The Department of Applied Mathematics offers Bachelor of Arts and Bachelor of Science degrees 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. The BA degree is also conducive to double majoring. The BS degree requires additional coursework in computation, statistical modeling and theory, thus giving a more advanced understanding of statistics and data science, and is appropriate for students who want a deeper statistical foundation and/or who are planning to pursue graduate studies. 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 and BS degrees each require 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 on real-world problems and/or 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 Bayesian computation, epidemiology, statistical climatology, statistics for energy science, signal processing and image analysis, networks, machine learning for physical systems, uncertainty quantification as well as the study of collaborative research. Students can gain professional exposure through the Data Buffs, the student chapter of the American Statistical Association (ASA) on campus.
Outside Area of Emphasis/Application
Students will choose an outside area of emphasis/application to acquire knowledge in an approved discipline-specific area of their choice 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 relevant departments, as well as faculty advisors within the Department of Applied Mathematics.
Capstone/Collaboration Class
The degree culminates in a capstone experience giving students the opportunity to apply the knowledge, skills and abilities developed throughout the Statistics and Data Science major. The capstone experience can be fulfilled through a semester-long project course, or through the Laboratory for Interdisciplinary Statistical Analysis (LISA). Both options offer an opportunity to gain valuable real world experience in collaboration with domain experts from government, industry or academia.
Requirements
Course Requirements
To earn a BS 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
| Code | Title | Credit Hours |
|---|---|---|
| Required Courses | ||
| Mathematical Foundations | ||
| APPM 2340 | Calculus 3 for Statistics and Data Science | 4 |
| or APPM 2350 | Calculus 3 for Engineers | |
| or MATH 2400 | Calculus 3 | |
| APPM 3310 | Matrix Methods and Applications | 3 |
| Computation | ||
| STAT 2600 | Introduction to Data Science | 4 |
| APPM 3650 | Algorithms and Data Structures in Python | 3 |
| Theoretical Core | ||
| STAT 3100 | Applied Probability | 3 |
| STAT 4520 | Introduction to Mathematical Statistics | 3 |
| Modeling Core | ||
| STAT 3400 | Applied Regression | 3 |
| STAT 4400 | Advanced Statistical Modeling | 3 |
| APPM 4560/STAT 4100 | Markov Processes, Queues, and Monte Carlo Simulations | 3 |
| Machine Learning/Artifical Intelligence Core | ||
| STAT 4610 | Statistical Learning | 3 |
| STAT 4350 | Applied Deep Learning 1 | 3 |
| or CSCI 4622 | Machine Learning | |
| Senior Capstone Project | ||
| STAT 4640 | Capstone in Statistics and Data Science | 3 |
| or STAT 4680 | Statistics and Data Science Collaboration | |
| Four of the following courses: 1 | 12 | |
| Data Assimilation in High Dimensional Dynamical Systems | ||
| Applied Deep Learning 2 | ||
| Spatial Statistics | ||
| Introduction to Time Series | ||
| Computational Bayesian Statistics | ||
| Philosophical and Ethical Issues in Statistics | ||
| Introduction to Operations Research | ||
| Introduction to Dynamics on Networks | ||
| Computational Neuroscience | ||
| Undergraduate Applied Analysis 1 | ||
| Undergraduate Applied Analysis 2 | ||
| Theory of Machine Learning | ||
| High-Dimensional Probability for Data Science | ||
| Stochastic Analysis for Finance | ||
| Random Graphs | ||
| Numerical Methods and Scientific Computing | ||
| Total Credit Hours | 50 | |
| 1 | As well as other APPM or STAT upper-division advisor-approved courses. |
Ancillary Requirements
| Code | Title | Credit Hours |
|---|---|---|
| Quantitative Skills | ||
| APPM 1350 | Calculus 1 for Engineers | 4 |
| APPM 1360 | Calculus 2 for Engineers | 4 |
| Computing Requirement | ||
| APPM 1650 | Python for Math and Data Science Applications 1 | 4 |
| or CSCI 1300 | Computer Science 1: Starting Computing | |
| or CSCI 2750 | Computing, Ethics and Society | |
| or ASEN 1320 | ||
| 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. Note: Not necessarily NS/math/computing/social science. 2 | 18 | |
| Total Credit Hours | 30 | |
| 1 | Or another department-approved course in Python with Mathematical Applications. |
| 2 | Can be used to fulfill Gen. Ed. requirements when applicable. |
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.
| Year One | ||
|---|---|---|
| Fall Semester | Credit Hours | |
| APPM 1350 | Calculus 1 for Engineers | 4 |
| APPM 1650 | Python for Math and Data Science Applications | 4 |
| Gen. Ed. Skills course (example: Lower-division Written Communication) | 3 | |
| Gen. Ed. Distribution course (example: Natural Sciences with Lab) | 4 | |
| Credit Hours | 15 | |
| Spring Semester | ||
| APPM 1360 | Calculus 2 for Engineers | 4 |
| STAT 2600 | Introduction to Data Science | 4 |
| Gen. Ed. Distribution/Diversity course (example: Arts & Humanities/US Perspective) | 3 | |
| Elective | 4 | |
| Credit Hours | 15 | |
| Year Two | ||
| Fall Semester | ||
| APPM 2340 or APPM 2350 | Calculus 3 for Statistics and Data Science or Calculus 3 for Engineers | 4 |
| STAT 3100 | Applied Probability | 3 |
| Outside Area of Emphasis course | 3 | |
| Gen. Ed. Distribution course (example: Arts & Humanities) | 3 | |
| Gen. Ed. Distribution/Diversity course (example: Social Sciences/Global Perspective) | 3 | |
| Credit Hours | 16 | |
| Spring Semester | ||
| APPM 3310 | Matrix Methods and Applications | 3 |
| STAT 3400 | Applied Regression | 3 |
| APPM 3650 | Algorithms and Data Structures in Python | 3 |
| Outside Area of Emphasis course | 3 | |
| Gen. Ed. Distribution course (example: Social Sciences) | 3 | |
| Credit Hours | 15 | |
| Year Three | ||
| Fall Semester | ||
| STAT 4520 | Introduction to Mathematical Statistics | 3 |
| STAT 4610 | Statistical Learning | 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 Hours | 15 | |
| Spring Semester | ||
| STAT 4400 | Advanced Statistical Modeling | 3 |
| APPM 4560 | Markov Processes, Queues, and Monte Carlo Simulations | 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 Hours | 15 | |
| Year Four | ||
| Fall Semester | ||
| STAT 4640 or STAT 4680 | Capstone in Statistics and Data Science or Statistics and Data Science Collaboration | 3 |
| Upper-division STAT elective | 3 | |
| Upper-division STAT elective | 3 | |
| Gen. Ed. Distribution course (Social Sciences) | 3 | |
| Outside Area of Emphasis (upper-division) course or elective | 3 | |
| Credit Hours | 15 | |
| Spring Semester | ||
| STAT 4350 | Applied Deep Learning 1 | 3 |
| Upper-division STAT elective | 3 | |
| Upper-division STAT elective | 3 | |
| Outside Area of Emphasis (upper-division) course or elective | 3 | |
| Outside Area of Emphasis (upper-division) course or elective | 3 | |
| Credit Hours | 15 | |
| Total Credit Hours | 121 | |
Learning Outcomes
By the completion of the program, students will be able to:
- Demonstrate problem-solving and modeling skills that allow the student to analyze and visualize data and answer statistical questions.
- Apply foundational mathematical concepts, including calculus and linear algebra, and advanced mathematical statistical concepts, including probability, to statistics and data science.
- Demonstrate proficiency in at least two programming languages and their data science packages and the ability to write efficient, reproducible code related to data analysis.
- Demonstrate in-depth knowledge of an application area and skills to collaborate with domain experts.
- Communicate statistical results clearly and concisely in oral, written and visual forms.