Syllabus

Title
2055 Research & Policy Seminar: Data Science and Machine Learning
Instructors
Dr. Kujtim Avdiu
Contact details
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/16/25 to 09/21/25
Registration via LPIS
Notes to the course
Dates
Day Date Time Room
Friday 10/10/25 03:00 PM - 05:00 PM TC.4.18
Friday 10/17/25 03:00 PM - 05:00 PM TC.4.18
Friday 10/24/25 03:00 PM - 05:00 PM TC.4.18
Friday 10/31/25 03:00 PM - 05:00 PM TC.4.18
Friday 11/07/25 03:00 PM - 05:00 PM TC.4.18
Friday 11/14/25 03:00 PM - 05:00 PM TC.4.18
Friday 11/28/25 03:00 PM - 05:00 PM TC.4.18
Friday 12/05/25 03:00 PM - 05:00 PM TC.4.18
Friday 12/12/25 03:00 PM - 05:00 PM TC.4.18
Friday 01/09/26 03:00 PM - 05:00 PM TC.4.18
Friday 01/16/26 03:00 PM - 05:00 PM TC.4.18
Friday 01/23/26 03:00 PM - 05:00 PM TC.4.18
Contents

This course offers master’s students of economics a practical insight into modern methods of data processing and machine learning. Using concrete examples such as the analysis of big data in company and transaction data, the forecasting of key economic figures or the automated evaluation of text data, students acquire application-oriented knowledge in the field of data science. Practical implementation is mainly carried out using the programming language R.

Topics covered in data science

-          Data preparation and transformation

-          Explorative data analysis

-          Interactive data visualization

-          Processing text data (natural language processing)

-          Working with big data

-          Synthetic data generation

 

Methods covered in machine learning

-          Linear and logistic regression

-          Decision trees and random forests

-          Support Vector Machines

-          Cluster analysis and principal component analysis

-          Optimization methods

Learning outcomes

After completing the course, students will be able to prepare, analyse and visualize large data sets. They will be able to apply methods of data science and machine learning, develop models and interpret their results. They will also be able to apply methods to big data and text data and prepare data-based analyses for economic decisions.

Attendance requirements

Attendance at the course is compulsory. Students may miss a maximum of three units to successfully complete the course.

 

Teaching/learning method(s)

The course combines lectures and practical exercises. This includes the joint programming of practical examples and a group project, which is concluded with a presentation of the results.

Assessment

The assessment is made up as follows:

-          Presentation of the group project: 60%

-          Active participation in the courses: 40%

 

Grading Scheme

-          85 - 100 %: Excellent

-          70 - 85%: Good

-          60 - 70%: Satisfactory

-          50 - 60 %: Sufficient

-          0 - 50%: Not sufficient

 

Prerequisites for participation and waiting lists

Basic knowledge of the R programming language is required for participation.

Readings

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Availability of lecturer(s)
Last edited: 2025-05-02



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