Syllabus

Title
1509 Applications of Data Science
Instructors
Univ.Prof. Dr. Verena Dorner, Dipl.-Ing. Nour Jnoub, B.Sc.
Contact details
  • Type
    PI
  • Weekly hours
    2
  • Language of instruction
    Englisch
Registration
09/01/21 to 09/14/21
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
Day Date Time Room
Thursday 10/14/21 04:00 PM - 08:00 PM TC.4.03
Thursday 10/21/21 04:00 PM - 08:00 PM TC.4.03
Thursday 10/28/21 04:00 PM - 08:00 PM TC.5.03
Thursday 11/04/21 04:00 PM - 08:00 PM TC.4.03
Thursday 11/11/21 04:00 PM - 08:00 PM TC.4.03
Thursday 11/18/21 04:30 PM - 07:30 PM TC.2.02

Contents

This course aims to explore fundamental topics of recommendation systems. Participants will explore the different types of recommendation systems that are found on e-commerce websites. In particular, we will discuss the assumptions of different recommendation systems regarding available data, user preferences, decision processes and environments, and get familiar with appropriate data modelling and analysis methods. Students will also learn to evaluate and adapt recommendation systems to fit various requirements. We will use the R programming language.

Learning outcomes

After completing this course, students will have an understanding of the different types of recommendation systems. Students will have learned how they fit different environmental and user parameters. They will be able to use statistical tools to implement recommendation systems, analyse relevant data, and interpret the data. This course aims to contribute to students’ ability to translate decision situations into design parameters of a decision support tool, to implement and evaluate such tools and to present their results in an academically appropriate and engaging fashion.

Attendance requirements

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

Teaching/learning method(s)

Each course session will consist of a lecture introducing a specific topic and methodology and a hands-on programming lab, implementing the main ideas of the lecture in code.

Assessment

The final grade will be composed of:

- (40%) a mid-term group assignment

- (50%) a final assignment

- (10%) to be earned by active participation during the lectures and practical sessions.

 

Prerequisites for participation and waiting lists

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.

Last edited: 2021-06-22



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