Algorithms in data science

linma2472  2018-2019  Louvain-la-Neuve

Algorithms in data science
5 credits
30.0 h + 22.5 h
Blondel Vincent; Delvenne Jean-Charles coordinator; Krings Gautier (compensates Blondel Vincent); Peel Leto (compensates Delvenne Jean-Charles);
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 :


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, and in part presented by the students themselves based on a book chapter or other documents.
Evaluation methods
Oral exam with written preparation. Oral and written presentation of theory and real data analysis during the term.
Faculty or entity

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

Title of the programme
Master [120] in Data Science Engineering

Master [120] in Computer Science and Engineering

Master [120] in Agricultural Bioengineering

Master [120] in Environmental Bioengineering

Master [120] in Statistic: General

Master [120] in Computer Science

Master [120] in Mathematical Engineering

Master [120] in data Science: Statistic

Master [120] in data Science: Information technology