There are many types of data scientists, with varying skillets and responsibilities. In my opinion, the most important groups you can segment data scientists into are those who write code used in production, and those who do reporting.

Analysts

For a lack of a better term, I called the second group analysts. This does not mean they are not data scientists, people in these roles benefit from knowing machine learning, the ins and outs of data, and general programming skills. However, in this position communication is much more important, and your knowledge of dash-boarding, charts, presentation, and interpretable statistics is more likely to be a key to success.

Engineers

An engineer writes code that gets used in production, meaning it effects business processes by being integrated with an organization’s existing software. Here, your knowledge of machine learning is more important, and you’re more likely to be working on a team that is code-oriented, unlike a business intelligence or business analysis team.

The Target of Data Science Education

There is no collegiate major for data science (at most universities), so pretty much everyone “moves” into data science, but the people that studied computer science or were software engineers usually take the data science engineer position, while people with other backgrounds more typically move into the analyst position. Data science education, as it stands today is much more focused on the position of the analyst than the engineer. In my opinion, this is because it is much easier to teach the skills an analyst needs, and many individuals and organizations want to cash in on the sudden spike of people wanting to change their career.

Since there is so much hype around “The Sexiest Job of the 21st Century,” as some have put it, parties involved around data science education over emphasize the availability and demand for analysts. So, the primary issue I take with data science education is that there seems to be and not a lot of focus on engineering skills. I believe the engineering skills are more useful, in higher demand, and fewer people have them. When I talk with people beginning their career in data science I usually hear about their skills related to analysis, but I almost exclusively read about engineering skills in job posts.

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