Data science has become a popular avenue for people wanting to break into tech or switch things up in their careers. But it’s not always clear what the best path forward is. In this Career Karma post, we’re going to break down the three most common ways of becoming a data scientist and discuss the pros and cons of each.
Going to a Traditional School
With the rise of coding bootcamps, going back to university isn’t as appealing as it once was, but depending on the person and the situation it might still be a good option. There’s no doubt you’re going to learn a lot of things in a 4-year statistics or computer science program that you won’t in a bootcamp. Yet, you have to weigh this against the far greater cost of a traditional degree and the time commitment involved compared to a coding bootcamp.
Perhaps even more problematic, such programs are notoriously unresponsive to what actual jobs in the field require.
The Galvanize Data Science Bootcamp I attended, on the other hand, is constantly updating its curriculum based on feedback from graduates and people in the industry.
Yet, if you want the deepest possible theoretical foundation and don’t mind the downsides, college might be for you.
Studying on Your Own
For most people, self-study is going to be the hardest path into data science. While I’m passionate about self-learning and think it’s one of the most important skills you can have, it can be frustrating to get stuck for weeks on a problem that could be resolved in 10 minutes with the help of a second or third perspective.
That said, there are more freely-available books and courses on the Internet than you could get through in a lifetime. If you’re an accomplished autodidact with a strong technical foundation, there’s no reason you couldn’t develop the necessary skill set and get a job on your own.
Blend the Two by Attending a Coding Bootcamp
Luckily, there’s now a third way: a coding bootcamp. Bootcamps have the advantages of structure and working in small groups, as well as self-directed projects and opportunities for exploring your interests.
These advantages are worth spending some time discussing. While setting off alone into the data science wilderness does have its charms, there’s such a bewildering variety of potential starting points and viable paths that one can be forgiven for being paralyzed by the enormity of it all. In my experience, having a clear-cut progression of study is the best way to approach a field as big as data science.
It’s also hard to overstate how valuable the mix of group projects and self-directed projects you find in a bootcamp can be. There’s value in being both the strongest and weakest member of a group, each of which will surely happen at least once in your bootcamp. There’s also value in seeing a variety of approaches to problems.
And, of course, you can use whatever personal projects you have to deepen your knowledge of the data science subfields that most interest you.
Before I attended a data science bootcamp, I seriously weighed the commitments required for all the available paths and ultimately decided a bootcamp was worth it. All the people I know who’ve made this transition have done the same.
There’s more than one pathway into a career in data science. Drop us a comment to tell us which one’s you’re considering.