Citation & licence

The Quantitative Research Methods course materials are based on, and follow the structure of, the textbook An Introduction to Statistical Learning, with Applications in Python. If you reuse them, please cite the source textbook.

James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An Introduction to Statistical Learning, with Applications in Python. Springer Texts in Statistics. Springer. https://doi.org/10.1007/978-3-031-38747-0

@book{islp2023,
  title     = {An Introduction to Statistical Learning: with Applications in Python},
  author    = {James, Gareth and Witten, Daniela and Hastie, Trevor and Tibshirani, Robert and Taylor, Jonathan},
  year      = {2023},
  publisher = {Springer},
  series    = {Springer Texts in Statistics},
  isbn      = {978-3-031-38746-3},
  doi       = {10.1007/978-3-031-38747-0},
  url       = {https://www.statlearning.com}
}

Citing these materials

The repository ships a CITATION.cff file, so GitHub’s “Cite this repository” button produces a correct entry automatically. In prose:

Weisser, C. (2026). Quantitative Research Methods — Lecture Materials. Hochschule Bielefeld (HSBI). https://github.com/ChrisW09/Quantitative-Research-Methods

Please cite the textbook alongside it.

Attribution

The slides, exercises, mock exams and notebooks in this repository were prepared by Prof. Dr. Christoph Weisser (HSBI), Summer Semester 2026.

The ISLP textbook, its text and its figures are © the authors / Springer. The datasets are distributed by the authors at statlearning.com for use with the book. The copyrighted textbook PDF and figure banks are not included in this repository (Source_Material/ is git-ignored) — see Repository layout.

Slides that reproduce a textbook figure attribute it to its source.

Contact

Questions about the materials, or an instructor request for the mock exams and runsheets: info@profweisser-ai.de.

About the author

I am Christoph Weisser, Professor of Mathematics, specializing in Business Data Science at Hochschule Bielefeld (HSBI), and former Technical Lead Analytics & Artificial Intelligence at BASF. My work focuses on Artificial Intelligence, Generative AI, Business Data Science, and agentic AI systems that bridge research with real-world industrial applications.

Before joining academia, I led international AI initiatives at BASF from strategy through production deployment. Today, I combine research, teaching, open-source software development, and selected industry collaborations to advance the practical application of AI.

I hold two master’s degrees from the University of Oxford and the University of St Andrews and completed the PhD Program in Applied Statistics & Empirical Methods (summa cum laude) at Georg-August-Universität Göttingen. I was awarded scholarships by the Studienstiftung des deutschen Volkes, the Konrad-Adenauer-Stiftung, and the Evangelisches Studienwerk Villigst. I regularly publish research in leading journals and at international conferences and contribute to open-source software.