Syllabus
Title
1264 Field Course: Data Science and Machine Learning
Instructors
Lukas Schmoigl, B.Sc., Ines Kusmenko, M.Sc.
Type
PI
Weekly hours
3
Language of instruction
Englisch
Registration
09/16/25 to 09/21/25
Registration via LPIS
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Dates
| Day | Date | Time | Room |
|---|---|---|---|
| Friday | 10/10/25 | 11:30 AM - 02:30 PM | TC.4.13 |
| Friday | 10/17/25 | 11:30 AM - 02:30 PM | TC.4.13 |
| Friday | 10/24/25 | 11:30 AM - 02:30 PM | TC.4.13 |
| Friday | 10/31/25 | 11:30 AM - 02:30 PM | TC.4.13 |
| Friday | 11/07/25 | 11:30 AM - 02:30 PM | TC.4.13 |
| Friday | 11/14/25 | 11:30 AM - 02:30 PM | TC.3.06 |
| Friday | 11/21/25 | 11:30 AM - 02:30 PM | TC.4.13 |
| Friday | 11/28/25 | 11:30 AM - 02:30 PM | TC.4.13 |
| Friday | 12/05/25 | 11:30 AM - 02:30 PM | TC.4.13 |
| Friday | 12/12/25 | 11:30 AM - 02:30 PM | TC.4.13 |
| Friday | 01/09/26 | 11:30 AM - 02:30 PM | TC.4.13 |
| Friday | 01/16/26 | 11:30 AM - 02:30 PM | TC.4.13 |
| Friday | 01/23/26 | 11:30 AM - 02:30 PM | TC.4.13 |
This course introduces graduate students of economics to data science and machine learning methods and tools. The focus of the class is on practical applications of a wide range of useful methods within the field of data science.
The following topics are covered:
- Coding Setup
- Databases and APIs
- Data Wrangling
- Data Visualization
- Webscraping
- Supervised Learning and Cross Validation
- Natural Language Processing
- Spatial Bayesian Model Averaging
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.
For this course participation is obligatory. Students are allowed to miss a maximum of 2 units.
The course content is covered and presented in lectures and tutorials. Understanding of the concepts is assessed by a written exam. Students apply the learned methods in assignments as well as in a data project of their choice. Students present and discuss their data project in class.
The final grade is composed of:
- Assignments (40%)
- Exam (60%)
Grading scheme:
> 90%: Excellent
(80%, 90%]: Good
(70%, 80%]: Satisfactory
(60%, 70%]: Sufficient
[0%, 60%]: Not sufficient
Programming skills in R or a similar programming language (e.g., Python, Julia). Basic understanding of probability, statistics, linear algebra and calculus. Practical understanding of statistical modeling and experience in working with data.
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Last edited: 2025-06-30
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