Tell us a little about your background.
I am a proud father of one and I am originally from Los Angeles, CA, US, although I now reside in Bowling Green, KY.
My educational background is originally in mathematics - I earned a BA in Mathematics from Western Kentucky University, along with a Stats minor. Most of my college work was very theoretical, with a few applied stats courses thrown in. I had a little R experience, but not very much, when I landed an internship at the apparel company Fruit of the Loom.
When that internship became a job offer on the newly created Data Science team, I knew I had to learn data science – quickly.
DataCamp does a terrific job of helping to guide my learning and help me practice with relevant/useful packages in R. Many courses are taught by the creator of the package themselves! Who could be a better teacher than the package author?
What was your experience with data science before you started your education with DataCamp?
I really did not have that much experience with data science prior to finding DataCamp. I had taken an online course or two, so I sort of knew what data science was, and I had done some analytics/basic statistical modeling in college, but I really had no clue what tasks "real" data scientists performed in their everyday careers, much less how they accomplished said tasks.
I ended up taking an online course or two before finding DataCamp, and, although they were somewhat helpful, I am not a huge fan of courses with long (20 minute) videos and weekly due dates, as I have a fairly busy schedule and it can be difficult to consistently set aside time to study. DataCamp allowed me to work at my own pace, which is extremely good for me.
What were the greatest challenges for you starting a career in data science?
For me, the most difficult part in beginning my career in data science would be knowing where to begin. I don’t have the strongest computer science background, and so it has taken a lot of work to gain programming proficiency, which DataCamp has helped me tremendously with!
Probably the next most difficult part of data science is that it is such a broad discipline that covers so many different areas. It can very difficult to know where to focus your efforts. I feel like DataCamp does a terrific job of helping to guide my learning and help me practice with relevant/useful packages in R.
Despite these challenges, however, data science is an extremely interesting, fun, and rewarding career path, and DataCamp’s platform helped me tremendously in succeeding in this field.
What drew you to data science? Why is learning data science important to you?
While in school, I noticed that most, if not all, of my classmates expressed a desire to use their mathematics degree to teach others, which I feel is a noble undertaking. But although I loved my job as a math tutor, I really wanted to apply my degree in the corporate world.
As the perennial "top career" in many HR studies, data science is both an extremely rewarding field as well as an extremely competitive field, with more and more top talent deciding to enter the field every day. Data science is also an extremely technical field, with broad usage as well as a steep learning curve.
Clearly, there are many reasons for me (or anyone for that matter) to learn data science!
What do you like about DataCamp?
There are lots of reasons that I enjoy DataCamp’s platform! I really love that I can work at my own (often very hectic) schedule, as opposed to most other online learning platforms, which tend to operate on 4-8 week intervals, which can be difficult for a parent and busy professional.
I also love the fact that I am being taught by a variety of world class experts in their respective fields. There are many courses on DataCamp that, for example, teach the user how to use a specific package/library. In many cases those courses are taught by the creator of that package themselves! Who could be a better teacher than the package author himself?
Can you give us some examples of how you’ve used skills you’ve learned from DataCamp?
I remember very distinctly the first premium course I tried out on DataCamp (Data Visualization in R with ggvis) – this was in the Fall of 2015 I believe. I had recently completed the Introduction to R course and thought the DataCamp platform was fairly neat—it was extremely interactive and completely dedicated to data science. I wasn’t yet a premium member, so I could only do the first chapter of the course (a feature that I really enjoyed/appreciated by the way). The first chapter showed me how to make some basic (yet very beautiful) charts with the ggvis package, and I immediately made a few at work the very next day and wowed my coworkers. From then on, I was hooked!
In another instance, I remember using dplyr immediately after taking Data Manipulation in R with dplyr and experiencing a huge performance increase, both in processing time (depending on the size of the dataset) as well as coding time. The skills I learned were very easy to implement in my own work. As an added bonus, I showed some of my coworkers and they also saw how useful R and dplyr can be.
And those are only two examples of the many instances I’ve learned immediately applicable skills from DataCamp – I always look forward to times when DataCamp releases a new course, because I know will learn new skills to make my job much easier and be more productive!
How does DataCamp compare to other online learning platforms you’ve tried?
I think it is obvious that DataCamp is the best platform for those seeking to learn data science. In my opinion, some clear advantages that DataCamp has over other online learning platforms are, in no particular order:
- Work at your own pace – No due dates, no stress, learn when you feel most ready to.
- Only data science – Not that there is anything wrong with other topics, but DataCamp is totally devoted to data science, and, with the Community articles, is a major hub in the data science world.
- Taught by experts – DataCamp users have the luxury of learning from real luminaries in the data science field.
- Excellent pricing structure – $39/month for unlimited access to a great data science instruction as taught by experts? Most people spend quadruple that amount on coffee!
- Show your skills/badges off on your LinkedIn profile – Show the world what you know! Other online learning platforms make you pay extra fees to share your badges, but DataCamp does not.
- Steadily growing – I have been a member of DataCamp for a couple of years now, and I have witnessed the platform growing and growing, and I am personally very excited about all of the new features/courses being added every month!
What is the greatest benefit of learning with DataCamp?
In my opinion, the greatest benefit would be the fact that much of what I learn (interactively I might add) can be applied at work the very next day, which is a huge benefit that I feel cannot be overstated. I also enjoy being taught by leading experts in the field of data science who are actively creating and maintaining tools and packages that I use every day.
That being said, getting to work at my own pace and during my own (often very limited) free time is another terrific benefit of DataCamp and is one of many reasons why I choose to stay with this platform.
What advice do you have for someone just starting out with DataCamp?
I would first tell a new user to take full advantage of what I call DataCamp’s "work at your own pace" model. When I am taking a course on DataCamp, I try and set a goal of one chapter per night, although I certainly do not stress if I fail to reach that goal, as there are no time limits to complete each course.
If you get stuck on a particular exercise, don’t forget you can view the solution. Sometimes working backwards on a particularly tricky exercise is still a good way to learn. And sometimes, just take a break – it will still be there when you get back.
And if you are new to R (but want to learn it), then I would start with Introduction to R, followed by Intermediate R, then Data Manipulation in R with dplyr, followed by Data Visualization with ggplot2 (Part 1), and then Introduction to Machine Learning. These five courses are all extremely well taught, easy enough to follow, and they teach very useful skills that come up all the time when using R. After completing those five, you will have enough skill and comfort in R (in my opinion anyway) to be able to then hone in on any particular task that you might be interested in, such as more machine learning, time series analysis, or finance.
From the DataCamp team: If you are unsure of where to start, try joining a Track!