Algorithms in data science

linma2472  2019-2020  Louvain-la-Neuve

Algorithms in data science
Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
5 credits
30.0 h + 22.5 h
Blondel Vincent; Delvenne Jean-Charles (coordinator); Krings Gautier (compensates Blondel Vincent);
Some familiarity with linear algebra and discrete mathematics is required (such as given in LFSAB1101, LFSAB1102, LINMA1691).
Main themes
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 :

1 Learning outcomes :
  • AA1 : 1,2,3
  • AA3 : 1,3
  • AA4 : 1
  • AA5 : 1,2,3, 5,6
More specifically, at the end of the course the student will be able to :
  • read a general or specialized literature on a specific cutting-edge theme of discrete mathematics, and summarize the key messages and results
  • explain those messages to their peers in a clear and precise way
  • solve mathematical problems in application to those results
  • identify the possible caveats of those results and criticize the exposition chosen by the references
  • relate the concepts encountered in the literature to concepts covered in other course, despite different notations or viewpoints
The mathematical objectives can change from year to year.

The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
The course contents may vary from one year to another and can tackle various algorithmic questions related to storage, broadcast or analysis of massive datasets (Big Data). E.g., plagiarism detection, web pages ranking, frequent patterns detection, social networks analysis, parallel computing and storage, principles of peer-to-peer networks, etc.
Teaching methods
In part ex cathedra lectures, and in part  projects with written and/or oral reports.
Evaluation methods
Oral and written presentation of theory and/or real data analysis during the term. Written exam, or oral exam with written preparation.
Faculty or entity

Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Master [120] in Agricultural Bioengineering

Master [120] in Data Science : Statistic

Master [120] in Mathematical Engineering

Master [120] in Computer Science and Engineering

Master [120] in Computer Science

Master [120] in Data Science Engineering

Master [120] in Data Science: Information Technology

Master [120] in Mathematics

Master [120] in Statistic: General

Master [120] in Environmental Bioengineering