Seminar data science

  • Unterricht

    Details

    Fakultät Math.-Nat. und Med. Fakultät
    Bereich Informatik
    Code UE-SIN.07818
    Sprachen Englisch
    Art der Unterrichtseinheit Seminar
    Kursus Master
    Semester SA-2022

    Zeitplan und Räume

    Strukturpläne 2h par semaine durant 14 semaines
    Kontaktstunden 28

    Unterricht

    Verantwortliche
    Dozenten-innen
    Beschreibung

    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.

    Lernziele

    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.

    Bemerkungen

    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/

    Soft Skills
    Nein
    ausserhalb des Bereichs
    Nein
    BeNeFri
    Ja
    Mobilität
    Ja
    UniPop
    Nein
  • Leistungskontrolle

    Fortlaufende Evaluation

    Bewertungsmodus Nach Note
  • Zuordnung
    Zählt für die folgenden Studienpläne:
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    Zusatz zum Doktorat [PRE-DOC]
    Version: 2020_1/v_01
    Zusatz zum Doktorat (Math.-Nat. und Med. Fakultät) > UE für Vertiefungsstudium in Informatik (Niveau Master)