My Journey into Health Data Science

My undergraduate and internship experiences made me aware of the knowledge gaps in the health system and motivated me to seek further education. To understand better how inferential statistical methods could generate evidence to influence policy, I returned to UB for a Master’s in Epidemiology and Control of Infectious Diseases. Unfortunately, the raging sociopolitical conflict in this part of the country meant significant disruptions to the course. However, being grounded in core mathematical concepts integral to statistical sciences like Epidemiology, I was able to study independently, supplementing my class work with resources from online courses. A two-month seminar with the International Network for Research and Development (INSEARCH) focused on R for Epidemiology ignited my curiosity around programming in healthcare and its benefits, particularly in a local context where data quality was a major challenge. I stayed on as a volunteer for INSEARCH, assisting other participants to complete the same program.

Having gained good programming experience, I was excited by the research prospects of Data Science for evidence generation through novel statistical and machine learning techniques. This led me to the MSc. Health Data Science program at Swansea University.

Studying and living in a new environment is always challenging, as was applying engineering principles to epidemiological concepts on unfamiliar health conditions, all with the pressures of an academic setting. Thankfully, prior experience and theoretical knowledge enabled me adapt quickly. Modules on Analysis of Linked Health Data nicely complemented my knowledge of epidemiological concepts and gave me further insights into evidence-generation techniques. To improve my writing skills and gain research experience unique to the UK, I worked as a grant writing intern with the National Center for Population Health and Wellbeing. Seeking to augment my analytic skillset further, I applied for the HDRUK Black internship and got onto the HDRUK Inflammation and Immunity Driver Program hosted by Queen’s University Belfast.

This internship improved my understanding of data analysis and inferential statistical techniques. Warmly hosted and mentored by Dr John Busby and Dr Charlene Redmond, I learned to improve my analysis and automate results into more optimal research outputs. It was quite insightful discussing the findings from our work investigating ethnic differences in asthma characteristics across a large multi-ethnic cohort with peers and researchers within the Driver program. In addition to analysis in R, the internship also allowed collaboration with other interns on the Python-based technical challenge, with our group ultimately winning the Amazon-sponsored prize for our model predicting A & E outcomes using machine learning models. Thus far, it has been an exciting journey in research development that I hope to continue. For one who originally envisioned a career in engineering or medicine, I have certainly found the best of both worlds in Health Data Science. Working in the Driver program has been quite beneficial in my pursuit of a research career, and I look forward to applying the techniques and knowledge gained at the next opportunity.