The Department of Applied Mathematics offers a Bachelor of Arts degree in statistics and data science through the College of Arts and Sciences. The BA degree is designed with an emphasis on inter- and cross-disciplinary training, and is intended to prepare students for a wide range of careers in areas such as statistics, data analytics, data science, business, engineering, economics, public health, epidemiology, insurance, forestry, psychology, social justice and human rights.

Courses at the undergraduate level are designed to provide foundational skills in both traditional statistical methods and cutting-edge data analysis techniques. These skills are in high demand in the current job market and prepare students for desirable careers in statistics and data science. Statisticians and data scientists are often involved in interdisciplinary work; the BA degree requires in-depth training in some area of science, engineering, social science or liberal arts that uses statistics to solve important problems. This knowledge prepares graduates to successfully communicate and collaborate with practitioners in these fields. A capstone course in statistical collaboration provides the opportunity for students to synthesize their previous coursework.

The Department of Applied Math offers a broad range of undergraduate research opportunities funded by a variety of federal agencies. Working with faculty, students interested in statistics and data science have developed solutions to various problems in fluids, dynamical systems, data analysis, probability and statistics, networks, signal processing, math biology, math education and numerics.  Students can gain professional exposure through the student chapter of the Society of Industrial and Applied Mathematics (SIAM) on campus.

Outside Area of Emphasis/Application

Students will choose an outside area of emphasis/application to acquire knowledge in a discipline-specific area, where statistical applications are prevalent. Students will take a minimum of 18 credits in a department or certificate program outside of APPM/STAT, including a minimum of 6 credits at the upper-division level. Final course selection will be made in consultation with advisors and faculty from the departments, as well as faculty advisors within the Department of Applied Mathematics.

Laboratory for Interdisciplinary Statistical Analysis (LISA)

After learning the communication and collaboration skills necessary to help domain experts answer their research, business or policy questions, students have the opportunity to join LISA to gain additional practical experience. Students will collaborate with a variety of researchers around campus and in the community to apply statistics and data science to solve real problems. Students in LISA will also work with graduate students and faculty to engage in outreach activities to improve statistics and data science skills and literacy in the wider community.


Course Requirements

To earn a BA in statistics and data science, a student must complete the requirements of the College of Arts and Sciences.

Students must earn a grade of C- or better in all coursework applied to the major and have at least a C average for all attempted work for the major. Calculus 1 & 2 (usually APPM 1350 and APPM 1360) are considered introductory courses and are prerequisites for entry into the major.

Required Courses and Credits

Required Courses
Mathematical Foundations
APPM 2340Calculus 3 for Statistics and Data Science4
or APPM 2350 Calculus 3 for Engineers
or MATH 2400 Calculus 3
APPM 3310Matrix Methods and Applications3
STAT 2600Introduction to Data Science4
Statistics Theory
STAT 3100Applied Probability3
STAT 4520Introduction to Mathematical Statistics3
Statistical Modeling
STAT 3400Applied Regression3
STAT 4610Statistical Learning3
One of the following courses:
STAT 4640Capstone in Statistics and Data Science3
or STAT 4680 Statistics and Data Science Collaboration
Three of the following courses: 19
Markov Processes, Queues, and Monte Carlo Simulations
Data Assimilation in High Dimensional Dynamical Systems
Applied Deep Learning 1
Applied Deep Learning 2
Advanced Statistical Modeling
Spatial Statistics
Introduction to Time Series
Computational Bayesian Statistics
Philosophical and Ethical Issues in Statistics
Algorithms and Data Structures in Python
Computational Neuroscience
Undergraduate Applied Analysis 1
Theory of Machine Learning
High-Dimensional Probability for Data Science
Stochastic Analysis for Finance
Random Graphs
Total Credit Hours35

Ancillary Requirements

Computing Requirement
APPM 1650Python for Math and Data Science Applications 14
or CSCI 1300 Computer Science 1: Starting Computing
or CSCI 2750 Computing, Ethics and Society
or ASEN 1320 Aerospace Computing and Engineering Applications
Outside Area of Emphasis Requirement
Additional coursework in a department or certificate program outside of APPM/STAT, including a minimum of 6 credits at the upper-division level. 218
Total Credit Hours22

Graduating in Four Years

Consult the four-year graduation guarantee for information on eligibility. The concept of "adequate progress" as it is used here only refers to maintaining eligibility for the four-year guarantee; it is not a requirement for the major. To maintain adequate progress in Statistics and Data Science, students should meet the following requirement:

  • In the first semester, declare the statistics and data science major.

Students must consult with a major advisor to determine adequate progress toward completion of the major.

Recommended Four-Year Plan of Study

Through the required coursework for the major, students will fulfill 12 credits in the Natural Science area, but not the laboratory requirement, of the Gen Ed Distribution Requirement and will complete the QRMS component of the Gen Ed Skills Requirement. Students can also possibly fulfill some of the required credit hours in the other areas Gen Ed Distribution and Diversity Requirements with the courses they take to complete the required Outside Area of Emphasis. 

Plan of Study Grid
Year One
Fall SemesterCredit Hours
APPM 1350 Calculus 1 for Engineers 4
STAT 2600 Introduction to Data Science 4
Gen. Ed. Skills course (example: Lower-division Written Communication) 3
Gen. Ed. Distribution course (example: Natural Sciences with Lab) 4
 Credit Hours15
Spring Semester
APPM 1360 Calculus 2 for Engineers 4
APPM 1650
Python for Math and Data Science Applications
or Computer Science 1: Starting Computing
Gen. Ed. Distribution/Diversity course (example: Arts & Humanities/US Perspective) 3
Elective 4
 Credit Hours15
Year Two
Fall Semester
APPM 2340
Calculus 3 for Statistics and Data Science
or Calculus 3 for Engineers
Gen. Ed. Distribution course (example: Arts & Humanities) 3
Gen. Ed. Distribution/Diversity course (example: Social Sciences/Global Perspective) 3
Outside Area of Emphasis course 3
Elective 3
 Credit Hours16
Spring Semester
APPM 3310 Matrix Methods and Applications 3
STAT 3100 Applied Probability 3
Gen. Ed. Distribution course (example: Social Sciences) 3
Outside Area of Emphasis course 3
Elective 3
 Credit Hours15
Year Three
Fall Semester
STAT 3400 Applied Regression 3
STAT 4520 Introduction to Mathematical Statistics 3
Outside Area of Emphasis Course (Upper-division) 3
Gen. Ed. Skills course (example: Upper-division Written Communication) 3
Gen. Ed. Distribution course (example: Arts & Humanities) 3
 Credit Hours15
Spring Semester
STAT 4610 Statistical Learning 3
Upper-division STAT elective 3
Outside Area of Emphasis Course (Upper-division) 3
Gen. Ed. Distribution course (example: Arts & Humanities) 3
Gen. Ed. Distribution course (example: Social Sciences) 3
 Credit Hours15
Year Four
Fall Semester
STAT 4640
Capstone in Statistics and Data Science
or Statistics and Data Science Collaboration
Upper-division STAT elective 3
Gen. Ed. Distribution course (Social Sciences) 3
Outside Area of Emphasis (upper-division) course or elective 3
Elective 3
 Credit Hours15
Spring Semester
Upper-division STAT elective 3
Outside Area of Emphasis (upper-division) course or elective 3
Elective(s) 8
 Credit Hours14
 Total Credit Hours120

Content Knowledge

Students completing the undergraduate degree in statistics and data science will be broadly knowledgeable in the following areas:

  • Mathematics, statistics and data science
    • Foundational knowledge in the areas of mathematics, statistics and data science that are most important to the analysis of data.
    • Statistical intuition and thinking.
    • Skills to write efficient, reproducible code related to data analysis in at least two programming languages (e.g., R, Python, C/C++, Julia, MATLAB, etc.).
    • Skills necessary to complete complex data analysis projects.
  • A domain of application
    • The ability to utilize their knowledge of mathematics, statistics and computing to develop algorithms and apply methods for solving real-world data analysis problems.
    • The ability to contribute to at least one domain of application as data scientists.
  • Professional skills in communication, collaboration and ethics
    • The ability to effectively communicate statistical results to experts and non-experts.
    • The ability to effectively collaborate with domain experts.
    • The ability to think critically about the relationship between data, ethics, and society.

Student Outcomes 

Upon graduation, students will:

  • Have acquired problem-solving and modeling skills that allow them to analyze and visualize data and answer statistical questions.
  • Understand mathematical statistics, including probability.
  • Have acquired foundational mathematical knowledge, including calculus and linear algebra, as it pertains to statistics and data science.
  • Be proficient in at least two programming languages and their data science packages.
  • Be able to write efficient, reproducible code related to data analysis.
  • Have acquired an in-depth knowledge of an area of application, as well as skills to collaborate with domain experts.
  • Have the ability to clearly and concisely communicate statistical results in oral, written and visual forms.

Bachelor's–Accelerated Master's Degree Program(s)

The bachelor's–accelerated master's (BAM) degree program options offer currently enrolled CU Boulder undergraduate students the opportunity to receive a bachelor's and master's degree in a shorter period of time. Students receive the bachelor's degree first but begin taking graduate coursework as undergraduates (typically in their senior year).

Because some courses are allowed to double count for both the bachelor's and the master's degrees, students receive a master's degree in less time and at a lower cost than if they were to enroll in a stand-alone master's degree program after completion of their baccalaureate degree. In addition, staying at CU Boulder to pursue a bachelor's–accelerated master's program enables students to continue working with their established faculty mentors.

BA in Statistics and Data Science, MS in Applied Mathematics

Admissions Requirements

In order to gain admission to the BAM program named above, a student must meet the following criteria:

  • Complete a minimum of 6 credits (2 courses) of STAT coursework at the 3000 or 4000 level.
  • Complete all prerequisite courses with a minimum grade of B.
  • Have a cumulative GPA of 3.4 or higher.
  • Have a cumulative GPA of 3.4 in all APPM and STAT coursework. If a student's cumulative GPA or APPM/STAT GPA is between 3.0 and 3.4, then one letter of reference is required. The letter can be written either by a faculty member or by a student’s undergraduate academic advisor. The letter should justify why the student should be considered for admission into the program and should attest to the student's ability to complete the MS program.
  • Have at least junior class standing.

Program Requirements

Students may take up to and including 12 credit hours while in the undergraduate program which can later be used toward the master’s degree. However, only six credit hours may be double counted toward the bachelor’s degree and the master’s degree. Students must apply to graduate with the bachelor’s degree, and apply to continue with the master’s degree, early in the semester in which the undergraduate requirements will be completed.

Though not required for admission, students must complete APPM 4440 Undergraduate Applied Analysis 1 before they graduate with their BA.

Please see the BAM degree program web page for more information.