MOD3a Data Science Tools - Einzelansicht

a) Data Science Tools:

● Overview of software tools for data science

● Version control and joint software development using github

● Creating reports and websites with (R)markdown

● Dynamic data analysis with R, markdown and knitr

● Automated processing using the Shell

● Scraping data from the internet

● Relational databases for spatial and non-spatial databases (PostgreSQL, PostGIS)

● Parallel computing and working with servers

● Specific approaches of data analysis: Bayesian statistics, Generalized and linear mixed models, Artificial neural networks and Deep learning, Non-linearity and GAMs, Advanced tools for 74 multivariate analysis

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 know and can apply current tools for modern data science. They have the ability of retrieving, handling, pre-processing and analysing complex data sets from the social and natural sciences. Moreover, they are capable of setting up a computer environment and workflow for a data analysis problem from scratch.

Admission to the M.Sc. program

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