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How to Become a Data Scientist Posted on May 15 - 2018

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All such roads lead to the same destination: a job assembling, analyzing and interpreting large data sets to look for information of interest or value.

Data science encompasses "Big Data," data analytics, business intelligence and more. Data science is becoming a vital discipline in IT because it enables businesses to extract value about the many kinds and large amounts of data they collect in doing whatever it is that they do. For those who do business with customers, it lets them learn more about those customers.

For those who maintain a supply chain, it helps them to understand more and better ways to request, acquire and manage supply components. For those who follow (or try to anticipate) markets – such as financials, commodities, employment and so forth – it helps them construct more accurate and insightful models for such things. The applications for data science are limited only by our ability to conceive of uses to which data may be put – limitless, in other words.

In fact, no matter where you look for data, if large amounts of information are routinely collected and stored, data science can play a role. It can probably find something useful or interesting to say about such collections, if those who examine them can frame and process the right kinds of queries against that data. That’s what explains the increasing and ongoing value of data science for most companies and organizations, since all of them routinely collect and maintain various kinds of data nowadays.

Basic Educational Background

The basic foundation for a long-lived career in IT for anybody getting started is to pursue a bachelor's degree in something computing related. This usually means a degree in computer science, management information systems (MIS), computer engineering, informatics or something similar. Plenty of people transition in from other fields, to be sure, but the more math and science under one's belt when making that transition, the easier that adjustment will be. Given projected shortages of IT workers, especially in high demand subject areas – which not only include data science, but also networking, security, software development, IT architecture and its various specialty areas, virtualization, and more – it's hard to go wrong with this kind of career start.

For data scientists, a strong mathematics background, particularly in statistics and analysis, is strongly recommended, if not outright required. This goes along naturally with an equally strong academic foundation in computing. Those willing to slog through to a master's or Ph.D. before entering the workforce may find data science a particularly appealing and remunerative field of study when that slog comes to its end. If so, they can also jump directly into mid- or expert/senior level career steps, respectively. View More


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