MOD3b Advanced Problems in Data Science - Einzelansicht

In the first part of the course, the students develop and discuss theoretical solutions for selected data analysis problems. In the second part of the course, they apply the tools and knowledge acquired in the module during an own project.

Basic and advanced reading:
● Gandrud C. (2014) Reproducible research with R and R Studio. CRC Press/Taylor & Francis Group, Boca Raton.
● Goodfellow I., Bengio Y. & Courville A. (2016). Deep learning. The MIT Press, Cambridge, Massachusetts.
● Haddock S.H.D. & Dunn C.W. (2011) Practical computing for biologists. Sinauer Associates, Sunderland, Mass.
● Matloff N.S. (2016) Parallel computing for data science: with examples in R, C++ and CUDA. CRC Press, Boca Raton.
● Obe, R., Hsu, L. (2011): PostGIS in Action. Manning Publications.
● Zarrelli G. (2017) Mastering Bash: automate daily tasks with Bash. Packt Publishing.

Targeted learning outcomes:

The students can reproducibly solve a complex data science problem during an own project. They know and can apply different solutions and approaches to typical data analysis research questions. They can automate repeated steps in the workflow, rendering the analysis more reproducible and efficient compared to manual handling.

Admission to the M.Sc. program

Project with presentation (Module exam), a) Successful completion of exercises (Study achievement)