JEM221 - Data Science with R I

Credit: 5
Status: Bachelors - All
Bachelors - elective
BEF - elective
CFS - elective
ET - elective
F,FM and B - elective
Masters - all
MEF - elective
Semester - winter
Course supervisors: prof. PhDr. Ladislav Krištoufek Ph.D.
Course homepage: JEM221
Literature: Mandatory literature:

- Ledolter, Johannes (2013): Data Mining and Business Analytics with R, John Wiley & Sons, Hoboken, New Jersey, NJ, USA
- Toomey, Dan (2014): R for Data Science, Packt Publishing Ltd., Birmingham, UK
- Zumel, Nina & Mount, John (2014): Practical Data Science with R, Manning Publications Co., Shelter Island, NY, USA

Additional suggested literature:

- Grolemung, Garret (2014): Hands-On Programming with R, O'Reilly Media Inc., Sebastopol, CA, USA
- Ojeda, Tony et al. (2014): Practical Data Science Cookbook, Packt Publishing Ltd., Birmingham, UK

On-line sources:
Description: Introductory course to Data Science with applications in the R programming environment. Special focus is put on understanding of basic practical programming in R, covering model evaluation, memorization methods, advanced regression techniques, and training variance reduction. The Data Science with R I course will be followed by Data Science with R II covering clustering, text mining, support vector machines, neural networks, and networks.




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