Illustration of data on screen
Data Science

This major develops foundational knowledge and expertise in processing, analyzing, modeling, and implementing software solutions for large data sets. Majors apply computing and modeling to multidisciplinary problems and develop professional skills in teamwork and problem solving in interdisciplinary settings and domains.

Focus Areas

Students select two:

  • Artificial Intelligence
  • Software Development
  • Data Pipeline
  • Mathematical Modeling
  • Statistical Modeling
  • Applied Computing: Journalism and Humanities
  • Applied Computing: Sociology
  • Applied Computing: Natural Resources

Academics + Experience = Opportunities

Complement academic preparation with experiential learning, creating more opportunities after graduation. Talk to an academic advisor and a career coach about making the most of your time at Nebraska.


Four-year success plan
What to do each year


Major requirements and courses


Printable summary
With sample four-year schedule

Transferrable Career Skills

  • Expertise in the analysis of large-scale data sources from the interdisciplinary perspectives of applied computer science, data modeling, mathematics, and statistics
  • Expertise in the application of computing, informatics, and modeling to solve multidisciplinary problems
  • Solving multidisciplinary data science problems as a member of an interdisciplinary team
  • Familiarity with ethical challenges in data science, including ethical collection of data, responsible use of data, and algorithmic bias


Experience is valuable and goes beyond the classroom. We strive to help you connect your academics with research, internships, education abroad, service learning and leadership experiences in data science, such as:

  • Interning with Hartford Insurance Company, Hudl, or Sandhills Global
  • Learn more about internships
  • Studying abroad in Budapest, Hungary or Bath, United Kingdom
  • Learn more about education abroad
  • Serving as the Math Club president
  • Learn more about leadership in student organizations
  • Volunteering with Math Motivators to tutor K-12 students
  • Learn more about service learning

Research and Academic Opportunities


Your skills in processing, analyzing, modeling, and implementing software solutions for large data sets and solving multidisciplinary problems in interdisciplinary settings will be extremely valuable to a variety of professions. Data science careers are some of the most in-demand across the country and have some of the highest job satisfaction rates.

Examples of Local and Regional Careers

  • Data Engineer, Hudl
  • GIS Web Developer/Analyst, The North Jackson Company
  • Statistical Analyst, Experian
  • Data and Research Analyst, Mercer
  • Business Analyst, Sandhills Publishing
  • Programmer/Analyst, Centrix Solutions

“'Data scientist' typically describes a knowledge worker who is principally occupied with analyzing complex and massive data resources. However, data science spans a broader array of activities that involve applying principles for data collection, storage, integration, analysis, inference, communication, and ethics...[A]ll undergraduates will benefit from a fundamental awareness of and competence in data science.”

National Academy of Sciences, Engineering, and Medicine

Read the report

Examples of MATH Courses

Core Requirement

Linear Algebra for Data Science | 315

Fundamental concepts of linear algebra, including properties of matrix arithmetic, systems of linear equations, vector spaces, inner products, determinants, eigenvalues and eigenvectors, and diagonalization, with emphasis in data science applications.

Professional Experience

Math in the City | 435

A research experience modeling problems of current interest to the local community, businesses, or government.

Focus Area

Graph Theory | 452

Theory of directed and undirected graphs. Trees, circuits, subgraphs, matrix representations, coloring problems, and planar graphs. Methods which can be implemented by computer algorithms.

Focus Area

Introduction to Topology | 471

Elementary point-set and geometric topology. Point-set topics include topological spaces, continuous functions, homeomorphisms, connectedness, compactness, quotient spaces. Geometric topology topics include Euler characteristic, classification of surfaces, and other applications.

Focus Area

Probability Theory | 487

Probability, conditional probability, Bayes' theorem, independence, discrete and continuous random variables, density and distribution functions, multivariate distributions, probability and moment generating functions, the central limit theorem, convergence of sequences of random variables, random walks, Poisson processes and applications.

Focus Area

Stochastic Processes | 489

Markov chains, continuous-time Markov processes, the Poisson process, Brownian motion, introduction to stochastic calculus.

Course listing in the Undergraduate Catalog

Data Science at Nebraska

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