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Lecturer(s)
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Podlipský Václav Jonáš, Mgr. Ph.D.
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Course content
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The content of the course is chosen for each semester. See the course objectives.
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Learning activities and teaching methods
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Dialogic Lecture (Discussion, Dialog, Brainstorming), Activating (Simulations, Games, Dramatization)
- Homework for Teaching
- 42 hours per semester
- Attendace
- 18 hours per semester
- Semestral Work
- 40 hours per semester
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Learning outcomes
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Scripting and Data Analysis in Linguistics (SDAL / AFO4 / AF11) Tuesdays, 11:30 Instructor: Jonáš Podlipský No prior experience? No problem! This hands-on, beginner-friendly course will help you gain essential skills for linguistsand professionals in many other fields: coding in R and statistical analysis. What you'll learn: the basics of handling, exploring, and visualizing data in the R programming language, the most widely used data analysis tool, and building foundational statistical models. Why take this course? Coding and data analysis are in-demand skills across a wide range of careers. Whether you're passionate about linguistics or eager to enhance your skill set, this course is designed for curious, motivated students ready to discover the power of data. We'll start from the very beginning, so no prior experience with programming or statistics is required! The course mainly rely on the following book most of which we will read during the semester: Winter, B. (2020). Statistics for Linguists: An Introduction Using R. Routledge. The course has two main goals: 1. To introduce students to basics of statistical analysis (of linguistic and other types of data) using R, including the creation of various types of graphs. Specifically, we will focus on importing data into R, transforming data into appropriate formats, inspecting raw data, conducting basic descriptive statistics (measures of central tendency and variability), and visualizing raw data using different types of plots. 2. The main aim of the course is to provide students with an understanding of regression modeling. We will practice the use and interpretation of basic linear models, as well as linear mixed models and generalized linear mixed models.
Students will gain insight into the area selected for the given semester: the basics of R scripting and statistical analysis.
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Prerequisites
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No prior expert knowledge is required. It is assumed that students will be capabel of working with data, various graphs, and computer code.
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Assessment methods and criteria
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Student performance, Systematic Observation of Student
Active participation in the seminars, submitting assignments throughou the semester, reading the assigned chapters, presentation of a final project, passing a final test (which will be waived for well-working students).
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Recommended literature
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Winter, Bodo. Statistics for Linguists: An Introduction Using R. Rourledge. 2020.
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