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Poslední úprava: Mgr. Jana Dlouhá (19.12.2021)
Data science is a combination of various fields, including mathematics, statistics, computer science, information science, machine learning and artificial intelligence. An article in the Harward Business Review refers to data science as "The Sexiest Job of the 21st Century" (Davenport & Patil, 2012). The most commonly used tools in this area are Python, SQL and R. R is a programming language and environment designed for statistical analysis of data and their graphical display. It is an implementation of the programming language S under a free license. Because it's free, R has already outpaced commercial software such as SPSS in terms of users. At the same time, it provides users with a number of features beyond the free software, such as Jasp or Jam. The functionality of the R environment can be extended using libraries called packages, of which more than 15,000 are available in the CRAN repository. R is thus very variable and can be used for a number of different tasks. Davenport, Thomas H., and D. J. Patil. "Data Scientist: The Sexiest Job of the 21st Century." Harvard Business Review 90, no. 10 (October 2012): 70–76. Rodriguez Salgado, J. J. (2021, December 9). What does a data scientist do? breaking down the responsibilities of data scientists. DataCamp Community. Retrieved December 19, 2021, from https://www.datacamp.com/community/blog/what-does-a-data-scientist-do |
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Poslední úprava: Mgr. Jana Dlouhá (13.01.2022)
Required: Wickham, H., & Grolemund, G. (2017). R for data science: import, tidy, transform, visualize and model data. O'Reilly. URL: https://r4ds.had.co.nz/index.html R package documentation - https://www.rdocumentation.org/ (used packages) R manuals https://cran.r-project.org/manuals.html Recommended: Mair, P. (2018). Modern Psychometrics with R. In Use R! Springer International Publishing. https://doi.org/10.1007/978-3-319-93177-7 Grolemund, G., & Wickham, H. (2017). R for Data Science. O’Reilly Media. https://r4ds.had.co.nz/ Zamora Saiz, A., Quesada González, C., Hurtado Gil, L., & Mondéjar Ruiz, D. (2020). An Introduction to Data Analysis in R. In Use R! Springer International Publishing. https://doi.org/10.1007/978-3-030-48997-7 (selected chapters) R Document Collections, Journals and Proceedings https://www.r-project.org/other-docs.html (including a list of books and other publications related to R) |
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Poslední úprava: Mgr. Jana Dlouhá (13.01.2022)
Attendance is not mandatory, but highly recommended. Credit will be awarded to students who actively participate in a reasonable number of lectures and exercises. Attendance can be compensated by completing assigned tasks and reading. The exam will take place at the agreed date at the end of the semester. Each student will be assigned a case study based on the knowledge covered during the course. Students perform an analysis and briefly (10 min) present their procedure and conclusions to their classmates. Exam evaluation
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Poslední úprava: Mgr. Jana Dlouhá (21.02.2023)
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Poslední úprava: Mgr. Jana Dlouhá (13.01.2022)
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