/MOD3

GitHub for the MOD3 module (Advanced data analysis) of the university of Koblenz-Landau

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MOD3

Welcome to the repository for the MOD3 module (Advanced data analysis) of the MSc Environmental Sciences/Ecotoxicology at the University of Koblenz-Landau!

BEFORE COMING TO THE CLASS MAKE SURE YOU HAVE DOWNLOADED ALL THE NECESSARY MATERIAL IN THE TO-DOWNLOAD FILE

Please make sure to read the README files for each sub-folder.

Learning content

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 multivariate analysis

b. 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.

Acknowledgement