R Language Programming for Excel Users
R Language is a vital skill for scientists, as evidenced by R's rapid rise in popularity.
Not surprisingly, we teach the R language used in programming in our Bootcamp. However, per our mission of “data science for everyone,” most of our students do not have extensive programming
backgrounds. Even with our students that code, R language skills are quite rare. Fortunately, our students universally share skills in using Microsoft Excel for various analytical scenarios. It is my belief that Excel skills are an excellent
foundation for learning R. Some example of this include:
- The core concept of working with data in Excel is the use of tables – this is exactly the same in R.
- Another core Excel concept is the application of functions to subsets of data in a table – again, this is exactly the same in R.
I have a hypothesis that our experiences teaching Data Science around the world are indicative of the market at large. That is, there are many, many Business Analysts, Data Analysts, Product Managers, etc. looking to expand their analytical skills beyond
Excel, but do not have extensive programming backgrounds.
Aspiring Data Scientist? You need to learn to code!
Understanding the programming language, R, is a vital skill for the aspiring Data Scientist as evidenced by R’s rapid rise in popularity.
While the R language ranks behind languages like Java and Python, it has overtaken languages like C#. This is remarkable as R is not a general purpose programming language. This is a testament to the power and utility of R language for Data Science.
Not surprisingly, when I mentor folks that are interested into moving into data science one of the first things I determine is their level of coding experience. Invariably, my advice falls along one of two paths:
- If the aspiring Data Scientist already knows Python, I advise sticking with Python.
- Otherwise, I advise the aspiring Data Scientist to learn R.
To be transparent, I use both R and Python in my work. However, I will freely admit to having a preference for R. In general, I have found the learning curve easier because R was designed from the ground up by statisticians to work with data. Again,
R’s rapid rise in popularity as a dedicated language for data is evidence that others feel similarly.