At end of my graduate school at the University of Liverpool, with thoughts of impending graduation, I started thinking for perhaps the first time in my life about who I wanted to be while I was having almost more than five years of extensive experience. I had lived happily as an information hub for more than five years working at different positions from financial Analyst, budget specialist, advanced data analyst. After graduation, I buckled down and got started working as a health systems specialist. working as a health system specialist overwhelmingly was how to apply the different methods and tools of cost-saving mechanisms in the health system framework to achieve the strategic goals. Understanding and analysis of the six building blocks of the health system; (i) service delivery, (ii) health workforce, (iii) health information systems, (iv) access to essential medicines, (v) financing, and (vi) leadership/governance, demands a vast expertise of data management, wrangling, manipulation and advanced analytics. I always used to apply the readily available tools that don’t require much programming like SPSS, STATA, LiST, MBB, Microsoft PowerBI. These tools demand less programming skills as they are embedded within like the anaconda version of glueviz(0.10.4) or the orange3(3.4.5). Some of the tools are industry specific that will help to select high impact low-cost interventions and some are used for simulation and modeling of the projects that need scale-up. After a quick google search on trending careers of the future and cross-referencing the required skills with my own past experiences, I landed naturally on data science. In this post, I will recount the path to my current position as a data scientist, and describe some differences between academic research and industry work – so that if you are considering the same options, you might be better informed about the trade-offs.
What is Data Science?
A famous Venn diagram (google “data science Venn diagram is shown below”) defines data scientists as having skills at the intersection of coding, statistics, and domain expertise.
They are the people who take a business problem, go prospecting for available and attainable data, re-formulate the question in technical terms, design and implement a statistical and machine learning task, and re-interpret the results for the business client to ultimately answer the original question. That makes it sound like to be a data scientist you need to be a statistician and a computer scientist with years of industry-specific experience. That’s not quite true.
The reality is, data science is both vast and new, with specializations and sub-fields quickly developing. Highly sought-after data scientists are people who are broadly familiar with all aspects of data science while being experts in one or two fields. It is a highly achievable career for health systems graduate students – with some preparation.
How did I become a Data Scientist?
There are plenty of resources online that outline possible paths to becoming a data scientist, however, I chose to go to a specific class based immersive than boot camps. I will simply describe my own experience. From the moment I realized I would enjoy being a Data Scientist I spent three months in an intensive full-time data science immersive course given by the General Assembly. During the three months of immersive course, I learned intensely. My technical knowledge deficit was overwhelming at times. But here, my academic training was an asset. Living with overwhelming stress without it paralyzing you is arguably what “perseverance” is all about. Like any profession, there are immense jargons and topics, popular and unpopular opinions, the latest and meanest blog posts, all exchanged electronically in an open environment. But I found local and Global instructors who helped me feel at home during the first few weeks of the immersive course.
This immersive class based course was just the beginning of my journey to becoming a data scientist. What I have learned so far was that the qualifications and projects of a data scientist are quite different from those of an academic, and yet the actual work is quite similar in nature. The great majority of a data scientist’s time is spent defining and re-defining an ambiguous problem until it can be clearly stated and then solved.
Once a data scientist finds interesting results, it is crucial to communicate them to the end customer or user. Building a story around a complex issue, supporting that story with evidence derived from data, and interpreting the results into a concrete recommendation for the customer or user, are the central tasks of a data scientist. From this perspective, your graduate training in health economics, statistics or operations research will provide a strong foundation for moving into data science.
Good luck with your career transition!