I'm in the process of preparing to teach an introductory course on data science using the R programming language. My audience is undergraduate students majoring in business subjects. A typical business undergrad does not have any computer programming experience, but has taken a few classes which use Excel.
Personally, I am very comfortable with R (or other programming languages) because I majored in computer science. However, I have the feeling that many of my students will feel wary of learning a programming language because it may seem difficult to them.
I do have some familiarity with Excel, and it is my belief that while Excel can be useful for simple data science, it is necessary for students to learn a serious programming language for data science (e.g., R or Python). How do I convince myself and the students that Excel is insufficient for a serious business student studying data science, and that it is necessary for them to learn some programming?
Edited in response to comment
Here are some of the topics that I will be covering:
- Data processing and data cleaning
- How to manipulate a data table, e.g., select a subset of rows (filter), add new variables (mutate), sort rows by columns
- SQL joins using the dplyr package
- How to draw plots (scatter plots, bar plots, histograms etc.) using the ggplot2 package
- How to estimate and interpret statistical models such as linear regression, logistic regression, classification trees, and k-nearest neighbors
Because I don't know Excel very well, I don't know whether all of these tasks can be done easily in Excel.