Aaron Curtis is a professor at the Brigham Young University—Hawaii. Recently, he taught a class on big data analytics. He tried using a textbook with the class, but realized it wasn't reaching his students. So to better engage with them, he created a DataCamp group for his students. With DataCamp, he found an educational resource that has helped his students grasp data science. We recently caught up with him to discuss how DataCamp for Groups can help students, how different students can learn at their own paces and how it can pay dividends for teachers and professors.
What subject do you teach?
I teach an IT elective course on big data analytics. The objective of the course is to provide students with a good understanding of the tools and technology that are used in analytics. Most students are computing majors or IT majors. But some students are also from the mathematics and psychology departments.
What made you decide to try out DataCamp?
Last semester, I was teaching a new course designed to introduce students to "Big Data Analytics." I had a textbook I was using, but I discovered mid-semester that it just wasn't resonating with my students. I had a mix of students in the class: some were proficient in programming, but not in statistics, while some were proficient in statistics, but not in programming, and some needed to get up-to-speed in both areas. A handful were comfortable with both programming and statistics and were ready to explore topics related to machine learning and more advanced analysis. The book I had selected was not working as well as I'd hoped in addressing the needs of all these groups. Plus, we were spending a lot of time trying to update and troubleshoot the lab configurations as outlined in the textbook. Eighty percent of my students have one or two semesters of programming experience, so I felt that with my course, I needed to reinforce general programming concepts and how they apply to R. That led to the class signing up for DataCamp's R courses.
Your class is using DataCamp to train its students. Why did you choose DataCamp as the primary learning resource?
DataCamp gets students more comfortable with statistics and programming. Also, DataCamp helps students feel like they accomplished something. The fact that DataCamp provides hints and real-time feedback is really great—especially since the feedback is linked to the particular mistake a student made (even within a single exercises). This instant and automated feedback is so useful to me as an instructor. DataCamp's interface allows users to access slides and videos at any time. Students tend to go straight to exercises, so it's a huge plus that slides are available in an interactive interface. That lets students have instant access to hints, and they can refer back to relevant slides and video in real time. If anything, I regret I didn't start with DataCamp earlier in the semester. A great side effect is I actually got more comfortable with R myself! It's helped me polish my own R skills and to become more proficient in the material that I'm teaching.
I plan on using DataCamp more thoroughly in upcoming classes.
How have your students felt about the learning experience with DataCamp?
They've liked that DataCamp courses are much more tailored to their needs. For example, if we worked with a book or a live demonstration, you have two options as an instructor when people can't follow along: Stop everyone and slow down the students who get it, or move on and then some students keep getting stuck. With DataCamp, I can help students that are stuck but others can keep moving on and making progress. Plus, it's provided my students with a more engaging learning experience.
Why would you recommend DataCamp in the classroom as an instructor?
It is much easier to handle larger groups of students and give everyone the attention they need. Assignments in DataCamp are also more modular, so they're easier to follow. Plus, the code examples are much more readable and digestible. In a textbook, often larger projects are covered, so students can have the tendency to just copy the textbook code. That's not possible in DataCamp's courses.
With DataCamp, the problems are challenging enough to be engaging, and most of the time the prompts are enough to understand how to work through the problem without jumping to the solution. The hosted environment was very helpful as well. Knowing that they could run their R code in the browser and that all the needed libraries were taken care of helped students focus on the coding rather than on managing their local machines. I plan on using DataCamp more thoroughly in upcoming classes.
What's been the most popular course with your students?
They've spent a lot of time on Intermediate R. They really liked the production quality. And the free Introduction to R course was great, given the audience. It reinforced general programming concepts and how these apply to R.