Seminar on Business Analytics

Type and Deduction

B.A master's program: required elective subject within the main focus Business Analytics

Requirements

No requirements given. The number of participants is limited to 20+X.

Course Language

German (Englisch for single groups)

Scope of Deduction
2 SWS, 6 CP

Lecturers

Dr. Kai Brüssau

Appointments during Winter Term 2024/25

Registration phase (registration via STiNE): Mo., 03.06., 9:00 am – Wed., 12.06., 1:00 pm
Allocation of left-over seats: Thur., 27.06. – Fr., 28.06.
Kick-off meeting and topic choice: Mo., 08.07., 1:00 – 2:00 pm, room 1004,
Deadline for choosing a topic (four topics including their priority): Fri., 12.07., 1:00 pm to kai.bruessau"AT"uni-hamburg.de
Outline discussions: upon appointment
Interim presentations of the work progress: upon appointment
Submission deadline of the 1st version of seminar thesis: Mo., 25.11., 1:00 pm
Review phase including review submission: Tue., 26.11. – Fri., 29.11., 1:00 pm
Submission of the final version of seminar thesis incl. reply to reviews: Mo., 9.12., 1:00 pm
Presentations (usually we do not need more than two appointments – nevertheless please block all dates and times):
- Fri., 13.12., 4:00 – 8:00 pm/
- Sat., 14.12., 9:00 am – 6:00 pm/
- Sun., 15.12., 9:00 am – 4:00 pm , room WiWi 2054/55

Please check the "Downloads and Infos" section on the right side and click on the topic list for further details concerning this seminar.

The seminar in Business Analytics comprises topics from the areas Business Analytics and Data Mining.
In accordance with your supervisor the focal point of your seminar thesis can be based on theoretic and scientific literature-based findings or it can be based on the practical implementation of a Data Mining task. In the latter case, the theoretical and scientific part of the thesis is smaller. The evaluation of your solutions and the resulting knowledge gain are given more room in this kind of thesis.
The seminar thesis can be written either in German or in English.

The following topics are available:

Data-Mining in Logistics
Data-Mining in E-Commerce
Web Scraping and Natural Language Processing
Application of Large Language Models
- Recommender Systems
- Categorization of products, providers or other entities
- Topic Modeling
- other applications
Forecast procedures for Machine Learning and their application on a self-chosen example (e.g. by kaggle.com)
Using Decision Trees, Random Forest and Boosting for a classification and depicting it with your own example (e.g. by kaggle.com)
Using Logistic Regression and Support Vector Machines for a classification and depicting it with your own example (e.g. by kaggle.com)
Convolutional Neural Networksfor image recognition and its uses
Reinforcement Learning and its applications
You can also coose your own topic in accordance with your supervisor

Amaratunga, T.: Understanding Large Language Models, Apress Berkeley, CA, 2023, https://doi.org/10.1007/979-8-8688-0017-7

Charu C. Aggarwal: Data Mining - The Textbook, Springer, 2015, DOI: 10.1007/978-3-319-14142-8.

Gorunescu, F.: Data Mining - Concepts, Models and Techniques, Springer, 2011, DOI: 10.1007/978-3-642-19721-5.

Hastie, T.; Tibshirani, R.; Friedman, J. (2009): The Elements of Statistical Learning, Springer Science+Business Media. (Aktuellste und korrigierte Version von 2017.

Kordon, A. K.: Applying Data Science. How to Create Value with Artificial Intelligence, Springer Nature Switzerland, 2020.

McTear, M.; Ashurkina M.: Transforming Conversational AI, Apress Berkeley, CA, 2024, https://doi.org/10.1007/979-8-8688-0110-5

Qamar, U. , Raza, M.S. : Data Science Concepts and Techniques with Applications, Springer, 2023.

Rebala, G.; Ravi, A.; Churiwala, S. : An Introduction to Machine Learning, Springer Nature Switzerland, 2019.

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin: Attention Is All You Need, 2017, https://arxiv.org/abs/1706.03762.

Websites, such as „Towards Data Science“ or „Medium“, my be used for familiarizing yourself to the topic, but not necessarily for quoting.