Syllabus
Title
1060 Field Course: Data Science and Machine Learning
Instructors
Lukas Schmoigl, MSc (WU), MSc
Type
PI
Weekly hours
3
Language of instruction
Englisch
Registration
09/21/26 to 09/27/26
Registration via LPIS
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Dates
| Day | Date | Time | Room |
|---|---|---|---|
| Friday | 10/02/26 | 12:00 PM - 03:00 PM | D5.1.001 |
| Friday | 10/09/26 | 11:30 AM - 02:30 PM | D3.0.233 |
| Friday | 10/16/26 | 11:30 AM - 02:30 PM | TC.5.05 |
| Friday | 10/30/26 | 11:30 AM - 02:30 PM | D5.0.002 |
| Friday | 11/06/26 | 11:30 AM - 02:30 PM | D5.0.002 |
| Friday | 11/13/26 | 11:30 AM - 02:30 PM | D5.0.002 |
| Friday | 11/20/26 | 11:30 AM - 02:30 PM | D5.0.002 |
| Friday | 12/04/26 | 11:30 AM - 02:30 PM | D5.0.002 |
| Friday | 12/11/26 | 11:30 AM - 02:30 PM | TC.3.05 |
| Friday | 12/18/26 | 11:30 AM - 02:30 PM | D5.0.002 |
| Friday | 01/08/27 | 11:30 AM - 02:30 PM | D5.0.002 |
| Friday | 01/15/27 | 11:30 AM - 02:30 PM | D5.0.002 |
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 and software implementations of a wide range of useful methods within the field of data science.
The following topics are covered:
- Coding Setup
- Databases and APIs
- Webscraping
- Data Visualization
- Introduction into Supervised Learning and Cross Validation
- Natural Language Processing
- Neural Networks
For more information visit the course website.
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 familiar with common software frameworks and packages across different coding languages to implement the learned concepts for practical use cases.
For this course participation is obligatory. Students are allowed to miss a maximum of two units.
The fundemantal course contents are covered through lectures. The lectures introduce key concepts and their coding and software implementation. Understanding of the concepts is assessed by assignments and quizzes throughout the semester and in a final exam at the end of semester. The assginments focus on the recent topics in the lectures and are conducted and presented in groups of students and the students solutions are discussed in class. The quizzes follow the presentations and are taken individually. The individual exam serves as an assessment of all topics covered at the end of the semester.
The final grade is composed of:
- Assignments (30%) (group)
- Quizzes (30%) (individual)
- Exam (40%) (individual)
Grading scheme:
> 90%: Excellent
(80%, 90%]: Good
(70%, 80%]: Satisfactory
(60%, 70%]: Sufficient
[0%, 60%]: Not sufficient
Students will be able to retake the exam if their overall grade is 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: 2026-06-13
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