This Data Science and Artificial Intelligence programme at Data ScienceTech Institute is “depth-first” in applied mathematics and their implementation, led by Professors from the “French School of Mathematics”. It aims to give students 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.
Prospective students seeking for an IT-focused, “breadth-first” through programming frameworks, should be looking at our Applied MSc in Data Engineering for Artificial Intelligence programme instead.
In this Applied MSc programme, you will:
- Sharpen your applied mathematics for data science and artificial intelligence;
- Focus your learning curve on understanding the heart of artificial intelligence algorithms;
- Operationalise your scientific skills with the analysis, design, implementation & monitoring of IT & Big Data architectures;
- Combine science and technology in application courses & projects requiring both, for dealing with industry-grade data science;
- 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.
All DSTI's Applied MSc programme are RNCP (National Registry of Professional Certification) Level 7 accredited.
Get more detailsVisit official programme website
Programme StructureCore Data Science & Artificial Intelligence
- Applied Mathematics for Data Science (25 hrs) - 1 ECTS
- Foundations of Statistical Analysis & Machine Learning Part 1 (25 hrs) - 1 ECTS
- Foundations of Statistical Analysis & Machine Learning Part 2 (40 hrs) - 1 ECTS
- Continuous Optimisation (25 hrs) - 2 ECTS
- Artificial Neural Networks (25 hrs) - 1 ECTS
- SAS "The SAS Ecosystem DSTI Chair" (25 hrs) - 2 ECTS
- Time-Series Analysis (25 hrs) - 2 ECTS
- Agent- Based Modeling (25 hrs) - 1 ECTS
- Metaheuristic Optimisation (25 hrs) - 1 ECTS
- Inverse Problems & Data Assimilation (25 hrs) - 2 ECTS
- Semantic Web Technologies for Data Science Developments (25 hrs) - 1 ECTS
- Advanced Statistical Analysis & Machine Learning (35 hrs) - 2 ECTS)
- Survival Analysis using R (25 hrs) - 1 ECTS
- Statistical Analysis of Massive and High Dimensional Data (25 hrs) - 1 ECTS
- Deep Learning with Python (25 hrs) - 2 ECTS
- Software Engineering Part 1 (25 hrs) - 1 ECTS
- Data Wrangling with SQL (25 hrs) - 1 ECTS
- Software Engineering Part 2 (25 hrs) - 1 ECTS
- Amazon AWS "Cloud-Computing DSTI Chair" (50 hrs) - 3 ECTS
- The Hadoop & SPARK ecosystem (50 hrs) - 3 ECTS
- Data Laws & Regulations - Philosophies, Geopolitics & Ethics (25 hrs) - 1 ECTS
- IT Project Management: PMP-PMI and Agile approaches (25 hrs) - 1 ECTS
- 12 months
Start dates & application deadlines
- Apply before , International
- Apply before , EEA/EU
DisciplinesApplied Mathematics Data Science & Big Data Artificial Intelligence View 36 other Masters in Artificial Intelligence in France
We are not aware of any academic requirements for this programme.
We are not aware of any English requirements for this programme.
- You will need to provide the usual registry data: your CV and a cover letter.
- Your English proficiency will be assessed by your understanding during the admission interview. You are also free and welcome to provide any English proficiency test that you may have taken.
- You may also submit as many recommendation letters as you wish to.
International14500 EUR/yearTuition FeeBased on the original amount of 14500 EUR per year and a duration of 12 months.
EU/EEA14500 EUR/yearTuition FeeBased on the original amount of 14500 EUR per year and a duration of 12 months.
Studyportals Tip: Students can search online for independent or external scholarships that can help fund their studies. Check the scholarships to see whether you are eligible to apply. Many scholarships are either merit-based or needs-based.
Apply and win up to €10000 to cover your tuition fees.
Due for update
Updated over a year ago
Check the official programme website for potential updates.