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Applied MSc in Data Analytics
100%
students in France have found an internship
Apprenticeship
apprenticeship study mode available
(in France only)
90%
students are satisfied with the programme
Updated on : 05/20/2023
Let’s talk about Data Analytics!
The Applied MSc in Data Analytics 1 programme, with its two entries in autumn and spring, will enable you to increase your business skills and job opportunities by strengthening decision-making through data analysis. In this Applied MSc, you will master the techniques and tools needed to conduct and deliver your analyses with relevant and structured reports, improving the return on investment of your company’s projects.
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 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.
The Data Analyst is one of roles whose increasing demand in the run up to 2022.
Infographics
Although forming the smallest population in-demand for data-centric jobs (#1 Data Engineer, #2 Data Analyst, #3 Data Scientist), the Data Analyst is 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 €42,000 and €70,000.
Average trainee remuneration in France: €1,000 per month
This Applied MSc programme is delivered in the following study modes
I am a student
I'm looking for further my current level of education.
I am likely to be under 30.
I am likely to be under 30.
Learn
I am a professional
I'm looking to upskill and/or reroute my career.
I am likely to be 30 and above.
I am likely to be 30 and above.
Upskill
Initial Education - Available Modes
| Mode | Duration | On-Campus |
Online (Off-Campus) |
Internship |
|---|---|---|---|---|
|
2 years |
Yes (compatible time zone) |
Optional in Year 1 Mandatory 6-month in Year 2 |
||
|
14 to 24 months |
Yes (in France) |
N/A (apprenticeship) |
Continuing Education - Available Modes
| Mode | Duration | On-Campus |
Online (Off-Campus) |
Online Asynchronous |
Internship |
|---|---|---|---|---|---|
|
9 months |
Yes (compatible time zone) |
No |
Mandatory 6-month
|
||
|
Flexible |
Occasionally, if student availability and programmes’ schedules match |
Occasionally, if student availability and programmes’ schedules match |
Yes |
6-month, mandatory (including with your current employer, if possible). |
Study modes
On-Campus
500 hours
Choose to study either on the campus of Nice Sophia-Antipolis or Paris. You will follow 8 months of courses (around 5hrs per day), followed by 5 to 6 months of internship.
Off-Campus - full time
500 hours
Designed for those who want to study full time.
Study online for a period of 8 months, followed by 5 to 6 months of internship.
Apprenticeship
550 hours
Designed for those who want to study and work at the same time.
Study online or on campus and acquire work experience in 12 months with our sandwich course.
SPOC – asynchronous mode
500 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 8 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
15 months
For the Data Analytics programme, the apprenticeship takes place over 15 months and is only accessible to
students holding a Bac+4 minimum.
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.
Objectives
Programme Structure
500-hour programme – 60 ECTS:
- 455 hours of classes including 75 hours of DSTI Warm Up
- 45 hours of support sessions
850-hour internship (5 to 6 months) – 30 ECTS
The Applied MSc in Data Analytics programme is composed of all the following modules*, which are actual hours of class attendance (personal work is expected on top of these):
Applied Mathematics for Data Science
3 ECTS
(25 hrs)
Calculus – Linear Algebra – Trigonometry & Complex Numbers
Big Data Processing with R
4 ECTS
(25 hrs)
Import and manipulate very large datasets with R – Best data structures selection – Data Transformation – Visualisation – Exploring and modelling
Python Machine Learning Labs
4 ECTS
(25 hrs)
Data structures – Cleaning & preparation – Pandas – Matplotlib – Scikit-learn – OpenCV – Python & Flask – Keras – Numpy
Foundations of Statistical Analysis & Machine Learning Part I
3 ECTS
(25 hrs)
Descriptive Statistics - Probability Theory – Applications using R
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)
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
Datawarehousing & ETL
4 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
4 ECTS
(25 hrs)
XML dataflow – DTD & Schemas – XLS Transformation – JSON & Transformations
Document Databases – NoSQL – Part 2
2 ECTS
(5 hrs)
Fundamentals of MongoDB Databases, Collection and Document – Advanced MongoDB queries, MongoDB aggregations, MongoDB data architecture –Applications with MongoDB and Robo3T
Graph Databases – NoSQL – Part 1
4 ECTS
(25 hrs)
Preparation to Neo4j Certification – Modelling a graph-based problem, implementation with the Neo4j database
Advanced Excel for Data Analytics & Machine Learning
3 ECTS
(25 hrs)
Formulas - Data Visualisation - PowerPivot - Solver - Visual Basic for Application
CRM Data Management
4 ECTS
(25 hrs)
Preparation of certification “Microsoft Power Platform Functional Consultant (PL-200)”
Data & Machine Learning Visualisation Ecosystem
4 ECTS
(25 hrs)
Tables & Machine Learning with SAS Viya
Reporting & Visualisation
4 ECTS
(25 hrs)
Preparation of certification “Analyzing Data with Microsoft BI (DA-100)”
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:
- 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
The admission criteria mentioned here are valid for all modes of study.
Eligibility for Applied MSc
To be considered for the DSTI’s Masters programmes, candidates must meet the following criteria:
1. High School Mathematics
Candidates must have studied Mathematics in high school or an equivalent qualification.
2. 3/4-year Bachelor’s degree
Candidates must have completed a 3 or 4-year Bachelor’s degree or equivalent from a recognised university.
3. Academic Records
DSTI offers three options to prove prospective students their academic qualifications. Submitting proofs of more than one qualification mentioned below will boost the chances of admission.
a. Option 1: Minimum Grades + Bachelor’s Degree Certificate
The candidate must have at least the following grade to be considered for the Applied MSc programme.
USA | Europe | India | China | |||
GPA | Germany | France | UK | CGPA | Class | Per cent |
2.0+ | 3.5+ | 12+ | 2:2 (2nd Class Lower div) | 6.5+ | Upper second class | 67+ |
Exceptional cases
- Those students who have lower grades than those mentioned in the table must qualify for one of the standard admission tests or the DSTI online entrance exam.
- Those students who may have an academic break of more than 8 years and have not worked in the tech sector are encouraged to take one of the standard admission tests or the DSTI online entrance exam.
- After submitting your transcripts, DSTI may ask students from countries where it is difficult to measure the equivalency of the grades to qualify for one of the standard admission tests or the DSTI online entrance exam.
b. Option 2: standard admission test + Bachelor’s Degree Certificate
To ensure quality applications, we value test scores from standardised tests. Submitting these scores will enhance your profile and chances of admission.
Test | The minimum score in Quantitative | Average Overall Score* |
GRE | 155 | 300 |
GMAT | 42 | 600 |
c. Option 3: Online DSTI Entry Exam + Bachelor’s Degree Certificate
If you cannot satisfy the two criteria mentioned above, you may consider an online DSTI Entry Exam. You may appear for it from your home. All you need is a computer and a stable internet connection. The DSTI Entry exam has two sections: Mathematics and IT.
4. Proficiency in English
Since all courses are taught in English, students must have a B2 level of proficiency in English. DSTI will assess English proficiency during the Admission Interview.
To boost an application, students may submit their IELTS or TOEFL scores.
For more information, please refer to Admissions at DSTI.
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.
Tuition Fees
Regarding the apprenticeship study mode, all tuition fees are paid by the host company.
Accelerated course in 15 months:
€
14,870*
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:
€
7,435*
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)
Careers Perspectives
-
Data Analyst
-
Data Analyst vs Data Scientist
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 Analytics, the DSTI Warm Up lasts 3 weeks (75 hours) and can be followed on-campus or online.