Clean Compute Predict
Applied MSc in Data Science & Artificial Intelligence
85%
graduated students
90%
students are satisfied with the programme
100%
virtually 100% permanent employment within the 6 months after the internship
Let’s talk about Data Science & Artificial Intelligence!
The Applied MSc in Data Science & Artificial Intelligence 1 programme, with its two entries in autumn and spring, will enable you to get a deep understanding of the main scientific grounds for artificial intelligence techniques, centred on modelling and then implementing rather than surveying data science APIs & frameworks. This Applied MSc programme is “depth-first” in applied mathematics and their implementation, led by Professors from the “French School of Mathematics”.
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.
The Data Scientist, a job once classified as the “sexiest of the 21st century”
Infographics
The Data Scientist, a job once classified as the “sexiest of the 21st century” by the Harvard Business Review, has now (thankfully) passed its “hype” stage. In this end of the first quarter of the 21st century, employers are well-aware of what is a Data Scientist, i.e. a good applied mathematician who can program a computer for proof-of-concepts (PoC). These PoCs are then industrialised by their IT-focused Data Engineers colleagues and cleverly leveraged organisation-wide by Data Analysts to serve decision-makers.
Although forming the smallest population in-demand for data-centric jobs (#1 Data Engineer, #2 Data Analyst, #3 Data Scientist), they rapidly become high-earners present among the range of established roles that are set to experience increasing demand in the run up to 2022 (“The Future of Jobs Report”, The World Forum).
The salaries
In Europe, we report a Mid-Level average salary between €55,000 and €80,000.
Average trainee remuneration in France: €1,300 per month
Study modes
On-Campus
800 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
800 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
800 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
Programme Structure
800-hour programme – 60 ECTS:
- 730 hours of classes including 75 hours of DSTI Warm Up
- 70 hours of support sessions
850-hour internship (5 to 6 months) – 30 ECTS
The Applied MSc in Data Science & 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):
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
Applied Mathematics for Data Science
1 ECTS
(25 hrs)
Calculus – Linear Algebra – Trigonometry & Complex Numbers
Foundations of Statistical Analysis & Machine Learning Part II
1 ECTS
(40 hrs)
Tests – Estimators – Confidence Intervals – Inference – ANOVA – PCA – Simple Linear Regression – Applications using R
Continuous Optimisation
2 ECTS
(25 hrs)
Critical points – Multiple variables function optimisation – Gradiant methods – Constraint-based optimisation with Lagrange Multiplier – Applications using Python using TensorFlow
Artificial Neural Networks*
1 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
(25 hrs)
Descriptive Statistics - Probability Theory – Applications using R
Time-Series Analysis
2 ECTS
(25 hrs)
Mathematical foundations – Applications with R including using Neural Networks
SAS "The SAS Ecosystem DSTI Chair"
2 ECTS
(25 hrs)
Preparation to SAS BASE Certification - SAS Base programming, application with SAS STATS
* The student chooses between “SAS BASE” or “Inverse Problems & Data Assimilation”.
Amazon AWS
“Cloud-Computing DSTI Chair”
3 ECTS
(50 hrs)
Preparation to AWS Certified Solutions Architect – Associate Certification
Software Engineering Part II
1 ECTS
(25 hrs)
Fundamentals of algorithmics & data structures using object-oriented programming – Applications in C++ & Python
The Hadoop & Spark Ecosystem
2 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
1 ECTS
(25 hrs)
Fundamentals of algorithmics & data structures using classical design & programming – Applications in C
Python Machine Learning Labs
1 ECTS
(30 hrs)
Data structures – Cleaning & preparation – Pandas – Matplotlib – Scikit-learn – OpenCV – Python & Flask – Keras – Numpy
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
Advanced Statistical Analysis & Machine Learning
2 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
1 ECTS
(25 hrs)
Statistical analysis of duration data - Applications using R
Inverse Problems & Data Assimilaton*
2 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
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)
* The student chooses between “SAS BASE” ou “Inverse Problems and Data Assimilation”.
Statistical Analysis of Massive and High Dimensional Data
1 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
2 ECTS
(25 hrs)
Recurrent Neural Networks - LSTM - Residual Networks - Computer Vision & NLP – Deep Learning on GPU – Application using Python & PyTorch
Agent-Based Modeling
1 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
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
Data Laws & Regulations – Philosophies, Geopolitics & Ethics
1 ECTS
(25 hrs)
EU & USA approaches – GDPR – Safe Harbour & Successors – Common Law vs Code Law
* 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:
- 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.
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.
Careers Perspectives
-
Data Scientist
-
Data Analyst vs Data Scientist
-
Artificial Intelligence Engineer
DSTI Warm Up - Be ready!
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 Science and Artificial Intelligence, the DSTI Warm Up lasts 3 weeks (75 hours) and can be followed on-campus or online.
Tuition Fees
€ 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