Become an 'outstanding' Data Scientist!
If you are motivated to enter in to the realm of data science purely as a means to 'future-proofing' your career, or aiming for the highest possible salary, then its highly likely that you are approaching this from the wrong angle. For those newly qualified analysts at a junior level that are seriously considering a career in data science it would be wise to start thinking about what really puts fire in your belly? A good question to begin with is 'what really excites you in particular about working with big data?'.
If you fundamentally have a natural proclivity towards statistics, advanced analytics, machine learning, AI, regression modelling, along with making commercial business cases for insights as something that you could happily work on all day long, then a data science career may well in fact be a natural career path for you. But it is important to ensure that your career aspirations are aligned with your both your aptitude and field of interest, in order to give yourself the best possible chance of landing your first real world data science job.
For those relatively new to the field of 'data science', yet can identify with many of the 360° skill sets required to make a solid all-round data scientist, it is important not to overload yourself at the outset by seeking to master every single component skill necessary to support the role. Try instead to begin by focussing on the one main characteristic of data science that initially piqued your interest and made you decide that this was the field for you. For example, you might start by concentrating on your main passion for artificial life, and then work to develop that interest through reinforcement learning, taking the essential courses and contributing towards solving problems by achieving some form of visibility for yourself through participation on platforms such as Kaggle.
Another plus point of zeroing in on your passion is that if you don’t possess a strong mathematical background, which is essential in data science to perform the calculations necessary to calibrate, measure and control tests, you can redress this along the way by learning on the job, perhaps combining this in tandem with machine learning and statical modelling to keep things less heavy and a little less algebraic. You will soon find that over time the basic elements and principles will become clear, and your proclivity and enthusiasm for specific aspect of data science, such as AI or statistics will help to carry you through.
Furthermore, It can also be very useful to write or talk about your learning and discoveries as you progress, as an opportunity to create and share stories around your findings that can lend towards the development of a road map, separating where you have come from and where you are heading. By sharing your new findings with the wider data science community you create a feedback loop, one that provides a critique of your developmental study.
Learn hard and follow your interests and passions, and sooner than you know it you will gain recognition and validation from both peers and potential employers for displaying all of the necessary traits of an outstanding data scientist.