Teaching
Our focus in academic teaching is on courses at the interface between management science and computer science. Lectures and exercises are designed to provide students with strong quantitative skills that form the basis for a profound understanding of data science methods and data-driven decision making. Bachelor’s and master’s theses are typically embedded in our own research context and pave the way for students' own research efforts. Hands-on supervision enables students to already achieve meaningful successes and find enthusiasm for research.
- Overview of courses
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Overview of Courses
SS 2024 WS 24/25 SS 2025 Bachelor Data Science for Management ✅ ✅ Proseminar ✅ ✅ Bachelor's Theses ✅ ✅ ✅ Master Text Mining ✅ Applied AI ✅ Seminar ✅ ✅ ✅ Master's Theses ✅ ✅ ✅
- Course: Data Science for Management
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Course: Data Science for Management (B. Sc.)
Prior to the start of the Information Age, companies were forced to collect data from non-automated sources manually. As a result, company decisions were frequently based on gut feeling and intuition. With the emergence of ubiquitous computing technology, company decisions nowadays rely strongly on data science methods and machine learning.
The course “Data Science for Management” provides an overview of the multi-disciplinary field of data science for management students. Topics include (but are not limited to) data collection, integration, management, modeling, analysis, visualization, prediction and data-driven decision making. The course includes practical sessions focusing on data analysis and programming in R.
The main objectives of this course are:
- Understand the basic concepts and business relevance of data science and data-driven decision making
- Gain an overview of different methods, algorithms and software tools for data science applications
- Understand the pitfalls and myths of data science
Organization:
- Module codes: 02-Meth:BSc-B11-Extra1 & 02-Meth:BSc-Extra6CP
- Lecturer: Prof. Dr. Nicolas Pröllochs (BWL XI)
- Term: Summer semester 24
- Language: German
- Course format: Lecture (6 CP)
- Grading: Final exam
- Schedule: See course flyer
- Course: Text Mining
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Course: Text Mining (M. Sc.)
The digital age has ignited a burst in the volume of textual materials available to businesses and the public. Text mining provides computational techniques to derive actionable (managerial) insights from such unstructured data sources. The course “Text Mining” provides students with an overview of a wide range of text mining methods: from regular expressions to lexicon-based sentiment analysis, to more complex machine learning approaches and supervised text classification. At the end of the course, participants will be familiar with the most important concepts, principles, and algorithms in text mining. The course includes practical sessions focusing on text mining in R. Basic experience in R programming is desirable but not mandatory.
The main objectives of this course are:
- Understand the basic concepts of text mining and its relevance for business applications
- Gain an overview of different methods, algorithms and software tools for extracting knowledge from unstructured text data
- Practice the implementation of text mining applications in R
Organization:
- Module codes: 02-BWL/VWL:MSc-B11-1
- Lecturer: Prof. Dr. Nicolas Pröllochs (BWL XI)
- Course format: Lecture (6 CP)
- Term: Winter semester 24 / 25
- Language: English
- Grading: Presentation & Term Paper
- Schedule: See course flyer
Course evaluation by students (2019 – 2022): 1.4
The number of participants is limited to a maximum number of 24 students. Please register for the course by sending an e-mail to datascience@wirtschaft.uni-giessen.de (see course flyer). The application deadline is October 1, 2024 (early applications are encouraged). The course is also opened to interested bachelor students currently enrolled in the 210- and 240-CP programs.
- Proseminar (B. Sc.) and Seminar (M. Sc.) in Data Science
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Proseminar (B. Sc.) and Seminar (M. Sc.) in Data Science
Data science is the field of study that combines domain expertise, programming skills, and knowledge of maths and statistics to extract meaningful insights from data. In this seminar, we will focus on Data Science methods and tools. Examples include machine learning models, data visualization, model selection, clustering, and forecasting. We will also review best practices in scientific writing. Students are welcome to propose seminar topics based on their personal interests. Basic experience in R programming is desirable but not mandatory. Please indicate your level of programming expertise. Individual assignments will consist of a specific problem from data science. Grading will be based on a seminar paper and an oral presentation.
Exemplary topics include, but are not limited to:
- Applying a data science method to a dataset (e.g. predicting movie ratings based on movie reviews)
- Presenting an R package
- Presenting a data science / machine learning method
Organization:
- Lecturer: Prof. Dr. Nicolas Pröllochs (BWL XI)
- Course format: Seminar / Proseminar
- Term: Winter semester 24 / 25 (Proseminar) / summer semester 25 (Seminar)
- Language: German
- Grading: Seminar paper & presentation
- Bachelor's and Master's Theses
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Bachelor's and Master's Theses
If you are interested in writing your thesis in the field of Data Science, please apply according to the instructions on the website of the Examination Office. Information on the application procedure and deadlines can be found on the website of the Examination Office.
If your thesis application has been approved, please send an e-mail with the following documents to datascience@wirtschaft.uni-giessen.de:- Completed survey form (see Stud.IP); this contains information on your preferred subject areas/suggested topics, the type of work (empirical work/literary work) and your previous knowledge.
- Proof of performance (FlexNow printout)
Examples of possible topics (other topics possible):
- Social Media Analysis (z.B. Twitter)
- Financial Data Science
- Data Science in E-Commerce
- Data Science in Marketing
- Text Mining & Natural Language Processing
- Machine Learning & AI
- Visual Analytics & Computer Vision
- ...
In all areas, both empirical / methodological work and literature papers are possible. Your own topic suggestions are welcome, but should fit into the area of Data Science.
Further information: Flyer
