1516 Field Course: Data Science and Machine Learning
Dr. Katharina Fenz, B.A.,M.Sc., Lukas Schmoigl, B.Sc., Maximilian Thomasberger, M.Sc.
Weekly hours
Language of instruction
09/18/23 to 09/24/23
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Day Date Time Room
Wednesday 10/04/23 03:30 PM - 06:30 PM D4.0.144
Wednesday 10/11/23 03:30 PM - 06:30 PM D4.0.144
Wednesday 10/18/23 03:30 PM - 06:30 PM D4.0.144
Wednesday 10/25/23 03:30 PM - 06:30 PM D4.0.144
Wednesday 11/08/23 03:30 PM - 06:30 PM TC.5.14
Wednesday 11/15/23 03:30 PM - 06:30 PM D4.0.144
Wednesday 11/22/23 03:30 PM - 06:30 PM TC.5.27
Wednesday 11/29/23 03:30 PM - 06:30 PM D4.0.144
Wednesday 12/06/23 03:30 PM - 06:30 PM D4.0.144
Wednesday 12/13/23 03:30 PM - 06:30 PM D4.0.144
Wednesday 12/20/23 03:30 PM - 06:30 PM D4.0.144
Wednesday 01/10/24 03:30 PM - 06:30 PM D4.0.144
Wednesday 01/17/24 03:30 PM - 06:30 PM D4.0.144
Wednesday 01/24/24 03:30 PM - 06:30 PM D4.0.144
Wednesday 01/31/24 03:30 PM - 06:30 PM D4.0.144

Inhalte der LV:

This course is an introduction to Data Science and Machine Learning for graduate students of Economics. The class will introduce you to concepts via lecture and practice them using R. The focus of the class is on practical applications of a wide range of useful methods within the field of Data Science. 

The field course will cover the following topics: 

  1. Data Wrangling 
  2. Exploratory Data Analysis and Data Visualisation  
  3. Webscraping and Geocoding
  4. Introduction into Machine Learning and Cross Validation
  5. Random Forest and Boosted Regression Trees 
  6. Text Classification with Support Vector Machine and Naive Bayes 
  7. Nearest Neighbor Learning
  8. Clustering Algorithms and Principal Component Analysis
Learning outcomes

The aim of this course is to introduce the students to various methods and concepts in Data Science, applying them in the context of economics, as well as improve their programming skills. Upon finishing this class, students will have a “Data Science Toolkit” at their disposal.

Attendance requirements

Attendance is compulsory. Students can be absent during 2 units, beyond that a confirmation of severe reasons for absence is requested. Students cannot be deregistered from the course after achieving accomplishments in one of the course's components.

Teaching/learning method(s)

Lectures, Assignments, Paper, Presentations and Exam.


The final grade is composed of: 

  • Exam 35% 
  • Paper and Presentation 35% 

Assignments 30%

Prerequisites for participation and waiting lists


Programming skills on an intermediate level are required (e.g. in R or Python). Although this is an applied class, basic understanding of probability, statistics, linear algebra and calculus is necessary.


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Last edited: 2023-08-10