Course objectives:
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The goals of the course are to reduce dependence on paid statistical software; to learn the basics of the popular R programming language with its wealth of analytical capabilities and active global community; and to practice common procedures in statistical data analysis
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Requirements on student
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Credit awarded for individually evaluated online DataCamp exercises and presentation of an R package.
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Content
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R programming language, working with RStudio; creating and manipulating data objects; data import and cleaning; data description and exploration; data transformation; creating custom functions; iteration to reduce of duplicate code; dealing with missing data; power analysis; selected statistical models (e.g., correlations, linear regressions, hierarchical models); structural models (e.g., EFA, CFA, path analysis); reporting results.
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Activities
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Fields of study
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R for Data Science: https://r4ds.had.co.nz/
Hands-On Programming with R: https://rstudio-education.github.io/hopr/
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Guarantors and lecturers
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Literature
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Time requirements
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All forms of study
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Activities
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Time requirements for activity [h]
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Continuous tasks completion (incl. correspondence tasks)
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20
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Presentation (of works, projects, etc.)
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2
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Homework for lessons
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10
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Being present in classes
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20
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Total
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52
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Prerequisites
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Competences - students are expected to possess the following competences before the course commences to finish it successfully: |
Any introductory statistics course. |
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Learning outcomes
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Skills - skills resulting from the course: |
Student can work with data, filter and transform it. |
Student can perform basic statistical analyses and create graphical representations of data and statistics. |
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Assessment methods
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Knowledge - knowledge achieved by taking this course are verified by the following means: |
The form of assessment of course is subject pass ("zápočet"). The result of a subject pass is expressed on the scale: a) "započteno" (i.e. "pass"), b) "nezapočteno" (i.e. "fail"). |
IIB25 - Seminar work / report |
IC10 - Presentation in lessons (individual or group) |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
A1 - Lecture |
B1 - Discussion |
E-learning (tutorial, electronic study materials) |
A4 - Interview (asking questions when encoding and repeating the subject matter) |
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