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Last update: Mgr. Ondřej Tichý, Ph.D. (28.09.2019)
<br> The classes will usually start with a short introduction of an (usually) online and (usually) publicly available data source, the method of its mining and continues with the tools and techniques allowing its users to exploit the mined data for further analysis. The class may end with examples of such analyses, but those are not the focus of the course. Students looking for data analysis courses may consider e.g. Statistics (not only with R) for corpus and quantitative linguistics (AMLV00046), English Diachronic Corpora (AAA500147) etc.<br> <br> The tools students will train with may include (but are not limited to): text editors with advanced RegExp capabilities (e.g. JEdit, EditPad), XML editors and processors (JEdit or oXygen), spreadsheet processor (Microsoft Excel), relational databases (MS Excel PowerPivot, MySQL), programming languages for text processing (Perl, Python, R) etc. The choice of the actual tools and techniques depends on the class composition, student interests and needs (feel free to bring in your own projects).<br> <br> <br> |
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Last update: PhDr. Gabriela Brůhová, Ph.D. (25.09.2020)
Credit is awarded for regular attendance (no more than 3 missed classes per semester), activity in class and for passing a final practical exercise in data processing. Attendance is compulsory, max 3 absences per semester are allowed. Any additional absence during the pandemic must be remedied by additional work by individual arrangement with the course instructor. |