Generative AI at BYU
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Undergraduate pathways

Study data. Build models.

BYU now offers distinct bachelor’s degrees in Data Science and Machine Learning. Both combine quantitative foundations, computing, ethical practice, and applied work—but they organize that preparation around different questions.

Compare the degreesOpen BYU catalog ↗

Two programs · shared foundations

  1. data science · collect, model, interpret, communicate
  2. machine learning · design, train, evaluate, deploy

Math · statistics · computingHuman need · ethics

Two ways into data-driven work.

This summary is an orientation aid. The official catalog and your MyMap govern the requirements for your catalog year.

Statistics · 74.5 credits

Data Science (BS)

Official program ↗

Builds the full data practice: acquisition, cleaning, storage, visualization, statistical modeling, interpretation, and communication. The program explicitly addresses unreliable data, bias, fairness, transparency, and the limits of inference.

  • Strong statistical and computing foundations
  • Data visualization, Bayesian methods, databases, and machine learning
  • Electives across scientific, economic, geographic, and computational domains
  • Capstone, internship, or research options

Computer Science · 74 credits

Machine Learning (BS)

Official program ↗

Combines computer science, mathematics, and statistics around the design and use of learning systems. The degree grew from BYU’s earlier Data Science/Machine Learning emphasis and became a standalone BS in Fall 2025.

  • Computer science, algorithmic, and software foundations
  • Probability, regression, optimization, and linear algebra
  • Machine learning, deep learning, natural-language processing, and related electives
  • Machine-learning capstone or undergraduate research

Choose by the work you want to practice.

Course titles overlap. The better distinction is the center of gravity.

Start with Data Science
If you want broad practice turning messy data into defensible insight—especially through statistics, visualization, uncertainty, and communication.
Start with Machine Learning
If you want deeper preparation in the computing and mathematical systems used to build, evaluate, and deploy learned models.
Test both
Take an introductory programming, data science, statistics, or machine-learning course and compare the work itself before deciding.
Ask an advisor
Bring a tentative course plan, prior credit, and the kind of problems you hope to solve. Requirements and substitutions depend on your catalog year.

Data-driven work also appears in other majors and emphases. Confirm current availability and requirements with the relevant department.

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