Data Analytics Series: Data Science vs. Data Analytics

Kyle Hodges

In the past few years, the term “big data” has become much more relevant in the worlds of both business and technology. Up until recently, I had no idea what big data was, or that UVA has a School of Data Science, and I’m sure many other students are also beginning to slowly learn more about how we can turn a knowledge of big data into a career. 

The first thing to understand when exploring career possibilities in big data is the difference between data science and data analytics. Although the two have similar names, they are quite different in terms of application and required skills. 

  • Data analytics refers to the process and practice of analyzing data to answer questions, extract insights, and identify trends. This is done using an array of tools, techniques, and frameworks that vary depending on the type of analysis being conducted.

  • Data science is centered on building, cleaning, and organizing datasets. Data scientists create and leverage algorithms, statistical models, and their own custom analyses to collect and shape raw data into something that can be more easily understood.

Basically, data science is similar to computer science, whereas data analytics is more closely related to statistics. Data analysts study what data scientists have organized to draw conclusions. So even though they are different jobs with different skills, they rely upon each other and work as a sort of team to untangle and interpret datasets.

One of the best parts about studying data is that you can do almost anything with your degree. There is a need for data analysts/scientists in almost every job field out there. Anywhere there is data, there is a need for someone to decipher the numbers. Some of the industries with a high demand for data analysts/scientists include healthcare, consulting, engineering, government/contracting, and e-commerce. That’s what makes a job in data so interesting: you can work anywhere and study data that covers everything from sales projections to medical breakthrough research. Data is a versatile and quickly growing job field; so if going into data sounds intriguing, go for it!

About the Author: Hi! My name is Katherine Calvert and I am a Career Peer Educator in the Business & Technology community at the Career Center. I am a second year in the College pursuing a B.A. in Economics, and I hope to eventually get an M.S. in Data Science.