BYU's Computer Science Data Science Emphasis offers students the unique chance to learn the practical and theoretical aspects of data science through a blend of programming, statistics, machine learning, optimization, big data, linguistics, and computational practice.
Data science is one of the fastest growing jobs in the world, because the amount of data used by industry and academia has skyrocketed: about 90% of the world's data was created in the last two years, with about 2.5 quintillion bytes of data produced every day. Contained in these vast troves of data are patterns that can be turned into actionable insights that improve our lives, streamline our processes, reduce our costs, and better connect people, products, and information.
But it is impossible for humans to sort through these mountains of data, and so both industry and academia are increasingly turning to the tools of data science and machine learning to make sense of it. Such tools must be principled, with a solid theoretical underpinning, but also practical, with scalable calculations that can be accelerated, parallelized, and deployed on massive datasets.
In the CS Data Science emphasis, students will learn both the theoretical and practical aspects of data science—focusing on the mathematical fundamentals that describe patterns, uncertainty, and knowledge representations, while at the same time sharpening computational thinking and programming know-how needed to turn ideas into reality.
The emphasis requires computer science courses, statistics courses, and data science-related electives that span data bases, machine learning, deep learning, regression, probability, statistics, natural language processing, calculus, and convex optimization.
For more information about the Data Science Emphasis, please contact the CS Undergraduate Advisor, Lynnette Nelson at email@example.com or (801) 422-9439.
We strongly encourage participation in our data science capstone classes as part of this emphasis. For more information, please see this page.