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Applied MSc in Data Science & Artificial Intelligence

Apprenticeship

apprenticeship study mode available

85%

graduated students

90%

students are satisfied with the programme

100%

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

Updated on : 12/02/2022

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.

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 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

840 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

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.

Apprenticeship

890 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

840 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).

Apprenticeship

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.

COLLECTIVE INFORMATION MEETINGS

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

The Applied MSc in Data Science and AI in apprenticeship study mode (only in France)

Objectives

  • Mathematics for data science

    Sharpen your applied mathematics for data science and artificial intelligence;

  • Artificial intelligence algorithms

    Focus your learning curve on understanding the heart of artificial intelligence algorithms

  • IT & Big Data architectures

    Operationalise your scientific skills with the analysis, design, implementation & monitoring of IT & Big Data architectures

  • IT project management and Legal

    Get awareness of IT project management and the legal consequences of data handling, with a pinch of ethical thinking regarding the consequences of mining (big) data.

Programme Structure

840-hour programme – 90 ECTS:

    • 775 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 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 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.

* 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.

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.

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. 

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 Science and 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:

17,350*

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)