30.0 h + 22.5 h
Some familiarity with linear algebra and discrete mathematics is required (such as given in LFSAB1101, LFSAB1102, LINMA1691).
The course explores questions, mainly of an algorithmic nature, regarding the challenges offered by the emergence of Big Data.
At the end of this learning unit, the student is able to :
Learning outcomes :
The course contents may vary from one year to another and can tackle various algorithmic questions related to analysis, storage, or broadcast of large datasets. E.g., data anonymisation, plagiarism detection, social networks analysis, principles of peer-to-peer networks, etc.
In part ex cathedra lectures that introduce the concepts and algorithms along with their theoretical foundations, and in part projects with written and/or oral reports.These projects contain a large part of implementation (in Python) and data analysis. It is thus required to learn this language (thanks in part to the proposed tutorials) if not already mastered.
The projects made during the term are evaluated baed on the written reports and the oral presentations. They amount to 12/20 of the final grade (in Jan and in Aug). The (written or oral, depending on the circumstances) exam amount to 8/20 of the final grade.
The Moodle page of the course.
- Documents sur la page Moodle / Documents on the Moodle page
Faculty or entity
Title of the programme
Master  in Statistics: General
Master  in Agricultural Bioengineering
Master  in Data Science Engineering
Master  in Mathematics
Master  in Environmental Bioengineering
Master  in Computer Science and Engineering
Master  in Data Science: Information Technology
Master  in Chemistry and Bioindustries
Master  in Computer Science
Master  in Mathematical Engineering
Master  in Data Science : Statistic