Last year I wrote an article on this topic and I wanted to update it for the new year – 2020. The innate skills required for data science listed and explained in last year’s piece remain in force, as do all of the job classes. But this time around I wanted to provide a slightly different perspective by pointing out top job categories, coupled with specific skills that could serve to lead to a career in data science. The job categories come from observations in my data science consulting practice, my work as a tech journalist, as well as from interactions with my data science students at UCLA.
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I’ve encountered many so-called “data analysts” across a wide spectrum of professions who would make excellent data scientists with the proper training. The tool-of-choice for data analysts is typically Excel, and many of these data professionals get an amazing amount of productivity from a common spreadsheet program. They import and analyze many of the same data sets used by data scientists, but are limited by Excel formulas and “Data” menu features. Some data analysts have taken the plunge into the VBA programming language that underlies Microsoft Office suite products, so they’d be primed to take the next step by learning R or Python for data science. I’ve met some very solid data analysts working in finance, sales, and marketing departments. With some well-structured education in data science and machine learning, I think these folks would be able to excel in the field.
Business Intelligence Analysts
I would classify BI analysts as a step up from the data analysts I describe above. These data workers engage a more powerful class of tools like Tableau, Looker, Alteryx, Microsoft Power BI, SAP Analytics, MicroStrategy, IBM Planning Analytics, Oracle Essbase, Oracle BI Suite, and many others. These tools are able to crunch larger data sets, and have much more powerful data visualization and wrangling capabilities. They’re members of the so-called “low-code, no code” movement. BI analysts typically have gone through special training by the tool vendors and are poised for the next step – data science. Some of these workers are in large enterprise finance departments, often in the financial planning and analysis (FP&A) area.
Data workers in this finance area also include users of budgeting and forecasting software like Oracle NetSuite. The common thread for these future data scientists is that they’re used to working with data, analytics, and data visualization. Many BI analysts could make the transition to data science with additional training.
Enterprise Resource Planning Users
ERP software is the mainstay of the large enterprise, and ERP users tend to be a group capable of becoming data scientists. Users of tools like Microsoft Dynamics, SAP ERP, Workday, as well as many others in this software class, are highly analytical and work with data on a daily basis. Many of these data workers have not used a programming language however, so there would be some catching up to do, but I see some good innate aptitude to pick up data science principles quickly given the proper training.
ETL (Extract, Transform, and Load) engineers are the workhorses of IT departments, and often work with data scientists to provide extracts from enterprise SQL databases, data warehouses, data marts, and data lakes. Although ETL engineers might see an easier transition to the role of data engineer, I’ve met some strong contenders for going all the way to data scientist. The good thing about ETL engineers is they possess strong data skills by default; they’re working with data and SQL daily. They’re used to big data and associated applications. I’ve known some ETL engineers who take MOOC classes in their spare time to tool-up for data science. I think moving to data science from an ETL engineering role, is a natural progression, and upwardly mobile path in many organizations with the right aptitude and education.
Recently I’ve been coming across academics who call themselves “computational scientists.” For instance, I was interviewing a recent Ph.D. in Political Science who called himself a “computational social scientist.” I felt this creative morphing of a professional title was a very good move in this climate of hyper-demand for data scientists. Although this candidate wasn’t an actual data scientist, the self-assigned title gave the impression that his perspectives were very data-centric. I’m confident that such a person used the essence of data science methodologies in his academic career, and research work. These skills would translate nicely for a role in data science. I’ve also seen resumes with “computational mathematician,” and “computational statistician,” all pointing to heavy data experience and ways of data science. I think it would be quite easy for individuals in this category to assume the role of data scientist without much additional training.
Recently, I interviewed a researcher in cosmology for a senior data science position. The gentleman was a renowned researcher in this field that studies the large-scale structure of the universe, with many published peer-reviewed papers to his name. Academics in the physical sciences use data constantly; and in this case, astronomers no longer sit in front of a telescope’s eyepiece. These days, telescopes just collect data for detailed analysis, sometimes using machine learning methods.
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This article touches on a handful of job categories that I’ve had experience with along with their ability for reaching across the aisle to the field of data science. The common link is the role data plays in these positions, all strongly data-centric in their perspective, and propensity. I’ve personally seen success for individuals in each category adopting the data science process and becoming quite successful with it. I’m sure there are many other categories! If you have personal experience with other categories, please leave a note here for other readers to see.
Ready to become a data scientist? Attend the ODSC East 2020 Career Expo this April 13-14 in Boston and see what companies are hiring!