Gone are the days when a four-year college education guaranteed a lifelong career. Changes in the economic environment and technology disruptions are driving professionals to upskill themselves and make career transitions to meet the needs of a new economy. Pearson, a learning company, recently released the results of its Global Learner Survey. The survey concludes that learners around the world are increasingly adding to their formal education through a DIY (do-it-yourself) mindset. It blends self-teaching, short courses, and online learning to keep pace with the talent demand.
I frequently meet technology professionals at webinars, conferences, or during interviews with our organisation. They often ask me questions on making a career transition to data science or about pursuing courses in data science to get a high-paying job. I advise them to ponder over the following questions, which will guide them on the course of action they should take.
Is Data Science Your Calling?
It is an important question. If you do not have the liking and aptitude towards this domain, you are less likely to succeed in this journey. I have also seen bias in terms of people with strong mathematical backgrounds tending to succeed while making a shift in career to data science. Invest time in learning topics such as Normal Distribution, Sampling Theory or Time Series Forecasting to check if you like numbers. A high paycheck should not be the only reason for you to attempt this career transition.
Which Job Role Matches Your Skills?
To answer this question, I would like to break the myth that the data science job is limited to data scientists. While the data scientist role is at the top-end of the pyramid, it requires highly specialised skills and years of education and experience. It is imperative to map your current skillsets with the broad categories of jobs available.
The second category of jobs is data analysis. Technical support professionals could explore it as they have the primary skills to understand and solve business or technical problems.
The third job family is data management. If you are a software developer or quality assurance engineer, then you can learn an analytical programming language to begin the transition.
The next category of jobs is of a data scientist, which is the most aspirational choice. It requires specialised training and education and an incremental approach to career progression in the field. If you are a software developer, then begin with transitioning to a data management job like a data engineer. From this point, you will have a higher chance of moving closer to becoming a data scientist.
How To Begin The Transition?
I have seen a clear bias towards experience compared to education. You can take several data science courses, but a hands-on job of the data analyst/data engineer will go a long way in enhancing your chances to make this transition. This education is also expensive, and hence I want to caution freshers to refrain from pursuing courses without any application or certification. The only exception that I want to make is certifications in analytical programming languages, as this skill requires hands-on work to get certified.
How Can One Manage The Journey?
The best way to manage this journey is to start it in the first place. Don’t waste precious time thinking about the transition. Take action. You could begin with walk-in interviews or attempt to clear the basic analytical programming language certification to make a start. It is difficult to imagine an industry where data science is not used today. It is pertinent for you to understand the use of data science in your current job role. Assess the skill gaps to get a basic understanding of the domain.
You could also make a move by taking up additional responsibilities and contributing to projects in data science in your current role.
Importantly, remember that great things are never built in a day. Demonstrate the right persistence to learn, work and contribute to this discipline.