Business Analytics with R – Best Practices for Successful Course Design
Katarzyna Toskin Southern Connecticut State University
Abstract R is one of the top programming languages used in Data Science. More specifically it is widely used for data analysis and visualization, statistical computing, and machine learning. R is an open source programming language issued under the General User License. Therefore, unlike commercial data analytics programs, it is easy to install and free to use. In addition, R has over 18000 packages available for a variety of tasks as well as an Integrated Development Environment called RStudio that includes many great features, tools, and utilities all of which make its learning and use straightforward and enjoyable.
Top tier companies such as Facebook, Google, Twitter, Microsoft, Uber and many more use R for analytics. Shin (2021) completed an in-depth analysis of the most in-demand skills from web scraping over 15,000 Data Scientist job postings. His findings revealed that 54% of the postings listed R as a third most in demand skill, after Python and SQL. However, R is also extensively used in academic research. Therefore, many of the world’s top universities teach their students R as it continues to gain popularity.
Undoubtedly there is a high demand for R statistical language. It is a skill that students should be exposed to while perusing their degrees especially in analytics. But designing a new course is challenging and often raises many questions from what textbook to use to deciding on the format of the course, and the scope of learning objectives. In my proposed presentation, I will discuss my experience with designing and teaching an undergraduate business analytics course using R. I will share my approach, examples, and student feedback.