Data Science B.S.

CSU’s Bachelor of Science in Data Science equips you with the knowledge and skills to thrive in today’s data-driven world. This interdisciplinary program combines mathematics, statistics, and computer science to prepare students for real-world applications of data analysis. You’ll learn to navigate and interpret large, unstructured datasets, build statistical models, and master tools like Python and R programming. Whether you're interested in AI, machine learning, or data visualization, this program offers a strong foundation to solve complex problems across industries.

Customize your education

Tailor your degree to align with your career aspirations through flexible learning opportunities. In your first two years, you'll build a strong foundation in core subjects before advancing into specialized coursework, such as Computer Science. Additionally, you’ll have the chance to apply your knowledge through a capstone project, participate in undergraduate research with the Data Science Research Institute, or explore independent learning through Individual Study and Honors Projects.

Achieve more in less time

Maximize your educational journey with stackable coursework leading to a Master’s in Applied Statistics. Through the accelerated master’s program, you can complete both a B.S. and an M.S. in just five years. This streamlined path offers advanced career opportunities and ensures you're ready to meet the growing demand for skilled data scientists.

Hands-On experience and career preparation

Students gain more than theoretical knowledge through CSU’s hands-on learning approach. Participate in co-curricular opportunities including internships, undergraduate research, and conferences where you can apply your skills in real-world settings. Collaborate with faculty and peers to complete projects that reflect the challenges and opportunities in today’s industries.

Endless potential across industries

The versatility of data science means graduates can apply their expertise across a variety of disciplines, with opportunities in:

• Technology: Machine learning engineering, data analysis, AI development.
• Healthcare: Predictive modeling, bioinformatics, patient data analysis.
• Finance: Fraud detection, risk analysis, algorithmic trading.
• Retail and Marketing: Customer behavior insights, inventory optimization, personalized marketing.
• Public Sector: Policymaking, urban planning, public health analysis.

Unlock opportunities in a rapidly growing field

The need for data scientists is rapidly growing, with a projected 35% employment growth between 2022 and 2032, much faster than average. This degree equips you with the skills to analyze, interpret, and visualize complex datasets, making you an invaluable asset in any data-driven industry. No matter what sector your passion lies in, this program sets you up for a fulfilling and impactful career in today’s digital economy.

Freshman

• CO 150 – College Composition (3 cr.)
• CS 150 B – Culture and Coding: Python (3 cr.)
• CS 164 – CS1--Computational Thinking with Java (4 cr.)
• DSCI 100 – First Year Seminar in Data Science (1 cr.)
• DSCI 369 – Linear Algebra for Data Science (4 cr.)
• MATH 156 – Mathematics for Computational Science I (4 cr.)
• STAT 158 – Introduction to R Programming (1 cr.)
• STAT 315 – Intro to Theory and Practice of Statistics (3 cr.)
• Electives (6 cr.)

Total credits: 29

Sophomore

• CS 165 – CS2--Data Structures (4 cr.)
• CS 220 – Discrete Structures and the Applications (4 cr.)
• DSCI 235 – Data Wrangling (2 cr.)
• MATH 151 – Mathematical Algorithms in Matlab I (1 cr.)
• MATH 256 – Mathematics for Computational Science II (4 cr.)
• STAT 341 – Statistical Data Analysis I (3 cr.)
• STAT 342 – Statistical Data Analysis II (3 cr.)

Select one course from the following (4 cr.)

• CS 250 – Computer Systems Foundations
• CS 270 – Computer Organization
• Electives (4 cr.)

Total credits: 29

Junior

• CS 201 – Ethical Computing Systems (3 cr.)
• CS 214 – Software Development (3 cr.)
• DSCI 320 – Optimization Methods in Data Science (3 cr.)
• DSCI 335 – Inferential Reasoning in Data Analysis (3 cr.)
• DSCI 336 – Data Graphics and Visualization (1 cr.)

Select one course from the following (3 cr.)

• CS 320 – Algorithms--Theory and Practice
• CS 370 – Operating Systems

Select one course from the following: (3 cr.)

• CO 300 – Writing Arguments
• CO 301B – Writing in the Disciplines: Sciences
• CO 302 – Writing in Digital Environments
• JTC 300 – Strategic Writing and Communication

Electives: Select 2 Computer Science Electives and one Social and Behavioral Sciences elective.

Total credits: 30

Senior

• DSCI 445 – Statistical Machine Learning (3 cr.)
• DSCI 478 – Capstone Group Project in Data Science (4 cr.)

Electives: Select minimum 9 credits of Data Science Electives, 13-15 credits of other electives.

Total credits: 32-34

Total program credits: 120

Asa Ben-Hur

My lab specializes in applications of machine learning in computational biology, aka bioinformatics.

Andee Kaplan

I'm interested in the intersection of Statistics and Computing, specifically the topics of Bayesian Statistics, Computational Statistics, Record Linkage and Entity Resolution, Markov chain Monte Carlo and Spatial Resampling, Statistical Machine Learning, Interactive Statistical Graphics, and Reproducible Research. I like struggling with JavaScript and learning new languages, R being my first love.

Emily King

My research takes an interdisciplinary approach to harmonic analysis with applications in image processing and data analysis. The modern world is powered by a deluge of data, and much of human progress hinges on developing better methods to process this data. Many powerful data processing algorithms rely on a well-designed representation system, and we can leverage beautiful mathematics to design such systems. Much of my research concerns the design of representation systems involving rich symmetries and combinatorial structures that allow one to extract critical features from various classes of data.

Michael Kirby

Darrell Whitley

Prof. Whitley is a faculty member (and former Chair, 2003-2018) of the Department of Computer Science at Colorado State University. From 1993 to 1997 Prof. Whitley served as Chair of the Governing Board of the International Society for Genetic Algorithms. From 1997 to 2002 Prof. Whitley served as Editor-in-Chief for the journal Evolutionary Computation published by MIT Press. From 2007 to 2011 Prof. Whitley was Chair of the ACM SIG on Evolutionary Computation (SIGEVO), and a member of the ACM SIG Governing Board.

James Wilson

• Ph.D. in Mathematics, University of Oregon, Eugene, June 2008.
• M.S. in Mathematics, University of Oregon, Eugene, June 2004.
• B.S. in Mathematics, Portland State University, Portland, June 2002.

Application Deadlines

Planning to transfer credits from another college or university? Please review our FAQ page, then complete a Tentative Transfer Evaluation Form to see how your prior credits may transfer.

Questions? Call (970) 491-7468 or email terrance.dickens@colostate.edu.