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Data science is a broad term used to describe the collection and analysis of information, and it is used in a variety of fields, from computer science and modeling to healthcare informatics and applications. It is, quite simply, everywhere. But understanding what the term means, how it might be applied to various disciplines or research questions, and who can use it is vital to crafting courses of study for students who are interested in pursuing a focused career in which it takes center stage.

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Data Science at a Glance

The term itself is casually bandied about in many discussion forums, articles, and focused conversation pieces. Most frequently, it makes an appearance in contexts closely related to computer science and software engineering, search engine optimization, and applications in which data mining is utilized. But it’s both more complex and more applicable than many are led to believe.

Data science is loosely described as the collection, analysis, and application of information from multiple sources. As mentioned, it’s an interdisciplinary realm that utilizes algorithms and scientific processes to parse data—the plural form of a datum or a single packet of information. While it’s similar to data mining, the active method of deriving that information, it is far less simplistic. Some of the questions that data science uses as a starting point prior to analysis are: What does one do with the data once it has been mined? Who benefits from these data? How might various industries or fields of study best utilize the information, and in what form?

Beauty in the Overlap

According to Data Science Central, the fact that data science is now used interchangeably with data mining muddies the water a bit. While the two concepts are related, they are not synonymous. Is it a tool or a field of application and study? The sparse answer is that it’s both, which is why it might appear closely linked with mining or the derivation of data from disparate sources.

The science of parsing, analyzing, modeling, and applying all that information reaches into a host of disciplines and various aspects of online activities. For instance, consider targeted advertising. It isn’t enough to know that a certain number of people have expressed interest in a product, service, or even a cultural phenomenon. Is that interest positive? Where was the mention made in the realm of the Internet? Is the focused advertisement offered based on internet search history, and if so, how do companies determine why the searches were conducted?

Another seemingly unrelated field in which data science is heavily utilized is that of health care. Health care in this context relates to services, the utility of record keeping and analysis, insurance provisions, patient experience, diagnostics, the utilization of procedural data to streamline the system or assist future practitioners, and even study of nursing practices to inform the creation of tools, databases, and teaching methods.

However it is currently utilized, a central idea resonates. It’s clear data gathering and analysis is an evolving, dynamic, and highly plastic field that can be adapted to serve human endeavors by providing and analyzing information. What began as a stand-in term for computer science in 1960 has grown to encompass much of our community-focused interactions. Data science has learned first to walk and then run in the past five decades; will this simple term for a complex field soon take flight?


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