Algorithms in data science

linma2472  2021-2022  Louvain-la-Neuve

Algorithms in data science
5.00 credits
30.0 h + 22.5 h
Q1
Teacher(s)
Blondel Vincent; Delvenne Jean-Charles (coordinator); Krings Gautier (compensates Blondel Vincent);
Language
English
Prerequisites
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.
Learning outcomes

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.
 
Content
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.
Teaching methods
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.
Evaluation methods
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.
Online resources
The Moodle page of the course.
Bibliography
Variable.
Teaching materials
  • Documents sur la page Moodle / Documents on the Moodle page
Faculty or entity
MAP


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

Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master [120] in Statistics: General
STAT2M
5
Master [120] in Agricultural Bioengineering
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5
Master [120] in Data Science Engineering
DATE2M
5
Master [120] in Mathematics
MATH2M
5
Master [120] in Environmental Bioengineering
BIRE2M
5
Master [120] in Computer Science and Engineering
INFO2M
5
Master [120] in Data Science: Information Technology
DATI2M
5
Master [120] in Chemistry and Bioindustries
BIRC2M
5
Master [120] in Computer Science
SINF2M
5
Master [120] in Mathematical Engineering
MAP2M
5
Master [120] in Data Science : Statistic
DATS2M
5