| Nr. | LV-Typ(en) | LV-Titel | |
| 5945 | PI | Introduction to R 
Anmeldung über LPIS vom 29.02.2024 14:00 bis 02.03.2024 23:59 |
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| LV-Leiter/in | Anna Shcherbiak, M.A. |
| Semesterstunden | 2 |
| ECTS | 2 |
| Unterrichtssprache | Englisch |
| Termine | ||||
| Di, | 05.03.2024 | 09:00-10:30 Uhr | D5.1.002 (Lageplan) | |
| Sa, | 09.03.2024 | 10:00-16:00 Uhr | D5.1.002 (Lageplan) | |
| Di, | 12.03.2024 | 13:30-15:30 Uhr | D5.0.002 (Lageplan) | |
| Sa, | 16.03.2024 | 10:00-15:00 Uhr | D5.1.002 (Lageplan) | |
| Sa, | 13.04.2024 | 10:00-15:00 Uhr | D5.1.002 (Lageplan) | |
| Sa, | 20.04.2024 | 10:00-14:00 Uhr | D5.1.002 (Lageplan) | |
| Termindownload (ical) | Termine abonnieren | ||||
| Weitere Informationen | https://learn.wu.ac.at/vvz/24s/5945 |
| Kontakt: | ||
| anna.shcherbiak@wu.ac.at | ||
| Inhalte der LV: | ||
R is a freely available statistical computing environment and programming language. It has become a dominant workhorse for modern statistical research and data analysis and is being widely adopted in industrial data analytics as well. This course focuses on practical data science skills (loading data, data wrangling, visualization, basic statistical models) that you will use every day in your work. The primary goal of the course is to gain comfort with R, learn some best practices, and write code that you can understand and share with others. This course will be appropriate for new users as well as those who have basic familiarity with R, but aren’t comfortable conducting their day-to-day data tasks in R. The course is open to all interested WU students, but is particularly recommended for students entering the specialization Decision Sciences to prepare for the course "Empirical Data Analysis". Upon successful completion, you will receive 2 ECTS. |
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| Lernergebnisse (Learning Outcomes): | ||
On successful completion of the course, you should: Know how to obtain, install, and configure free and open-source statistical software from the Internet on the platform they are using. Know how to write and run R scripts, edit them in RStudio, view objects in the user space, and invoke help. Produce dynamic and reproducible reports with R Markdown Distinguish between data types, convert between numeric values, factors, and character strings. Know how to import data from Stata, SPSS, Excel, or a CSV file into R, and diagnose it for the presence of coding errors. Execute and interpret some basic statistics in R Visualize various types of data in R using the ggplot2 package |
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| Regelung zur Anwesenheit: | ||
Full attendance is expected for all lectures. If you cannot attend a lecture due to exceptional/unforeseen circumstances, please contact the lecturer by email, and we will deal with the absence on a case by case basis. |
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| Lehr-/Lerndesign: | ||
This course will use a combination of lectures, programming demonstrations, and assignments to solidify foundational programming skills. Lectures will be primarily PowerPoint based and made available on Canvas before class for downloading and reviewing. Most of the course will be focused on working with data, so bringing your own computer is encouraged. |
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| Leistung(en) für eine Beurteilung: | ||
in-class exercises -- 20% homework -- 35% individual take home exam -- 45% |
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