Data Science
Associate of Science (AS) Computer Science · Santa Barbara City College · 2026-2027 Catalog
Overview
The Associate in Science in Data Science degree has two primary goals. First, it prepares students for an efficient transfer to a four-year institution for a bachelor’s degree. Second, it teaches new and returning students skills that are immediately valuable in the marketplace. The program is designed to provide a combination of core computing and statistical inference skills using data sets from a variety of disciplines. The core sequence covers data science foundational concepts, core programming practices and mathematical principles used in data science careers. Students can choose to focus on specialized areas including data structures and algorithms, advanced mathematics, database systems, geographical information systems, and research methods.
Students learn a combination of computing and statistical inference skills using data sets from a variety of disciplines. The core sequence covers data science foundational concepts, core programming practices and mathematical principles used in data science careers. Specialized areas including data structures and algorithms, advanced mathematics, database systems, geographical information systems, and research methods.
Graduates of the data science major pursue a wide variety of careers, including data and system analysis, data science research, business analytics, data engineering, database administration, statistical assistance, software engineering, and management. Many take initial positions with large corporations, and others with small, newly established firms. Some become entrepreneurs and consultants.
Requirements
Associate Degree Graduation Requirements
Complete all of the following:
- All Major Requirements listed below with a “C” or better or “P” in each course (at least 20% of the major requirements must be completed through SBCC).
- One of the following General Education options:
- OPTION 1: SBCC General Education pattern OR
- OPTION 2: Cal-GETC pattern
- A minimum of 60 semester units of degree-applicable college credit (SBCC courses numbered 100 and higher and C1000 and higher).
- Maintain a cumulative GPA of 2.0 or higher in degree-applicable coursework.
- A minimum of 12 units through SBCC.
| Course | Title | Units |
|---|---|---|
| Major Requirements | ||
| Required Core | ||
| CS 104 | Introduction to Programming | 3 |
| orCS 105 | Programming Methodology | |
| CS 106 | Data Structures and Algorithms | 3 |
| orCS 114 | Intermediate Python | |
| CS 118/MATH 118 | Data Science for All | 4 |
| MATH 150 | Calculus with Analytic Geometry I | 5 |
| MATH 160 | Calculus with Analytic Geometry II | 5 |
| List A - Complete 2 courses from the following (not used to satisfy the Required Core above) | 4-8 | |
| CIS 107 | Introduction to Database Systems | |
| CIS 117 | Introduction to SQL Programming | |
| COMM 288 | Communication Research Methods | |
| CS 104 | Introduction to Programming | |
| CS 105 | Programming Methodology | |
| CS 106 | Data Structures and Algorithms | |
| CS 108 | Discrete Structures | |
| CS 114 | Intermediate Python | |
| CS 133 | Programming for Engineers | |
| CS 134 | Version Control with Git | |
| CS 137 | C Programming | |
| CS 140 | Object-Oriented Programming Using C++ | |
| ERTH 171/GEOG 171 | Introduction To Geographic Information Systems And Maps | |
| MATH 180 | Transition to Advanced Mathematics | |
| MATH 200 | Multivariable Calculus | |
| MATH 210 | Linear Algebra | |
| MATH 220 | Differential Equations | |
| PSY 200 | Research Methods and Experimental Design in Psychology | |
| SOC 115 | Introduction To Social Research | |
| STAT C1000 | Introduction to Statistics | |
| orPSY 150 | Statistics for the Behavioral Sciences | |
| orSOC 125 | Introduction to Statistics in Sociology | |
| Total Units | 24.00-28.00 | |
Learning Outcomes
- Apply foundational data science concepts including computing summary statistics, creating data visualizations, simulating experiments and probability concepts.
- Use foundational programming concepts to explore and analyze real-world datasets using problem decomposition, and code design strategies.
- Write software that can organize data into data structures used in major commercial applications.
- Use techniques of calculus and numerical methods to analyze curves and make error estimations.
- Describe limitations and issues surrounding data analysis in terms of bias, ethics, establishing causality and privacy.
Recommended Sequence
Make an appointment with your SBCC academic counselor through Starfish to create a Student Education Plan that reflects a recommended course sequence for this program that is tailored to your individual needs.
How to schedule an Academic Counseling appointment.