Seminar data science
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Enseignement
Détails
Faculté Faculté des sciences et de médecine Domaine Informatique Code UE-SIN.07818 Langues Anglais Type d'enseignement Séminaire
Cursus Master Semestre(s) SA-2022 Horaires et salles
Struct. des horaires 2h par semaine durant 14 semaines Heures de contact 28 Enseignement
Responsables - Cudré-Mauroux Philippe
Enseignants - Khayati Mourad
- Lerner Alberto
Description The seminar on data science involves presentations that cover recent topics on data science. The area of this year’s seminar is time series operators. Modern SQL databases do not strictly support time series, but two features come close. First, SQL:2011 introduced extensions to support temporal data. Chief among the extensions is the concept of periods, which allows a time interval to be associated with every row of a table. Second, the older SQL:1999 introduced the notion of window functions. These allow data to be manipulated as if it was a sequence.
In the scope of this seminar, we investigate papers that describe algorithms and operators to process time series data. The papers explore mechanisms that highlight the strengths and weaknesses of these operators when applied to time series.
Objectifs de formation The goal for the students is to learn how to critically read and study research papers, how to describe a paper in a report, and how to present it in a seminar. Under supervision, students will select one paper to study, contrast and compare with related work. This seminar aims to help students to gather in-depth knowledge of an advanced topic and develop the skills required to describe a complex problem from the predictive analytics area in the form of both a presentation and a written report.
Commentaire MSc-CS BENEFRI - (Code Ue: 63507 / Tracks: T6) . The exact date and time of this course as well as the complete course list can be found at http://mcs.unibnf.ch/.
Course and exam registration on ACADEMIA (not myunifr.ch). Please follow the instructions on https://mcs.unibnf.ch/organization/
Softskills Non Hors domaine Non BeNeFri Oui Mobilité Oui UniPop Non -
Modalités d'évaluation
Evaluation continue
Mode d'évaluation Par note -
Affiliation
Valable pour les plans d'études suivants: Complément au doctorat [PRE-DOC]
Version: 2020_1/v_01
Complément au doctorat ( Faculté des sciences et de médecine) > UE de spécialisation en Informatique (niveau master)
Enseignement complémentaire en sciences
Version: ens_compl_sciences
Paquet indépendant des branches > UE de spécialisation en Informatique (niveau master)
Informatique [3e cycle]
Version: 2015_1/V_01
Formation continue > UE de spécialisation en Informatique (niveau master)
Informatique [POST-DOC]
Version: 2015_1/V_01
Formation continue > UE de spécialisation en Informatique (niveau master)
MSc en informatique (BeNeFri)
Version: 2010_2/V_02
MSc en informatique (BeNeFri), cours, séminaires et travail de Master > UE de spécialisation en Informatique (niveau master)MSc en informatique (BeNeFri), cours, séminaires et travail de Master > T6: Data Science