Syllabus

Title
2329 Research & Policy Seminar: Data Science and Machine Learning
Instructors
X N.N., Lukas Schmoigl, B.Sc., Assoz.Prof. PD Dr. Bettina Grün
Contact details
bettina.gruen@wu.ac.at, lukas.schmoigl@wu.ac.at
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/17/24 to 09/22/24
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Research & Policy Seminar: Data Science and Machine Learning
Dates
Day Date Time Room
Friday 10/11/24 02:00 PM - 04:00 PM D2.0.392
Friday 10/18/24 02:00 PM - 04:00 PM D4.0.019
Friday 10/25/24 02:00 PM - 04:00 PM D4.0.144
Friday 11/08/24 02:00 PM - 04:00 PM TC.4.05
Friday 11/15/24 02:00 PM - 04:00 PM TC.4.17
Friday 11/22/24 02:00 PM - 04:00 PM D2.0.392
Friday 11/29/24 02:00 PM - 04:00 PM TC.4.17
Friday 12/06/24 02:00 PM - 04:00 PM TC.3.10
Friday 12/20/24 02:00 PM - 04:00 PM TC.4.17
Friday 01/10/25 02:00 PM - 04:00 PM TC.4.17
Friday 01/17/25 02:00 PM - 04:00 PM TC.4.17
Friday 01/24/25 02:00 PM - 04:00 PM TC.4.17
Friday 01/31/25 02:00 PM - 04:00 PM TC.4.17

On this page:

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Contents

This course complements the field course (1367) to introduce graduate students of Economics to Data Science and Machine Learning methods and tools. The field course (1367) will introduce the concepts. Additional technical aspects and advanced topics are covered in this seminar (2329) mainly using R.

The focus of both classes is on practical applications of a wide range of useful methods within the field of Data Science. 

Both the field course and the seminar will cover the following topics:

  • Introduction & Organization
  • Data Wrangling and Exploratory Data Analysis
  • (Interactive) Data Visualization
  • Introduction into Supervised Learning and Cross Validation
  • Random Forest and Boosted Regression Trees
  • Introduction to Unsupervised Learning
  • Mixture Models
  • Principle Component Analysis and Factor Models
  • Natural Language Processing and Text Classification

Additional topics covered in the Research & Policy Seminar are:

  • Webscraping
  • Spatial Data Visualization
  • Dashboard Building
  • Sentiment Analysis
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Learning outcomes

After completing this course students will have a “Data Science Toolkit” at their disposal. They will be able to describe, characterize and apply key concepts and methods of data science and machine learning as outlined in the course contents. In addition, students will be able to use statistical software to perform data analysis using data science and machine learning methods.

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Attendance requirements

For this course participation is obligatory. Students are allowed to miss a maximum of 2 units .

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Teaching/learning method(s)
  • Lectures and hands-on tutorials covering special and more technical topics on data science and machine learning.
  • Homework assignments and exercises with in-class presentations by students
  • Data analysis project with two presentations in class:
    1. Exploratory data analysis
    2. Application of machine learning model(s)
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Assessment

The final grade is composed of: 

  • Presentation 1 (30%)
  • Presentation 2 (40%)
  • Assignments and exercises (30%)

Grading scheme:

  • > 90%: Excellent
  • (80%, 90%]: Good
  • (70%, 80%]: Satisfactory
  • (60%, 70%]: Sufficient
  • [0%, 60%]: Not sufficient

 

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Prerequisites for participation and waiting lists

This course can only be attended in combination with the Field Course Economic and Social Policy (1367).

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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Readings

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Last edited: 2024-07-20



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