The Data Science Field and Statistics
Almost all consumers have some familiarity with the field of data science even if they are not aware that they do. Anyone who has ever been delivered recommendations by a company like Amazon or Netflix has had their data analyzed in order to produce those recommendations. Data science involves gathering information and analyzing it to reach a conclusion about consumer behavior. The multidisciplinary nature of the field, which encompasses mathematics, computing, information science, statistics and more, is one of the main differences in more traditional statistics careers and one in data science. In addition to statisticians, people who work in data science may be engineers, business analysts, data analysts, data scientists and more.
What Statisticians Do
According to the U.S. Bureau of Labor Statistics, statisticians gather and interpret numerical information to reach conclusions that are applied in many different fields, including agriculture, economics, and business. While statisticians have been around for centuries, the field of data science is a new one. A statistician in data science will have access to huge amounts of complex data compared to the volume of information statisticians traditionally use. While some predict that the occupations of data scientist and statistician will eventually merge and some may use the terms somewhat interchangeably, in general, the work of a statistician still differs in some ways from that of a data scientist. Statisticians tend to deal more with data that is specifically gathered for the purpose of a study they have created. They also may deal with data in a more nuanced way than data scientists. The two fields are often complementary. Data scientists may be more capable of making sense of vast quantities of data but statisticians are still needed to apply their methods in many cases.
Becoming a Statistician in Data Science
Statisticians bring a mathematical approach to data analysis while the field of data science is focused on scientific computing to reach its conclusions. Therefore, in order to work as a statistician in data science, a person should have a strong background in both traditional statistical techniques and in computers. Statisticians who want to work in data science should take an interdisciplinary approach to their education. Along with an understanding of the methodology of statistics, their skills should also include machine learning, data mining and distributed computing along with programming languages. Statisticians should look for internships in the field of data science. According to the BLS, for most positions, statisticians need a graduate degree.
Companies have become very good at obtaining and storing data. However, making useful real-world analyses of that data can still present a problem, and this is where statisticians come in. A statistician in data science may need to acquire some additional skills in dealing with large quantities of data, but this fast-growing field needs their practical skills in applied statistics.
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