June 12, 2019

Transitioning from Academia to the Data Science Bootcamp Career Track


Transitioning from university to industry employment can be difficult for a number of reasons. First, learning a new set of technical skills, as well as acclimating to a business environment, presents a steep learning curve. Second, there is a significant mindset shift that must occur in a relatively short amount of time. It can be daunting to someone, even if they have an advanced degree. One of the models that individuals making this transition have found an enormous amount of success with is attending a data science bootcamp. Bootcamp programs offer participants a structured and proven method of gaining the hands-on experience employers are looking for, and ultimately landing an industry job as a data scientist.

At The Data Incubator, we run a free 8 week Data Science Fellowship bootcamp in New York City, Washington DC, San Francisco, and Boston to train PhD and Master's graduates and get them hired as professional data scientists. We've put together a few tips on how to navigate the transition from academia to a data science bootcamp career track that should both answer these questions and, hopefully, give readers a better idea of the benefits of a data science bootcamp.

Practice the fundamentals

It's critically important that students practice the fundamentals of data science before they ever even apply for a bootcamp program. Bootcamp programs are designed for students who are already experienced with basic concepts in computer science, probability theory, and mathematics. When you apply to a data science bootcamp program you'll be competing against a lot of highly talented people with strong quantitative backgrounds, and having a good grounding in quantitative problem-solving will be important for not only getting accepted to a bootcamp program, but also for achieving career success through one. Practicing fundamental computer science problems beyond one's degree field is critically important. In job interviews, these kinds of questions are ubiquitous, so whiteboarding code for basic algorithms (sorting, search), data structures, and statistics is always going to be helpful for future job searching in data scientist roles. Learn Python, pandas, SQL, and some basic regression models to get started. Make sure to download Anaconda, as well, and learn how to use Jupyter Notebook.


When your goal is knowledge, like in academia, it's okay to spend as much time as you want learning a new concept or completing a project. On the other hand, during a bootcamp program and on the job, you will often find that there is not enough time to deal with all the tasks and assignments required of you. In a situation where you have more on your plate than you can handle, it is essential to develop the ability to decide which tasks require execution, which can be postponed, and which ones can be simply ignored.

Look outside your degree field

In academia, students can get used to well-defined programs with a diploma reliably granted after all of the steps have been accomplished. However, the most exciting opportunities in industry are not always well defined, and companies may not even know they are looking for you. Finding the right fit depends on the realization of what kinds of fields and roles you are interested in, and what your specific background prepares you to contribute. It's not too early to start that process while you are applying and before you start a bootcamp program.

Shift your mindset

For students in academia, it's easy to settle into the mindset of "which problems sound most interesting?" Whereas in industry, the much more important question is often "what will be most useful?" Learn to take a more goal-oriented approach than you're used to. Think of your work as trying to optimize for a total cost required to reach a goal. In academia, you might have had internal deadlines with your workgroup; however, there were no real consequences for not meeting a deadline. Missing a deadline would simply mean that a paper's submission date would be postponed. Another way of looking at this is to say that you were optimizing for absolute performance score, regardless of the time required to achieve the result. Industry is very different. Time always has a cost, be it the individual's salary or the cost of the resources allocated to that project, and it must be considered. Besides the implications on prioritizing your work, this also has implications on what you should strive for in order to be successful. It's important to shift mindset from "deliver perfect results" to "deliver results fast."

Transitioning from academia to industry is always challenging. Thankfully, the bootcamp model that has become popular in recent years offers a proven method of overcoming these challenges. By providing students with hands-on training, and connecting them with hiring companies while they're undergoing this training - providing the hiring companies with a living portfolio of students' work - The Data Incubator's Fellowship program has helped hundreds of students achieve career success through this transition. You can learn more about The Data Incubator's Fellows' success on our blog along with more helpful information on preparing for the Fellowship and solving technical data science problems.

Want to learn more about The Data Incubator? Read what alumni have to say on SwitchUp.

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