Exploring the Goals and Evolution of a Data Scientist's Journey

As a data scientist, understanding the data, preparing data for analysis, and driving business impact are all crucial goals. These objectives shape the landscape of an individual's career and contribute to the overall success of a data-driven organization. In this article, we will delve into the evolution and goals of a data scientist, examining the path from a laboratory biologist to a data science professional, and explore how the term ldquo;data scientistrdquo; came to prominence.

The Path from Biology to Data Science

My journey as a data scientist began in a completely different realm. I started my career as a laboratory biologist, where I quickly realized the limited advancement opportunities. Upon observing the burgeoning field due to the advent of new sequencing technology, I pursued a PhD in Computational Biology. This was a pivotal moment in my career, where I learned to write programs, worked with Linux systems, and heavily utilized statistics.

ldquo;There was no such term as Data Science, let alone Data Scientist back then,rdquo; I recall. My focus was on the computational skills that could offer more opportunities in the future. The outside world was witnessing the rise of Big Data and Artificial Intelligence, but I was largely unaware of these trends since I was rooted deeply in my biology domain.

From Academia to Data Science

After completing my PhD, I took a postdoctoral position, a ldquo;break-it-or-make-itrdquo; job in academia. Here, I refined my skills and became proficient in working with AWS and achieving a 3X Associate certification. It was during this time that I started reading about the rapid developments in the field of Data Science.

ldquo;The hype around Data Science was everywhere,rdquo; I noted. College programs were integrating it into their curriculums, and industry and government professionals were discussing ldquo;Disruptionrdquo; and ldquo;Data is THE NEW OILrdquo; with gusto. However, I felt their understanding was limited, with many lacking even basic programming skills.

Data Science: An Overhyped Term?

From my perspective, Data Science was an overhyped term that brought together the fields of Statistics, Mathematics, and Computer Science. Ironically, what I had been doing for years was already a form of Data Science, just unnamed at the time.

Evolving Goals and Professional Shifts

Recognizing the value of the skills I had developed, I began to strategize about how to fit my background into the new paradigm. I tailored my resume to align with the qualifications recruiters were seeking and made the jump to a lucrative position in a bank with a noticeable increase in compensation.

I predict that in the future, the field of Data Science will continue to evolve, but no one can predict exactly how it will change. The people who will benefit most will be the ones who adapt quickly to these new opportunities.

Conclusion

Ultimately, the goals of a data scientist involve understanding data, preparing data for analysis, validating models, and driving business impact. Whether you start your journey as a biologist, as I did, or from another domain, the path to becoming a successful data scientist involves continuous learning, adaptation, and a keen understanding of the underlying data.