Bachelor's Degree Options
BS in Mathematics: Applied and Computational Mathematics Emphasis
Brigham Young University’s Applied and Computational Math Emphasis (ACME) program is a major designed for solving the problems of the 21st century. Mathematics provides the foundation of modern technology and science, it is the key to building successful algorithms in artificial intelligence and machine learning, and it provides the analytical power needed to process, evaluate, and take full advantage of the ever-growing flood of data and information in the world. ACME is a new educational model that teaches both the theory and the practical skills in mathematics, statistics, and computation needed to solve the problems of the modern world.
Students who participate in ACME can expect a focus in rigorous mathematics, scientific computing, and modeling. After finishing two years of mathematical prerequisites, students then take two year-long core sequences each year for their junior and senior years.
Program Outcomes
Mathematics Fundamentals
Demonstrate basic mathematical understanding and computational skills in calculus, linear algebra, and differential equations, and advanced calculus.
Effective Reasoning and Communication
Explain and criticize mathematical reasoning through speaking and writing in a precise and articulate manner.
Statistics
Demonstrate a knowledge of inference, estimation, regression, multivariable statistics, Bayesian statistics, time-series analysis, and state-space modeling.
Advanced Applied Mathematics
Demonstrate understanding of linear and nonlinear analysis, the analysis of algorithms, combinatorics, asymptotic methods, approximation theory, transform theory, optimization, dynamic programming, probability theory, stochastic processes, differential equations, dynamical systems and optimal control theory.
Computing
Demonstrate facility in computer programming, data processing, databases, numerical simulation, scientific visualization, and virtual experimentation. Write, compile and execute numerical algorithms in a low-level language, such as C/C++, as well as develop I/O wrappers for standard numerical libraries in a common scripting language, such as Python.
Demonstrate the ability to use the technologies for parallel and distributed computing.
FOR MORE INFORMATION ON AMCE, CONTACT
Applied & Computational Mathematics Emphasis (ACME)
801-422-2061
275 TMCB Brigham Young University
Provo, UT 84602
acme@mathematics.byu.edu
BS in Bioinformatics
Bioinformatics is an interdisciplinary program offering substantial training in both the biological sciences and the physical and mathematical sciences; our program emphasizes the integration of computer science with biology. A foundation in biology, computer science, and statistics provides the basis for developing and applying computational methods to test biological hypotheses. Students attracted to this program have dual interests in computer science and biology and find it an excellent choice for their broad interests.
Students who complete this program either enter the top graduate programs in bioinformatics or the life sciences in the world, enter leading professional schools (including law school, medical school, or dental school), or find employment in biotechnology, pharmaceutical, or software development companies.
PROGRAM OUTCOMES
Broad Understanding of Biology
Students will interpret relationships among living things and analyze and solve biological problems, from the molecular to ecosystem level using basic biological concepts, grounded in foundational theories.
Computer Programming
Students will design computer programs to facilitate biological data analysis.
Research and Inquiry
Students will be able to conduct basic bioinformatics research.
FOR MORE INFORMATION ON BIOINFORMATICS, CONTACT
Department of Biology
4102 Life Sciences Building (LSB)
Provo, Utah 84602
801-422-2582
lsadvisement@byu.edu
BS in Machine Learning
BYU's Machine Learning offers students the unique chance to learn the practical and theoretical aspects of data science through a blend of programming, machine learning, optimization, big data, linguistics, and computational practice.
In the Machine Learning program, 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 program requires various courses founded in data science that span data bases, machine learning, deep learning, regression, probability, natural language processing, calculus, and convex optimization.
PROGRAM OUTCOMES
Machine Learning Practice:
Students will design and implement significant computer programs that meet a human need.
Machine Learning Theory:
Students will analyze problems and their algorithmic solutions using theoretical concepts.
Career Preparation:
Students will have sufficient maturity in machine learning to work in a professional setting or enter a graduate program.
Diversity, Equity, and Inclusion:
Our program is accessible to everyone, including women, minorities, and those new to programming, and provides an equal opportunity for every student to succeed.
FOR MORE INFORMATION, CONTACT
Lynnette Nelson, CS Undergraduate Advisor
2250 TMCB
lnelson@cs.byu.edu
(801) 422-9439
https://calendly.com/lnsch-1/cs-240-interview-appointment?month=2025-06
BS in Statistics: Data Science Emphasis
The curriculum and degrees offered through the Department of Statistics are designed to equip students with decision-making skills for careers as professional statisticians in industrial organizations, government agencies, insurance companies, pharmaceutical companies, universities, and research institutes.
The Data Science emphasis is designed to help students develop skills that are needed to work on a data science team. These skills include programming, facility with data structures and algorithms, statistical methods, and experience working with real world big data problems. Students with a Data Science emphasis leave BYU with a multi-faceted, disciplined, and flexible approach to data, a rich vocabulary for working with others in data-focused disciplines, and a well- developed capacity for understanding and communicating statistical results.
PROGRAM OUTCOMES
Skill in fitting statistical models:
Graduates of the program will demonstrate the ability to analyze data by appropriately fitting, assessing, and interpreting a variety of statistical models.
Computing skills:
Graduates of the program will be able to manipulate data, use appropriate statistical methods, document, and debug code in one or more statistical software programs.
Communication skills:
Graduates of the program will be able to write technical reports and make technical presentations containing statistical results, and work in teams to demonstrate the consulting skills of a professional statistician.
Theoretical Foundations:
Graduates of the program will be able to solve problems in basic probability theory, statistical inference and calculus.
Professional Preparation:
Graduates of the program will be employable in jobs with BS Data Science or BS Applied Statistics requirement.
FOR MORE INFORMATION, CONTACT
Kimri Mansfield, Undergraduate Advisor
kmansfield@stat.byu.edu
2152D WVB
801-422-4506
https://calendar.app.google/uvuh6V2tScnEZ3LN9
BS in Applied Physics: Data Science
The new BS in Applied Physics: Data Science major provides students experience with the fundamentals of scientific modeling and data science from an underlying foundation in physics. Students gain an understanding of physical laws and how models provide predictive and explanatory descriptions of complex physical and astronomical processes. Complementing this model building perspective for students, experimental lab courses give hands-on experience with designing physical experiments, collecting data, and determining measurement uncertainty. All courses help students build intuition about uncertainty and bias in measurements that inform physics-based approaches to data science.
Students complete mentored research projects in data science on campus or through external internships. Each student completes sufficient research to write a senior thesis or capstone report. This experiential learning is the culminating experience for majors in the Department of Physics and Astronomy and is a crucial part of their preparation for graduate studies or to successfully begin their careers on graduation.
Physics Theory and Application:
Apply principles to model and solve representative problems analytically and computationally at an introductory level from the primary physical theories (classical mechanics, quantum mechanics, special relativity, thermodynamics, electromagnetism, and optics) and at an advanced level in classical mechanics, electrostatics, statistical mechanics, thermodynamics, and optics/electrodynamics.
Data Science Theory and Application:
Apply principles of data science to model physical and astronomical systems, address physical problems, and evaluate the uncertainty, sensitivity, and fidelity of the models.
Experiment and Computational Skills:
Design and conduct experiments, build scientific equipment, write scientific programs to simulate physical systems, and analyze data.
Effective Communication:
Communicate professionally to a technical audience both orally and in writing. Be able to understand scientific ideas by reading books and journal articles.
Professional Ethics:
Understand scientific ethical practices and demonstrate them in the conduct of scientific research.
Research and Professional Preparation:
Conduct experimental, theoretical, or computational research related to data science under the direction of a mentor to contribute to the generation of new knowledge or technologies and prepare to do this professionally.
FOR MORE INFORMATION, CONTACT
Department of Physics and Astronomy
N283 ESC
Provo, UT 84602
physics_office@byu.edu
801-422-4361
https://physics.byu.edu/undergraduate/advising#faculty-academic-advisors
BS in Data Science
The Interdisciplinary Data Science BS degree supports students preparing for competitive jobs in data science, machine learning, statistics, artificial intelligence, and related fields. The program will prepare students for competitive positions in many of the world’s best companies, and can also serve as excellent preparation for graduate work. Graduates of this program will be able to curate and maintain data, build models to effectively analyze this data, understand the principles of modeling and the basis of the models themselves in order to draw accurate conclusions from these analyses, and ethically provide insight in how to use this work in their various applications.
The BS in Data Science program provides a solid data science core and is supported by foundational courses in mathematics, statistics and computer science. Students will also complete data science themed courses in a discipline of their choice, and a capstone experience that provides real-world contact with employers and their challenges.
Students interested in adjacent fields such as business, science and engineering that require skills such as data management, analytics, visualization, and forecasting would also benefit from the program.
Program Outcomes
Data Science Practice:
Students will be able to acquire, clean, wrangle, store, analyze and visualize a wide variety of data types using a wide variety of data science tools; they will be able to apply their knowledge of tools to solve novel problems, and understand the benefits and limitations of new tools.
Data Science Theory:
Students will demonstrate the ability to analyze data by appropriately visualizing, fitting, assessing, and interpreting a variety of models, and will understand the limits and possibilities of conclusions drawn from data.
Data Science Ethics:
Students will be able to mitigate the effects of biased or unreliable data, will be able to avoid the dangers of overreliance on unreliable data, and will be an ethical and positive voice arguing for fairness and transparency in high-impact applications of data science.
Communication Skills:
Students will be able to create effective visualizations, write technical reports and make technical presentations that convey insight harvested from data.
Professional Preparation:
Students will have sufficient maturity in data science to work in a professional setting in data science or to enter a graduate program.
FOR MORE INFORMATION, CONTACT
Natalie Romeri-Grass
#2162 WVB
801-422-9202
natalie.rg@byu.edu