James, an economics graduate, has always been interested in numbers, logic, and critical thinking. He loved “whodunit” puzzles as a kid. Algebra fascinated him the first time he solved for X. He built some great visualizations in Excel. But, still, he felt he wasn’t challenging oneself enough in the work he was doing, and really was lacking the feeling of building things. James earlier felt that making a career transition to data science was too far out of reach, but he started to reconsider. He decided to sign up for a comprehensive data science MOOC online, and though he did not know anything about data science, he loved every minute of it. The spark to become a data scientist was ignited but he was a little skeptical about how he would go about entering the data science industry after completing the course.

Are you also looking to transition to a data science career just like James? You are not alone. A growing number of millennials are jumping into data science. But that doesn’t mean a career transition is not a difficult process. Especially, if you are trying to move to an industry like data science where you don’t have much or any experience. A career transition is hard especially while holding a 9 to 5 job. A career transition into data science requires a great amount of motivation, discipline, inspiration, and most importantly, time. As the old-age saying goes –“Rome wasn’t built in a day”; a career transition into data science doesn’t happen overnight.

A couple of weeks ago, Springboard hosted its flagship conference, LEAP. We had talks on “data science careers and industry expectations” from top data scientists in India. We got the opportunity to speak to Farhat Habib, Director of Data Science, InMobi who suggested a few pointers for a career transition to data science –

Despite popular myths, one can become a data scientist without a Ph.D. Anyone can become a data scientist-even if he/she didn’t study programming in college. Luckily, data science is a vast field with a lot of scope for most professionals to fit in. You can find people from different walks of life in the data science industry- economics, computer science, finance, physics. There is no one-size-fits-all solution and the transition path into data science varies from person to person based on the background. Regardless of the background, one thing that is common for anyone making a career transition to data science is having an inquisitive open approach to learning data science.

Transition Pathway for a New Learner to Enter the Data Science Industry

Whether you are re-entering the workforce or stuck in a career you dislike, transitioning into a data science career is within your grasp and here’s how –

Set a Crystal Clear End Goal and Commit to It

Transitioning into data science is not easy. But when you have an end goal in mind, it definitely helps you overcome the bumps on the road. A crystal-clear goal would mean something like –

  • I want to work at an exciting data science or AI start-up, as a data scientist, in the industry I love.
  • I want to become a data scientist at a well-established tech company.
  • I want to build great machine learning models and earn a handsome pay for it.

No matter the specifics of your dream, you must be committed to the end goal of becoming a data scientist.

For some more tactical advice on commitment, plan your overall schedule and come up with the number of hours you want to commit to every week. Even if you are working full-time, set a goal to devote at least 15 hours to learning data science every week. This can include anything – taking a data science course, reading data science books, working on a challenging data science project, or working through some interesting data science tutorials.

Assess your Strengths and Weakness

Take enough time to assess yourself after completing the data science course. Knowing your strengths and weaknesses and, of course, areas of interests will help you with the career transition into data science. When we say strengths, do not consider thinly veiled strengths like “You are hard-working” or something like “My intelligence distracts my peers”. Describe an actual strength and weakness like – Machine Learning, Deep Learning, Exploratory Data Analysis, or Inferential Statistics. Everyone has weaknesses and there are only a handful of data scientists who are proficient with all the skills.

Just like knowing your strengths, knowing your weaknesses is also a key part of your transition. Identify an actual weakness and plan on what steps you can take to try and get better. You might have a biased opinion of yourself when assessing your strengths and weaknesses. In this aspect, getting inputs from a mentor can help you honestly assess your key strengths and weaknesses. A mentor can help you identify key areas you can work on, making you stronger.

Say, for instance, you think deep learning is one of your major weaknesses because it’s hard for you to figure out which libraries or methods will win out in the end. Follow the below approach to work on your weakness –

·       Read hands-on style books and deep learning tutorials which focus on implementations.

·       Practice multiple deep learning techniques and methods on diverse realistic data science projects.

·       Share and explain your deep learning projects through writing.

Find a Mentor

Career transition is a reality for everyone and your next transition is coming soon. We don’t want you to just survive it. Plan your career transition and get advice from mentors about your data science career.  A mentor will always make you accountable to your goals by helping you connect to the right resources for deeper learning. Sometime you might feel that the transition into data science is much harder than you had expected but a mentor will help you overcome these overwhelmed feeling when you’re not sure that you can do it.

Practice, Practice, and More Practice

While learning data science, what matters a whole lot is learning by doing. This means dedicating enough time to perfecting the art of doing data science. If you are a new entrant into the data science industry, set aside some amount of time daily or every week, to learning data science by doing. You cannot become a data scientist just by 7 hours of practice. It is just not possible. You might hear some people saying it takes 10000 hours to become an expert data scientist. Now, you don’t need 10,000 hours of practice to get your first data science job. However, you need to dedicate some serious time to learning.

To give one example, Springboard’s data science career track is about 500-600 hours in total. It takes most of our students an average of 6 months, considering they devote 15 to 20 hours to the program each week. Some take even more time because it depends on how many hours they can devote to the program each week.

Use data science tools and technologies that real data scientists use

While free data science resources are great, you are not in a real-world environment when you use them. As an enterprise data scientist, you will not be working like this. So, what you want to do from the moment you begin learning data science is to build models just like real data scientists too. You want to use the tools and work in a similar environment as they do. For aspirants who are yet to choose a data science course, opt-in for a MOOC that is built around tools and processes real data scientists use. Familiarize yourself with Git and GitHub, work on group data science projects, get hands-on with various data visualization tools, and more.  This will help you adjust must faster once you’re in an actual data scientist job role instead of having to go through another steep learning curve to master and adapt to various data science tools and technologies.

What your next steps should be?

“To know and not to do is not yet to know.” – Buddhist proverb.

So if you are at the career change crossroads now, scared on how to transition to data science – enroll for a project-based data science course. A career transition might not be easy, but trust me it will become less scary the moment you take the first step.  Do not put yourself in an isolated box or discourage yourself just because your educational background is not the perfect fit for the data science mold. Stay the course. Finish the race and emerge as a clear winner. You will definitely be one step ahead of finding and landing your dream data science job by traversing the above path after completing a comprehensive data science course.