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The term “data science” has become something of a buzzword in recent years, and data scientists generalists are increasingly sought out for their expertise. Although experts disagree on where the parameters of data science actually lie, it is an interdisciplinary field that combines statistics, machine learning, analytics and more.


What Is Data Science?

Computers have become exponentially more powerful in the past two decades, and businesses and organizations have been able to gather more data than ever before. However, the most comprehensive data does these businesses and organizations no good unless it is possible to analyze that data and turn it into something useful. This is where the field of data science comes in. Netflix viewing choices, Amazon activity, and purchase records and information gathered about customers for loyalty programs are some of the areas where consumers are most likely to encounter data science at work.

Data scientists analyze data, including data on customer behavior and preferences, to help businesses decide what direction they should be taking next. However, not every piece of data that a company gathers is clear or useful information. One of the tasks of a data scientist is to identify and analyze the good data and set it apart from the bad. While some people say data science and statistics are in the same field, the significant difference in data science is the role of technology.

Data Science Generalists

The question as to whether it is better to be a data science specialist or generalist is not without controversy. Many companies allow their data scientists to fall automatically into the role of a specialist, but as an article in the Harvard Business Review points out, this is not an appropriate use of data scientists. Specialization is appropriate when efficiency is the primary goal, but this is not the case for data scientists. In fact, specialization can decrease efficiency because there can be communication breakdowns between specialists. With data science generalists, wait times are shorter. The generalist also has more to draw on in terms of context. A data science specialist may have deep but narrow knowledge of a single industry or aspect of data science, but a generalist is likely to have a better idea of how it all fits together.

Careers for Data Science Generalists

A person who is interested in a career as a data science generalist should pursue a degree in computer science, math, physics or a similar field. While it is possible to become a data scientist with a bachelor’s degree, many people in the field have a graduate degree. For someone interested in becoming a generalist, a good career path might involve working for a small startup since this is likely to require developing general data science skills. The more roles a data scientist has in an organization and the more industries the data scientist works in, the stronger that generalist’s skills will become. This versatility can mean more career options.

Data science is a relatively new field, but some universities offer degrees in the discipline. A person who is intellectually curious and who has an analytical mind may want to learn more about what a data science generalist is and how to pursue a career in this area since it is likely to grow in the years ahead.

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