Offered at: San Luis Obispo Campus
The Bachelor of Science (BS) in Data Science is an interdisciplinary program that prepares students to analyze, interpret, and derive insights from complex data. The curriculum combines foundational knowledge in mathematics, statistics, computer science, and domain-specific applications, providing students with the skills necessary for careers in data analysis, machine learning, and big data management. Students gain proficiency in programming in multiple languages, data visualization, data mining, and predictive modeling, while engaging in hands-on projects with real-world data. This program equips graduates to excel in data-driven decision-making roles across diverse industries.
Concentrations
Machine Learning Engineering
Offered at: San Luis Obispo Campus
The Machine Learning Engineering concentration prepares students to design, build, deploy, and maintain scalable machine learning systems in real-world environments. Blending statistical modeling, software engineering, and data infrastructure, this concentration emphasizes the practical implementation of machine learning solutions beyond experimentation.
Mathematical Foundations
Offered at: San Luis Obispo Campus
The Mathematical Foundations concentration provides students with a rigorous grounding in the theoretical principles that underpin data science. This concentration emphasizes the mathematical structures, methods, and reasoning essential for understanding and developing modern data-driven techniques.
Statistical Modeling
Offered at: San Luis Obispo Campus
The Statistical Modeling concentration focuses on the development, analysis, and application of statistical methods for understanding complex data. This concentration prepares students to build interpretable models, draw valid inferences, and quantify uncertainty in data-driven decision-making. Through applied projects and coursework, students gain experience selecting appropriate modeling techniques, evaluating model performance, and interpreting results in context.
Program Learning Objectives
- Analyze a complex data science problem and apply principles of statistics, computer science, mathematics, and a domain-specific field, such as health, engineering or application development, to identify solutions.
- Design, implement, and evaluate a data-driven solution to meet a given set of analysis requirements.
- Work with data of a wide range of volume, variety, velocity, and veracity.
- Communicate effectively in a variety of professional contexts.
- Attend to professional responsibilities and make informed judgments in data science practice based on legal and ethical principles.
- Function effectively as a member or leader of a team engaged in activities appropriate to the program's discipline.
Degree Requirements and Curriculum
In addition to the program requirements listed on this page, students must also satisfy requirements outlined in more detail in the Minimum Requirements for Graduation section of this catalog, including:
- 40 units of upper-division courses
- 2.0 GPA
- Graduation Writing Requirement (GWR)
- U.S. Cultural Pluralism (USCP)
| Code | Title | Units |
|---|---|---|
| MAJOR COURSES | ||
| CSC 1001 & CSC 1001L | Fundamentals of Computer Science and Fundamentals of Computer Science Laboratory | 4 |
| CSC 2001 & CSC 2001L | Data Structures and Data Structures Laboratory | 4 |
| DATA 2000 | Data Science Seminar | 1 |
| DATA 3301 | Introduction to Data Science | 4 |
| DATA 4401 | Data Science Process and Ethics | 4 |
| DATA 4460 | Senior Project - Data Science Capstone | 2 |
| DATA/CSC 4610 | Fundamentals of Machine Learning | 4 |
| DATA/CSC 4620 | Foundations and Applications of Deep Learning | 4 |
| MATH 1151 | Linear Algebra | 3 |
| MATH/DATA 1264 | Calculus for Data Science I (2) 1 | 4 |
| MATH/DATA 1265 | Calculus for Data Science II | 4 |
| MATH 2031 | Transition to Advanced Mathematics | 3 |
| MATH/DATA 2621 | Introduction to Mathematical Optimization | 3 |
| STAT 1510 | Statistics I | 3 |
| STAT 2610 | Introduction to Probability and Simulation | 3 |
| STAT 3520 | Statistics II | 3 |
| STAT 3530 | Applied Linear Models | 4 |
| Concentration | ||
| (See list of Concentrations below) | 21-23 | |
| SUPPORT COURSES | ||
| Select from the following: (Upper-Division 3) 1 | 3 | |
| Philosophy of Technology | ||
| Ethics, Science, and Technology | ||
| Robot Ethics | ||
| GENERAL EDUCATION (GE) | ||
| (See GE program requirements below) | 37 | |
| FREE ELECTIVES | ||
| Free Electives 2 | 0-2 | |
| Total Units | 120 | |
- 1
Required in Major or Support; also satisfies General Education (GE) requirement.
- 2
If a General Education (GE) course is used to satisfy a Major or Support requirement, additional units of Free Electives may be needed to complete the total units required for the degree.
Concentrations
Machine Learning Engineering
| Code | Title | Units |
|---|---|---|
| REQUIRED COURSES | ||
| CSC 3449 | Algorithms and Complexity | 4 |
| DATA 3302 | Data Visualization | 4 |
| STAT/DATA 1810 | Introduction to Statistical Computing with R | 3 |
| Select from the following: | 4 | |
| Introduction to Database Management Systems | ||
| Introduction to Databases and Introduction to Non-Relational Database Systems | ||
| Modeling Electives | ||
| Select from the following: 1, 2 | 2-6 | |
| Artificial Intelligence | ||
| Advanced Deep Learning | ||
| Graph Mining | ||
| Differential Equations | ||
| Advanced Linear Algebra | ||
| Differential Equations and Boundary Value Problems | ||
| Mathematics of Data Science | ||
| Introduction to Numerical Analysis | ||
| Numerical Optimization | ||
| Probability Theory | ||
| Multilevel and Mixed Modeling | ||
| Bayesian Reasoning and Methods | ||
| Statistical Analysis of Time Series | ||
| Survival Analysis Methods | ||
| Categorical Data Analysis | ||
| Applied Multivariate Statistics | ||
| Generalized Linear Models | ||
| Statistical Learning with R | ||
| Computing Electives | ||
| Select from the following: 1, 2 | 2-6 | |
| Natural Language Processing | ||
| Artificial Intelligence | ||
| Semantic Computing | ||
| Computer Vision | ||
| Seminars in Artificial Intelligence and Machine Learning 3 | ||
| Research Experience in Artificial Intelligence and Machine Learning 3 | ||
| Projects in Artificial Intelligence and Machine Learning 3 | ||
| Special Advanced Laboratory | ||
| Special Advanced Activity | ||
| Advanced Machine Learning | ||
| Computational Linguistics | ||
| Special Advanced Topics in Artificial Intelligence | ||
| Artificial Intelligence | ||
| Advanced Deep Learning | ||
| Data Science Seminar 3 | ||
| Intermediate Statistical Computing with R | ||
| Total Units | 23 | |
- 1
Courses can only be used once for major degree credit.
- 2
A minimum of 23 units is required to complete the concentration. Unit selection for requirements will vary based on students' selection of modeling electives and computing electives.
- 3
CSC 4891, CSC 4892, CSC 4893 and DATA 4720 can be taken for a combined total of up to 4 units.
Mathematical Foundations
| Code | Title | Units |
|---|---|---|
| REQUIRED COURSES | ||
| MATH 2343 | Differential Equations | 3 |
| MATH 3055 | Graph Theory | 3 |
| MATH 3152 | Advanced Linear Algebra | 4 |
| MATH 3351 | Differential Equations and Boundary Value Problems | 3 |
| MATH/DATA 3622 | Mathematics of Data Science | 3 |
| MATH 3651 | Introduction to Numerical Analysis | 3 |
| MATH 4653 | Numerical Optimization | 3 |
| Total Units | 22 | |
Statistical Modeling
| Code | Title | Units |
|---|---|---|
| REQUIRED COURSES | ||
| STAT/DATA 1810 | Introduction to Statistical Computing with R | 3 |
| STAT/DATA 3820 | Intermediate Statistical Computing with R | 3 |
| Statistical Modeling Electives | ||
| Select from the following: | 15 | |
| Multilevel and Mixed Modeling | ||
| Bayesian Reasoning and Methods | ||
| Statistical Analysis of Time Series | ||
| Survival Analysis Methods | ||
| Categorical Data Analysis | ||
| Applied Multivariate Statistics | ||
| Statistical Learning with R | ||
| Total Units | 21 | |
General Education (GE) Requirements
General Education (GE) Requirements
- 43 units required, 6 of which are specified in Major and/or Support.
- If any of the remaining 37 Units is used to satisfy a Major or Support requirement, additional units of Free Electives may be needed to complete the total units required for the degree.
- See the complete GE course listing.
- A grade of C- or better is required in one course in each of the following GE Areas: 1A (English Composition), 1B (Critical Thinking), 1C (Oral Communication), and 2 (Mathematics and Quantitative Reasoning).
| Lower-Division General Education | ||
| Area 1 | English Communication and Critical Thinking | |
| 1A | Written Communication | 3 |
| 1B | Critical Thinking | 3 |
| 1C | Oral Communication | 3 |
| Area 2 | Mathematics and Quantitative Reasoning | |
| 2 | Mathematics and Quantitative Reasoning (3 units in Major) 1 | 0 |
| Area 3 | Arts and Humanities | |
| 3A | Arts | 3 |
| 3B | Humanities: Literature, Philosophy, Languages other than English | 3 |
| Area 4 | Social and Behavioral Sciences (Area 4 courses must come from at least two different course prefixes.) | |
| 4A | American Institutions (Title 5, Section 40404 Requirement) | 3 |
| 4B | Social and Behavioral Sciences | 3 |
| Area 5 | Physical and Life Sciences | |
| 5A | Physical Sciences | 3 |
| 5B | Life Sciences | 3 |
| 5C | Laboratory (may be embedded in a 5A or 5B course) | 1 |
| Area 6 | Ethnic Studies | |
| 6 | Ethnic Studies | 3 |
| Upper-Division General Education | ||
| Upper-Division 2/5 | Mathematics and Quantitative Reasoning or Physical and Life Sciences | 3 |
| Upper-Division 3 | Arts and Humanities (3 units in Support) 1 | 0 |
| Upper-Division 4 | Social and Behavioral Sciences (Area 4 courses must come from at least two different course prefixes.) | 3 |
| Total Units | 37 | |
- 1
Required in Major or Support; also satisfies General Education (GE) requirement.