Home 9 Featured Posts 9 “Caught My Eye” by Katina: Exploring the World of Data Science: A Primer for Librarians

“Caught My Eye” by Katina: Exploring the World of Data Science: A Primer for Librarians

by | Sep 15, 2022 | 0 comments

Exploring the World of Data Science: A Primer for Librarians is by Larissa Pack. Ms Pack is a freelance science and medical writer. Her article appears on the Information Today website.

Data science is a concept that is continuing to gain popularity in mainstream media. It can often be in discussions of AI, machine learning, data analytics, predictive analytics, or other related terms. Whether it is the recommended shows on your Netflix account, the creation of digital faces that are indistinguishable from those of real human beings, or even the candidacy of a data scientist in a recent U.S. election, data science is continually revolutionizing our world.

Data science is a combination of mathematics, programming, and the scientific process. Specialized blocks of code are developed to run large amounts of data through mathematical processes to find notable trends, answer complex questions, or develop solutions to a wide range of problems. Applications for data science may vary widely, but any business, governmental agency, or other institution can use data science to find quantitatively determined opportunities for growth and efficiency.

How Data Science Answers Tough Questions

Data science begins with a question. Regardless of whether the question is curious (e.g., Can you tell the difference between a goldendoodle puppy and a piece of fried chicken?) or complicated (e.g., “Can I use AI to determine if cancer exists in an image from a patient?”), the goal is to create a solution that is accurate, repeatable, and timely. …”

Once the question has been determined, a data scientist begins a multistep process to create the necessary solution. The first step in this process is to gather a large amount of data. For some questions, data has already been collected for others to use. However, other questions require data scientists to collect data through surveys or experiments or to “scrape” data from websites when allowed. …”

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