5 Pieces of Advice from DataCamp SubscribersSeptember 7th, 2017 in User Stories
Not long ago, these learners were just starting out in the world of data science. Today, they are CTOs, data scientists at research groups, financial analysts, and more! Here’s the advice they wish they received.
1. Don’t get overwhelmed
As someone coming from a background very different from data science, I'd say that it's important to focus. Don't get overwhelmed by all of the technologies and terminology—it's easy to hear things like "Pandas", "Scikit-learn", "dplyr" and "boosting" and be paralyzed by what seems like a mountain of things you don't know. Start in a place that is specifically made for beginners, and gradually build from there. Don't feel like you have to know it all immediately. That's what I love about DataCamp—it gives beginners a great place to start, and provides a clear, effective path forward.
Don’t be afraid to spend some time on it. I usually don't teach R to students, but rather to my colleagues, and in a way they are the most difficult of course attendees because they want to be efficient right afterwards. But however you do it, it takes some time to really be able to do data science in an efficient way. My first advice would be not to be too impatient because the time you invest in learning and practicing your skills is never lost!
2. Make a Plan
One of the main things is to have a sort of structure for how you're going to do it... So like I said, the Tracks thing really helps with that because you've then got a learning plan. But also blocking out time in a week to do it and setting out how much time. If you're going to sit down for an hour, make sure you actually do, and make sure you do that a few times a week. The DataCamp website doesn't talk about that, it doesn't really cover that side of things. But obviously setting a schedule is really important part of keeping the momentum up when you're learning something. It is doable to learn this stuff!
My ideal learning plan would involve, say 3-4 long sessions in a week, where I'd spend 2-3 hours split between DataCamp and reading statistics textbooks. However in reality, it's pretty hard to find that time, so I tend to do a minimum of 10 mins of statistics reading or 10 mins of DataCamp each weekday morning so that at least something has been done each day, and 2-3 times per week try and do an extra hour of either one in the evening too. Usually on the weekends, I get in one longer session too.
Check out these 8 helpful steps from the Learn Data Science Infographic!
3. Be Consistent.
My advice, especially for beginners who are using DataCamp to start learning data science skills for the first time, is to set aside time for DataCamp every single day. Just like picking up any new skill in life, learning data science requires intelligence and persistence for the subject, and you can only get the results you want if you spend at least an hour learning every day and progressing further and further on DataCamp with every attempt. Work hard on the exercises, stick to the task at hand and you'll definitely enjoy success.
The intro courses are pretty interesting. If you start the introductory course, you just have to be consistent with it. I think it applies to learning anything well—you have to be consistent with it. If you do a course, then you stop using R, or stop coding for a month or so, then you pretty much forget how to do everything. So the one thing I think people should do is be consistent with it. Do the practice courses, practice what you already know, to make things less hazy in your mind.
I have gone back to courses a few times. If I get stuck doing a project, I can quickly go back and remember how to use a particular function, or figure out what syntax error I am making by using DataCamp courses.
And finally, I think you should learn your basics first. Just looking up answers on other sites and copy and pasting the answers won't be helpful in the long term.
4. Work at your own pace.
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.
5. Practice, Practice, Practice!
Once you have completed a course with DataCamp, you might think it is over, but it’s not. Practice mode is great because you have exercises not only to get the knowledge but to assimilate it, to have it in your brain, to have the mechanics of using the good libraries, the good functions, etc. That is great.
What you are teaching on DataCamp are languages. When I was learning Japanese, I understood that to learn a language, you really have to practice, practice, practice. As my Japanese teacher once told me, to study a language is like sports. And we don’t realize that enough: when you try to get good at golf or basketball, you don’t just study the theory, you have to practice first. It is the same with languages: to understand is not enough, you have to practice and to practice a lot. You can study as much as you want to, but if you don’t try an exercise 1000 times, it won’t really get in your brain. Exercises that repeat and repeat the same stuff help you not just to understand, but to assimilate the knowledge. The Romans have a great saying for that: "Repetitio est mater studiorum"—"knowledge is mother of learning."