What are some examples of ways you've applied skills you've learned on DataCamp?
For example, I didn't know anything about the dplyr package. Coming from STATA, vectors in base R was tricky for me. But dplyr made everything suddenly doable and I could transfer a lot more knowledge using that. Before I learned dplyr through DataCamp, I really couldn't use R for my daily research purposes. As a beginner, things would just take too long, and I couldn't justify a few hours doing something when I could maybe do it in 30 minutes in STATA.
One of my professors gave me the task of cleaning up some names. We needed some way to predict their gender, because we didn't have a gender variable. So I uploaded the names, and I had taken the text manipulation course by Charlotte Wickham, and it made cleaning the dataset ten times easier. And I was able to manipulate it with dplyr and then I found an R package within ten minutes of searching: it's called gender, and it does exactly what I was looking for. It takes a vector of first names and returns a confidence score and a gender based off that. My professor gave me a week to do it, and within a few hours, I had it done, and he was amazed. It wasn't even that it was that sophisticated, but I knew where to go and I knew what to do and it was all thanks to R and DataCamp.
How does DataCamp compare to other online learning platforms?
Apart from the books which I think are really hit or miss (except Hadley's book, which I really like—I still go back to that sometimes). I tried Codecademy, for a few weeks, but it didn't stick. I think it was that I didn't like the structure of the courses. It just didn't click. But DataCamp did.
I've learned STATA in a classroom settings. Which was fine, but there were two TAs teaching the course who were kind of learning themselves. They knew how to do a bit more than everyone else, but they also had 30 students they needed to attend to in an hour. A lot of people didn't understand how to set up their directory, so that was the majority of the course. With DataCamp, you don't even have that problem, you don't have to set directories, which is a non-trivial thing when you are learning programming. So that's awesome, and especially that you have some of the biggest names in data science.
Again, going back to this machine learning course I took in grad school. I was so interested in machine learning and so let down by the class, because the professor was a really nice guy, but just wasn't that great at teaching. We used the software that was a drop-down based machine learning software. I don't know why anyone would use it, and they have since switched over to R. I left the course and my intuition was really bad. I couldn't explain what was different about machine learning as compared to classical statistics. And literally in the first video of Machine Learning Toolbox, five of my questions were answered. Fundamental things that I didn't understand. So what I did, and what I do often, is I will start with the videos and I take notes off of those. When I started at HBS, one of my friends was a math major, very bright dude, and I talked to him a little bit about machine learning and he knew way more than me. I ran into him a few months ago and he was like, "Wow, you definitely know your stuff now." And that's thanks to DataCamp.
Once I find more time, the cool thing is that I will be able to recreate the experience completely with Python. I know very little about Python, but I am finding more and more reasons to learn it, so it will be double the experience. And then you guys have a course or two on SQL. So the fact that it is all in this format that I already know, it benefits me and I feel like it benefits most people. It is much less of an investment, much less of a risk, because everything I've done is going to transfer over, not only in terms learning to code, but also in learning from the DataCamp format, so that's exciting as well.