LFU:online

We will cover the following topics:

• Data preparation and data wrangling in R
• Data analytics in R
• Data-based decision-makingg

In this class, students will acquire the ability to independently and systematically prepare and analyze various types of data to make better managerial decisions. The content of the course focuses on data analysis in Strategic Management.

In this class, students will acquire the ability to independently and systematically prepare and analyze various types of data to make better strategic decisions. The content of the course focuses on data analysis in Strategic Management. More specifically, it covers data collection and processing, exploratory data analysis, data visualization, inferential statistics, modeling, and effective communication of results. In each session we will apply the R programming language and thereby collect basic programming skills. In terms of content, the course consists of two parts:

1. Data preparation and descriptive statistics
2. Data modelling and regression analysis

The course will be taught in a workshop style. Learning video and other online resources provide input for the class. In the seminar, this input is recapitulated, and assignments will be discussed in detail in plenary. In addition, students will work on a group project where they have the opportunity to apply their skills and the knowledge they gained throughout the course.

• In-class participation and assignments: 30%
• Mid-term exam: 30%
• Group presentations: 30%
• Peer-rating: 10%

• Çetinkaya-Rundel, M. & Hardin, J. (2021). Introduction to Modern Statistics. Open Intro. Available at: https://openintro-ims.netlify.app/index.html.
• Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani: An Introduction to Statistical Learning with Applications in R. Springer. Available at: https://web.stanford.edu/~hastie/ISLR2/ISLRv2_website.pdf.
• Wickham, H., & Grolemund, G. (2016). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O'Reilly Media, Inc. Available at: https://r4ds.had.co.nz/.

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Registration for courses in the Master's program is done via LFU:online without exception. There is a wide choice of different SE, UE or PS within the individual modules - please indicate your preferences here. IMPORTANT: It is NOT NECESSARY to enroll for the VO, VU or EX of a module, as it will be automatically confirmed, if there is an enrollment for the SE, UE or PS: https://lfuonline.uibk.ac.at/public/lfuonline_lv.home