Blog Post 5
Bryan Bittner 2022-07-20
rmarkdown::render("_Rmd/2022-07-20-blog-post-Module12.Rmd",
output_format = "github_document",
output_dir = "./_posts",
output_options = list(
html_preview= FALSE
)
)
Blog Post 5
Question: 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?
Answer: I think R is a game changer when it comes to Data Science. The two most popular languages are definitely going to be either R or Python. As a student that has now taken both R and Python course, it is easy to see why. In my humble opinion, R is better when it comes to use as more traditional statistical programming language. Python is more of a versatile language in that it seems to have traditional programming features in addition with the statistical options.
As a Reddit lurker some of my favorite posts deal with R vs Python. One of my favorite posts said to learn when you should program things in Python and when you should program in R, then, program it all in Python.
I myself am lucky enough to be in a position of being able to work using either language. Currently I am using Python, but that Caret package almost made me change my mind. For now I will stick with Python but I will keep R close in my back pocket.
Question: What things are you going to do differently in practice now that you’ve had this course?
Answer: First of all this course gave me another extremely useful tool in my data science tool kit. The importance of that should not be understated. I think the realistic project examples that we worked on help cement what I need to do to complete a project for work. It was nice to work on a real data science project from start to finish and see the outcome. The lessons learned here were used immediately as am currently working through a revenue forecasting project.
Question? What areas of statistics/data science are you thinking about exploring further?
Answer? As of now I have three courses remaining in my masters program, so my immediate plan is to finish those up and hopefully earn my degree. That is the point where the formal learning ends and the self-learning begins. We have only covered a handful of model types so I would imagine that I will be digging deeper into the thousands of other model types. Additionally I think that Time Series analysis would be extremely beneficial in my career so that will also be something I need to learn more about.