Syllabus

Title
1478 Data Analytics
Instructors
PD Mag.Dr. Gertraud Malsiner-Walli, M.Stat., Peter Knaus, Ph.D.
Contact details
peter.knaus@wu.ac.at; gertraud.malsiner-walli@wu.ac.at
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/14/23 to 09/27/23
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Specialization in Business Administration Course II - Data Science
Course II - Data Science
Course II - Data Science
Dates
Day Date Time Room
Tuesday 10/10/23 08:00 AM - 11:00 AM TC.5.15
Thursday 10/12/23 06:00 PM - 08:00 PM LC.-1.038
Tuesday 10/17/23 08:00 AM - 11:00 AM EA.6.026
Thursday 10/19/23 06:00 PM - 08:00 PM LC.-1.038
Tuesday 10/24/23 08:00 AM - 11:00 AM TC.5.01
Wednesday 10/25/23 03:00 PM - 05:00 PM TC.3.02 (P&S)
Tuesday 11/07/23 08:00 AM - 11:00 AM TC.5.01
Thursday 11/09/23 06:00 PM - 08:00 PM LC.-1.038
Tuesday 11/14/23 09:00 AM - 12:00 PM TC.1.02
Tuesday 11/21/23 09:00 AM - 12:00 PM TC.2.01

On this page:

to top
Contents

One core element of modern Data Science are computational methodologies from the field of Machine Learning as well as Statistical Learning. The main methods will be discussed to allow for handling Regression, Classification and Clustering tasks. Real-life examples and data sets will be used. The statistical programming language R will be used to solve problems numerically.
 

to top
Learning outcomes
Students are able to identify a data science problem and choose the appropriate technology to solve the problem. Furthermore, the students are able to implement the respective algorithms using the statistical programming language R by selecting useful extension packages. Upon completion of the course participants will be able to:

1. Analyze data science problems structurally and find the appropriate method to solve the respective problem.
2. Solve data science problems using R.
to top
Attendance requirements

You are allowed to skip one unit at maximum. This regulation holds also for the online modus. At the final presentation of the project results you must be present. 

to top
Teaching/learning method(s)
At the beginning theoretical foundations of Machine Learning technologies will be presented. Furthermore, an introduction to R for Data Science will be given. Over the course of the lecture student presentations will be a central part. 
to top
Assessment
  • Homework (30%)
  • Project (40%)
  • Final Exam (30%)
to top
Prerequisites for participation and waiting lists

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.

to top
Readings

Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.

to top
Availability of lecturer(s)

Last edited: 2023-04-17



Back