Reflection
What your current thoughts are in terms of using R for data science - do you think you’ll continue to use R going forward? Why or why not?
I have really enjoyed learning more about R and using it for data science in this course. R does appear to be a very efficient platform for most things data science related. I do not have much exposure to other programming languages besides R and SAS, but between the two R definitely seems to provide more opportunities for more complex problems and methods. Through using github and docker, communication and collaboration with others is made very accessible. I do think I will continue to use R going forward as I have seen how efficient it is and how many new methods and applications there are to try.
What things are you going to do differently in practice now that you’ve had this course?
The biggest thing that I will do differently now that I’ve had this course is to take advantage of github. For collaborative projects, this platform has made communicating, identifying changes, and accessing files very easy and time efficient. It is something I wish I had used with previous group projects and hope to use more of in the future. Another thing that I will do differently in practice is take advantage of the tidyverse package. This is a package that I had explored very little of before taking this course. The advantages of working in the tidyverse are monumental.
What areas of statistics/data science are you thinking about exploring further?
One unsupervised learning method that I would like to explore further is Principal Component Analysis. In my consulting class, one group project is using this method to guide their research. I am very interested to see how this method is used in different contexts. The entire machine learning portion of this class was very interesting and is something I would like to explore more. Another topic of interest to me is collaboration. I’d love to explore more ways to communicate more efficiently with others when programming.