Alexander Rieber (Ulm University)
Here we host the lecture notes and problem sets for the "Projektkurs Data Science und Business Analytics" at Ulm University. This undergraduate economics course will teach students how to use real world data, analyse this data with R and build arguments around their analysis.
We have quite a heterogeneous composition in class, however basically non of our students has a deep understanding of R, most of them have never used R before. Hence we start very basic and aim at providing students with enough knowledge to do an entire project in R. We provide a sample project in the "case-study" folder to:
a) guide students on how the structure a project b) show what they will to in this course
To get students up and running R quickly we do weekly assignments where they have to solve problem sets in R on their own. If you like R programming, take a look at the folder Problem-Sets. All Problem-Sets are created using RTutor. We provide only the Problem Sets and not the solution files to these problem sets. If you are an instructor and plan to build on our problem sets we are happy to send you the solution files. Just write me an E-mail.
We also host these problem sets on RStudio Cloud. If you want to test the problem sets you can create a free account on RStudio Cloud and follow the links provided for each problem set below.
The topics include the following:
- Problem Set 1: Basic introduction to R and a short introduction to ggplot as well as a small introduction to data frames. Here is the link to the RStudio Cloud to test this problem set
- Problem Set 2: Basic introduction how to get data into R, join different datasets and manipulate datasets. Here we introduce the basic dyplr syntax. Here is the link to the RStudio Cloud to test this problem set
- Problem Set 3: Introduction to ggplot and how to make plots effective and visually appealing. Animated graphs as well as maps are also included in this section. Here is the link to the RStudio Cloud to test this problem set
- Problem Set 4: Introduction to linear regression, standard errors, confidence intervals and the interpretation of coefficients. Here is the link to the RStudio Cloud to test this problem set
- Problem Set 5: Introduction to causality with monte-carlo simulations and real world data. Here is the link to the RStudio Cloud to test this problem set
- Problem Set 6: Extend on the notion of causality using an experiment and instrumental variable regression to show students when it is possible to interprete coefficients causally.
If you like to give these problem sets a try just klick on the above link for every problem set and it will open up an RStudio Cloud project with the respective problem set.
For more Problem Sets and a thourough description of RTutor visit: https://github.com/skranz/RTutor.
Julius Düker made some introduction videos detailing
- How to run an interactive Problem set in the Browser using RStudio Cloud
- How to run an interactive Problem Set in RMarkdown using RStudio Cloud
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Except for the RTutor problem sets, you can use all materials under a Creative Commmons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
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You can also use and share the RTutor problem sets similar to the (CC BY-NC-SA 4.0) license with one restriction: You are not allowed to use or share the problem sets if you make or plan to make publicly available or provide a link to any solutions of these problem sets. (To motivate students to solve the problem sets, they are grade-relevant to a small extend. Therefore I don't want solutions too easily available.)
- The problem sets have been created by Julius Düker, Sebastian Kranz and Alexander Rieber.