Many companies have access to mountains of data and increasingly recognize the importance of turning these data into insights. This development leads to a pressing need for data analysts and explains the growing popularity of data analytics software. This course introduces participants to one such software package, namely the open-source programming environment R. Through lectures and interactive, assignment-based, tutorials, students will be introduced to three crucial aspects of data analytics in R: (i) basic objects and operations in R; (ii) flow control and programming in R; and (iii) working with data in R (management, analysis, and visualization).
The course offers a good foundation for individuals who intend to get involved in data analytics (e.g., start a business intelligence/analytics master program or plan to take other, independent data analytics courses), but have little or no coding/programming experience. The skills acquired in this course are also useful for data analytics in syntax-driven environments other than R.
ECTS
The number of credits earned after successfully concluding this course is the equivalent of 2 ECTS according to Maastricht University’s guidelines. For further information see the MSS terms and conditions.
Goals
- Get to know the open-source language R and the accompanying developers’ environment RStudio;
- Become familiar with the basic objects and operations in R;
- Get introduced to programming and algorithmic thinking;
- Learn to work with data in R.
Coordinator: Dr. B. Foubert
Bram Foubert is Associate Professor at the Department of Marketing and Supply Chain Management at Maastricht University School of Business and Economics (SBE), where he teaches courses in data analytics and econometrics. His research interests are in the areas of consumer response modelling and retailing. More specifically, he investigates how marketing instruments, such as sales promotion, store environment, or advertising, affect purchase and consumption behavior and customer life time. His research has appeared in the Journal of Marketing, Journal of Marketing Research, Journal of Retailing, and Marketing Science, among others.
Prerequisites
- Knowledge of algebra (high-school level)
- Familiarity with datasets (e.g., in Excel or SPSS)
- No prior coding or programming experience needed
Recommended literature
The course is entirely self-contained but will make use of the statistical programming environment R:
- www.r-project.org
- www.rstudio.com
Teaching methods
- Assignments
- Lectures
- Presentations
- Skills
- Work in subgroups
Assessment methods
- Assignment
- Attendance
For further information, please click the "LINK TO ORIGINAL" button below.