For data scientists, their main reason for taking the class was clear — they’re constantly working with data, and learning R will gives them a more effective and flexible way of working with data. Also, learning R will come more naturally as they have a lot of opportunity to practice the language while at the same time making a direct impact on their work.
When trying to understand why the some of the other students signed up for the class there were a variety of reasons, for example:
- Engineers who wanted to be able to improve their ability to modify and visualize data.
- Operations and finance looking for an alternative for repetitive daily/weekly Excel updates.
- People who are already familiar with R but wanted to freshen up their knowledge and learn how to use it effectively at Facebook.
In the three examples above, we see ways that non-data scientists can gain value from learning R. These tangible use cases are great things to have to keep focused because learning R takes a fair amount of persistence. Broadly, you want to be in one of these two categories if you’re not a data scientist/analyst:
- You’re already doing something and it can be improved/made faster by learning R
- You want to do something but it will be very difficult/impossible without knowing R (or some other programming language)
One last point on this topic —sometimes R is not the best tool for the job. For example, if you already know how to use SQL+Excel you already have a deadly duo of tools to aggregate, analyze, and visualize data. Having used R myself for around 7 years, I often find myself resorting to SQL + Excel simply because it’s faster and more sharable. So if you spend a lot of time learning R, don’t feel like you need to use it for everything because sometimes it will actually take twice as long then if you use tools you’re already an expert in.