hrs)
Fundamentals of algorithmics & data structures using object-oriented programming – Applications in C++ & Python
Big Data Ecosystem by Adaltas
4 ECTS
(50 hrs)
HDFS – Scheduling & resources management – Workflow management & ETL – Dataflow management - Scalable Enterprise Serial Bus – Realtime processing – Machine Learning – Data Exploration & Visualisation - Application using an real, industry-grade Hadoop & Spark cluster
Software Engineering Part I
2 ECTS
(25 hrs)
Fundamentals of algorithmics & data structures using classical design & programming – Applications in C
Python Machine Learning Labs
4 ECTS
(25 hrs)
Data structures – Cleaning & preparation – Pandas – Matplotlib – Scikit-learn – OpenCV – Python & Flask – Keras – Numpy
Data Wrangling with SQL
3 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
MLOps by Adaltas
4 ECTS
(50 hrs)
Introduction to DevOps, GitOps, DataOps, MLOps
Unit test in the context of Spark Data Engineering, CI/CD, artifact deployment to registries (docker, jar, notebooks, ..) GitOps and MLOps, probably Apache Liminal
Cloud and MLOps, Databricks plateform and MLFlow
Advanced Statistical Analysis & Machine Learning
4 ECTS
(35 hrs)
Multiple Linear Regression – CART and Random Forests and applications to their applications – Features Selection & Engineering – Models Comparison & Competition – Applications using R
Survival Analysis using R
4 ECTS
(25 hrs)
Statistical analysis of duration data - Applications using R
Inverse Problems & Data Assimilaton*
4 ECTS
(25 hrs)
Variational and sequential data assimilation – Identification of the initial condition, parameter estimation – Applications using Python
Semantic Web technologies for Data Science developments
4 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)
Statistical Analysis of Massive and High Dimensional Data
4 ECTS
(25 hrs)
Context for new uses of massive datasets (open data, social networks, Twitter…) – Review of conventional statistical methods (tests, regression, classification) and their (un)suitability massive datasets – Latest alternative statistical tools for analysing modern datasets – Implementation in realistic situations using R
Deep Learning
4 ECTS
(25 hrs)
Recurrent Neural Networks - LSTM - Residual Networks - Computer Vision & NLP – Deep Learning on GPU – Application using Python & PyTorch
Agent-Based Modeling
4 ECTS
(25 hrs)
Solving complex problems using ABM – Comparisons with statistical – Markov and system dynamics approaches – ABM validation for “trustability”
Graph Databases – NoSQL – Part 1
4 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
2 ECTS
(25 hrs)
PMBOK (PMI) – Agile Approaches – Kanban – Quality Metrics
Data Laws & Regulations – Philosophies, Geopolitics & Ethics
2 ECTS
(25 hrs)
EU & USA approaches – GDPR – Safe Harbour & Successors – Common Law vs Code Law
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
criterion.
• 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
APPRENTICESHIP STUDENTS ONLY
(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.
* Veuillez noter que le contenu des cours et les technologies de soutien peuvent varier lorsqu’ils sont dispensés en fonction des besoins du marché du travail et sous la supervision du conseil scientifique de Data ScienceTech Institute.
** Pour autant que vous ne soyez pas soumis à un programme de sanctions des États-Unis d’Amérique qui affecterait vos droits à suivre ces cours et/ou à passer ces examens.
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:
- Multiple choice questionnaires in negative or zero mathematical expectation (MCQs)
- DSTI internal Exam
- Where applicable, external industrial certification exam
- 6 month internship in the data field, an evaluation form is sent to the internship tutor
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.
Mandatory full validation
- All our “Applied MSc” programmes can only be completed as “full validation”.
- For a “partial validation”, please refer to our “certifying professional training courses” offer.
Admission Criteria
Field: Theoretical or Applied Sciences, Engineering and Technology, Economics.
Field: Theoretical or Applied Sciences, Engineering and Technology, Economics.
Field: Theoretical or Applied Sciences, Engineering and Technology, Economics.
DSTI accepts the two international & standardised aptitute exams, GMAT & GRE, as subsitutes for our own entry exam.
Our expectations are:
- GMAT: a minimum global score of 650 and ideally greater or equal to 700.
- GRE: a minimum global score of 310 and ideally greater or equal to 320.
Prerequisites
IT MINIMUM REQUIREMENTS
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.
Perspectives de carrière
-
Data Scientist
-
Data Analyst vs Data Scientist
-
Artificial Intelligence Engineer
DSTI Warm Up - Soyez prêts !
Les programmes de Applied MSc comprennent des cours préparatoires pour vous mettre à niveau sur les compétences requises pour commencer à tirer parti des différents sujets. Pour le Applied MSc in Data Science and Artificial Intelligence, le DSTI Warm Up dure 10 jours (2 semaines) et peut être suivi sur le campus ou en ligne.
Tarification
Regarding the apprenticeship study mode, all tuition fees are paid by the host company.
Accelerated course in 15 months:
€
17,850*
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:
€
8,925*
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)