Syllabus

Titel
1902 Applications of Data Science
LV-Leiter/innen
Dr. Claudio Di Ciccio, M.Sc.
Kontakt
  • LV-Typ
    PI
  • Semesterstunden
    2
  • Unterrichtssprache
    Englisch
Anmeldung
04.09.2019 bis 18.09.2019
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Bachelor
Termine
Wochentag Datum Uhrzeit Raum
Donnerstag 03.10.2019 09:30 - 13:30 D2.0.038
Donnerstag 10.10.2019 09:30 - 13:30 D2.0.038
Mittwoch 16.10.2019 15:00 - 19:00 D4.0.039
Mittwoch 23.10.2019 15:00 - 19:00 D4.0.039
Mittwoch 13.11.2019 15:00 - 19:00 D4.0.039
Mittwoch 20.11.2019 15:00 - 19:00 D4.0.039

Inhalte der LV

The course gives an introduction into applications of data science in the field of marketing, supply chain management and business process management. In this semester, we will focus on the field between data and process science, namely process mining.

The course will begin with the main aspects of Business Process Management, focussing in particular on their automation and monitoring. Thereupon, the main concepts of process mining will be illustrated, especially discovery, conformance checking, and performance checking.

During the entire course, theoretical, formal, and practical session will be alternated to have a full overview on the matter.

Lernergebnisse (Learning Outcomes)

After completing this course students will have knowledge about different areas of application for data science. Students will have a basic understanding of area-specific challenges and algorithms. Besides an understanding of the problem structure, students will learn to apply mathematical and statistical tools to support decision making. Apart from that, completing this course will contribute to the students’ ability to efficiently work and communicate in a team, work on solutions for complex practical problems by using modern statistical software.

Regelung zur Anwesenheit

The rules on the attendance of a Continuous Assessment Course (PI) apply. See the dedicated page on the WU portal for further information.

Lehr-/Lerndesign

The course will combine alternative ways to deliver the different topics to the students. On the one hand, a classical lecture style approach where the instructor presents the software and the content will be used; on the other hand, students will have to solve hands-on problems in class and as homework.

Leistung(en) für eine Beurteilung

The final grade will be computed on the basis of:

  • Attendance (10%)
  • In-class teamwork (45%)
  • Project work & project presentation (45%)

Teilnahmevoraussetzung(en) und Vergabe von Wartelistenplätzen

Successful conclusion of the course 1 of SBWL Data Science.

Please be aware that, for all courses in this SBWL, registration is only possibly for students who successfully have completed the entry course (Einstieg in die SBWL: Data Science).

Note that for courses within the SBWL "Data Science" we can only accept students enrolled in one of WU's bachelor programmes who qualify for starting an SBWL; particularly, we cannot accept students from other courses and programmes enrolled at WU as 'Mitbeleger' only.
Zuletzt bearbeitet: 02.10.2019



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