Syllabus

Title
1636 Field Course: Economic and Social Policy - Data Science and Machine Learning
Instructors
Dr. Katharina Fenz, B.A.,M.Sc., Thomas Mitterling, M.Sc., Lukas Schmoigl, B.Sc., Maximilian Thomasberger, M.Sc.
Type
PI
Weekly hours
3
Language of instruction
Englisch
Registration
09/19/22 to 09/25/22
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Dates
Day Date Time Room
Friday 10/14/22 10:00 AM - 01:00 PM D3.0.222
Friday 10/21/22 10:00 AM - 01:00 PM D3.0.222
Friday 10/28/22 10:00 AM - 01:00 PM D3.0.222
Friday 11/04/22 10:00 AM - 01:00 PM D3.0.222
Friday 11/11/22 10:00 AM - 01:00 PM D3.0.222
Friday 11/18/22 10:00 AM - 01:00 PM D3.0.222
Friday 11/25/22 10:00 AM - 01:00 PM D3.0.222
Friday 12/02/22 10:00 AM - 01:00 PM D3.0.222
Friday 12/09/22 10:00 AM - 01:00 PM D3.0.222
Friday 12/16/22 10:00 AM - 01:00 PM D3.0.222
Friday 12/23/22 10:00 AM - 01:00 PM D3.0.222
Friday 01/13/23 10:00 AM - 01:00 PM D3.0.222
Friday 01/20/23 10:00 AM - 01:00 PM D3.0.222
Friday 01/27/23 10:00 AM - 01:00 PM TC.1.01 OeNB

Contents

This course is an introduction to Machine Learning and Data Science for graduate students of Economics. The field course (1636) will introduce you to concepts in Data Science which will be practiced in the seminar (1709) using R. Hence, participation in both classes is necessary. The focus of the classes is on practical applications of a wide range of useful methods within the field of Data Science. 

Both field course and seminar will cover the following topics: 

  1. Introduction and Organisation 
  2. Data Wrangling 
  3. Exploratory Data Analysis and Data Visualisation  
  4. Introduction into Spatial Methods and Spatial Data Visualisation 
  5. Regression and Classification Trees, Cross Validation 
  6. Random Forest and Boosted Regression Trees 
  7. (Spatial) Bayesian Model Averaging 
  8. Clustering Algorithms 
  9. Support Vector Machine and Naive Bayes 

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, Presentation and Discussion of Assignments and Exam.

Assessment

The final grade is composed of: 

  • Exam 70%
  • Assignments 20%
  • Class participation 10% 

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.

Last edited: 2022-04-22



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