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Last update: prof. Mgr. Vojtěch Janoušek, Ph.D. (04.03.2019)
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Last update: doc. RNDr. Petr Jeřábek, Ph.D. (14.03.2019)
Learning materials (only for students): https://www.natur.cuni.cz/geologie/petrologie/vyukove-materialy/analyza-dat-v-prostredi-r-a-python Web links: de Vries A: Using R with Jupyter Notebooks http://blog.revolutionanalytics.com/2015/09/using-r-with-jupyter-notebooks.html Jupyter: Open source, interactive data science and scientific computing across over 40 programming languages http://jupyter.org/ The R Project for Statistical Computing https://www.r-project.org/ Dive into Python 3 http://www.diveintopython3.net/ Scientific Python Lecture Notes http://www.scipy-lectures.org Wikipedie: R (programming language) https://en.wikipedia.org/wiki/R_(programming_language) Wikipedie: Python (programming language) https://en.wikipedia.org/wiki/Python_(programming_language)
Literature: Becker RA, Chambers JM, Wilks AR (1988) The New S Language. Chapman & Hall, London, pp 1-702 Crawley MJ (2007) The R book. John Wiley & Sons, Chichester, pp 1-942 Janoušek V, Moyen JF, Martin H, Erban V, Farrow C (2016) Geochemical Modelling of Igneous Processes - Principles and Recipes in R Language. Bringing the Power of R to a Geochemical Community. Springer-Verlag, Berlin, Heidelberg, pp 1-346 Langtangen, H P (2016) A Primer On Scientific Programming With Python, Texts in Computational Science and Engineering, pp 1-992 Maindonald J, Braun J (2003) Data Analysis and Graphics Using R. Cambridge University Press, Cambridge, pp 1-386 Murrell P (2005) R Graphics. Chapman & Hall/CRC, London, pp 1-328 Rollinson HR (1993) Using Geochemical Data: Evaluation, Presentation, Interpretation. Longman, London, pp 1-352 Rossant C (2015) Learning IPython for Interactive Computing and Data Visualization - Second Edition, Packt Publishing, pp 1-175 |
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Last update: doc. RNDr. Petr Jeřábek, Ph.D. (14.03.2019)
The examination is a practical test, whereby the participants are required to write several short programs in the R and Python programming languages. |
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Last update: prof. Mgr. Vojtěch Janoušek, Ph.D. (01.10.2020)
1 Introduction to data analysis and algorithmization I. [OL]
2. Introduction to data analysis and algorithmization II. [VJ]
3. Fundamentals of the Python language I. [OL] Introduction to Jupyter Notebooks and JupyterLab Python crash course, basics of Python programming
4. Fundamentals of the Python language II. [OL] Scientific Python
5. Fundamentals of the R language I. [VJ]
Introduction, fundamental data types and basic operations with them
6. Fundamentals of the R language II. [VJ] Programming and graphics
7. Python applications I. [OL] Calculations and statistics
8. Python applications II. Directional statistics
9. R applications I. [VJ] Calculations and statistics
10. R applications II. [VJ]
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