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
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Several persons are involved in the preparation and delivery of this module: Prof. Ralf Schäfer, Andreas Scharmüller, Lucas Streib, Dr. Mira Kattwinkel, Dr. Verena Schreiner, Jonathan Jupke, Stefan Kunz and Dr. Nanki Sidhu.
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and of course a huge thanks to all the package authors and the whole programming community as well as the stackexchange and stackoverflow community.