Is Excel sufficient for data science? 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.
First of all check out this post. It has many reasons why Excel is inferior to other solutions, regarding data science tasks. Excel also can't handle large datasets (hundreds of thousands of records - not to mention anything in the vicinity of Big Data), image and sound data.

Excel is good for simple tasks concerning spreadsheets; it emphasizes more on presentation and ease of use, while having minimal support for actually analysing the data. Unless all you want to do is to calculate simple statistical measures (mean, average, etc) or building a very simple model (e.g. linear regression), Excel is inefficient. That being said, 99% the work a company has to deal with concerning data is simple enough to be manageable through Excel.

However Data Science mainly deals with regression, classification and complex models that excel isn't equipped to handle! If your students want to have a look at data science you need to teach them a tool that will be useful to them (R, python, etc.). These languages also have libraries with tons of built in models to "play with".

Another really huge reason I would go with the latter options is that they are open source. I personally feel that open source software should be preferred from an educational standpoint to proprietary solutions (this is also why I suggest python and R over Matlab)!