What (if anything) has changed about what you think a data scientist is and what they do
Some things that I left out in my initial explanation of what a data scientist does:

• I think that a data scientist should be able to glean insight from data. A great data scientist is a great storyteller, someone who can distinguish the signal from the noise in a dataset. This means finding the trends in a dataset that best explain outside phenomenon.
• A data scientist should also be able to use data to make predictions about the future. This is usually done in the form of machine learning.

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 will definitely be using R for data science in the future. I had primarily used Python for data science before this class, but really like the flexibility of R. I think that statistical libraries in R are the most diverse of any language, and I really enjoyed working in R Shiny as a way to publish insights in an interactive way. Having worked with Pandas a lot in the past, I found the tidyverse to be very comparable.

What things are you going to do differently in practice now that you’ve had this course?
After this course, I am much more comfortable using R for data science, and will be able to take better advantage of the libraries available for statistics. I’ll also be more comfortable taking advantage of docker containers to publish work and share/collaborate with others.