Can you tell us a little about your background?
I am a research associate at a university in Germany. I completed my Master's program in quantitative finance, so I have a very strong quantitative background. I started DataCamp to improve my knowledge in Python and I really like the way you are doing it.
I have knowledge in R, which I gained by myself, also C++, Perl, and Bash. We are also using DataCamp for our students—we are teaching a small group of 50 students every semester, and we have done it once now, and a lot of them used DataCamp heavily. Some of them already completed 60,000 XP. In the upcoming semester we are also going to do it again, and are forcing them to get 30,000 XP points with R. Introduction to R, loading data, cleaning data, merging data—you are explaining all these topics really well in your courses, so we are going to use DataCamp again and I hope we will do it many more times because it is working really well for us.
What was your experience with data science before you started your education with DataCamp?
I was forecasting the financial markets for a couple years. Starting from classical time series forecasting I moved to time series forecasting with machine learning. Due the data availability I moved a step further and conducted natural language processing of news and tweets in order to get a forecasting for the upcoming volatility.
What drew you to data science? Why is learning data science important to you?
I was always curious about the information contained in the numbers. Data science accompanied with machine learning gives you the possibility to extract a signal and information, even in a very noisy environment. If you map this piece of information to a process and do the connection you gain an understanding of true relationships which were not visible to you in the first place. Sometimes this feels like solving a puzzle—but a very exciting one.
What do you like most about DataCamp?
It is a mixture of lecture and application, so you have really short videos and you are applying the stuff, and are also trying to extend it a little bit. The combination of lecture and applied tutorial is really nice. I also did courses on Coursera and it is kind of different. You have really long lectures there and it is not easy to make the connection afterwards between the tutorials and lecture. DataCamp has a more efficient learning approach.
You are using Python and R, and the examples you are using, they are really problem oriented, and I like that you don't go too deep into the algorithms, because that would divide focus. For instance, you don't spend a lot of time to explain K Means, or your networks or any other fancy algorithm. You state that there is more to learn, but not right then (in an intro course) so you keep the learner focused.
DataCamp helped me to learn Python, once again from scratch, in a very organized way. Instead of learning snippets and small parts using books and Stack Overflow you get the whole package from one single source.
How does DataCamp compare to other online learning platforms you've tried?
I tried Coursera previously, I actually finished the Big Data Specialization. Coursera is great when you are seeking for the big picture, while DataCamp is great when you want to get your hands dirty on hands-on exercises. It is like Stack Overflow on steroids.
What was the challenge that DataCamp solved for you?
Learning a programming language can sometimes be painful as it takes a couple of learning hours till you are able to produce something. I went through this process a couple of times in my life and I was kind of tired of this. I was able to produce Python code that fit my needs, but I was missing the big picture and was missing hands on experience with libraries.
DataCamp gives you a lot of practice as you code similar snippets over and over again. The guys teaching are real experts and each course is spiced up by their practical experience.
How does DataCamp complement your classes?
We used to spend a lot of time doing the introduction, saying "This is R. Load the data into R. Clean the data. Etc." And now we are simply telling the students to go to DataCamp and we can dive directly into the models. So saving time allows us to focus on models that are more related to finance and to economics in general. So we are able to shift the focus a little bit. But they are Master's classes, and at this university, students lack coding experience. This is sometimes really difficult when you want to work empirically with them. With DataCamp, we can get a lot more students to the point where they can work empirically.
In addition, each student can study at its very own speed and repeat exercises if necessary. When we have been teaching R as a lecture, this was a huge problem, as you always had the fast and slow learners in the same room.
What advice would you have for someone just starting out?
I tell my students that they should try to do some courses, and in order to become proficient, they also need to look for their own projects and apply this knowledge they get from DataCamp on real world examples. Extending your motto, "Learn by Doing."
Get through the introductory courses real quick so that you can dive into the most interesting topics for your which again depend on your field of interests. Programming can be really fun, however learning the syntax can sometimes also be really boring—especially for someone who didn't code anything before. When you see what kind of results you are able to produce just with a couple of lines code you will become more excited about coding. DataCamp gives my students a lift, and helps them get there.
