A Style All Your Own
“The goal of career development is to move from jobs that anyone can do to jobs that only you can do.”
I found this piece of wisdom on my Twitter timeline from startup advisor and diversity inclusion advocate Jennifer Kim. Elaborating on this, my question is: how long must one “do their time” before moving to the next step? In the recent past, I would have answered my own question with a firm, “none at all.” But now, I’m prepared to stand corrected.
I never really understood the value of quietly working up the totem pole in college. While my peers were going through rounds and rounds with different consulting firms, I focused whole-heartedly on nurturing my first business, even at the expense of my grades. At the time, I saw myself as “better” for daring to be unique and thrive outside the system. But throughout my MBA, I took the opposite approach. I put my natural leanings towards entrepreneurship temporarily on the back burner for those two years to focus solely on the course material. As a result, I emerged with a stronger foundation that I could then apply to my second business, DiKayo Data. My perspective has changed further from observing my colleagues in data science utilizing their training and company support to build unparalleled models that transform the field. I wonder if working up the ladder deserves more credit than I’ve given it.
The debate over styles of learning extends far past industry versus entrepreneurship. The budding data scientists I consult on the best ways to break into the field often ask me whether it’s better to learn a bunch of programming languages or zero in on one. Should we specialize or is it better to be a jack of all trades? My answer is always the same: there are roles fitted to either style of mastery and whichever fits your personality best is the one you should adopt.
Do you find yourself drawn to a variety of programming languages? Maybe you’ve attempted some projects that utilize the basics of R and Python integrated with SQL along with others built with front-end languages like JavaScript and HTML. Having such a diverse smorgasbord of a portfolio is very attractive to companies hiring data analysts. The tasks required for this role are grounded in a general knowledge of databasing and surface level data processing.
What if you want to specialize? There’s obviously more than one way to do this. Do you like data visualization the most? Doing a deep dive into Tableau will position your perfectly for business analyst roles. Though databasing is just as necessary a skill for a business analyst as it is for data analysts, back-end programming knowledge definitely takes a backseat. Conveying technical problems to management using clear and precise visualization is front and center. On the other end of the specialization spectrum are machine learning engineers. Some machine learning engineers specialize in Python and others in R. But given the depth of their models, developing a detailed flow primarily in one language makes for a streamlined innovation process.
The roles I mentioned represent only a small fraction of the opportunities out there and I also didn’t delve into the full scope of details available to you. This lowdown from Hacker Earth is a good read to kick off your exploration or enhance what you already know. However, even though the field offers a larger degree of diversity in roles than you might think, some truths span across all data science opportunities. 62 percent of data science job postings on Indeed as of January 25th, 2020 mentioned Python as a desirable skill. 40 percent mentioned SQL. R followed closely at 39 percent, but after that, no other tool held a high percentage, meaning that such tools’ significance is more opportunity specific.
As for me myself, I have comfortably claimed my place at the intersection of data science and communications, merging my long history with journalism and my more recent passion for analytics. Because data wrangling is not my sole focus, I may never be as well-versed in R as my colleagues are or even as much as I want to be. Nevertheless, I can commit to getting a little more informed and versatile each day - enough to understand the full scope of how all individual data science innovations come together to create the industry at large.
Want more insight into how these trends can inform your individual career?
I’m offering discounted #DataHired sessions ($20 dollars off with the code ASKANYTHING) to help you pivot into the field at this time.