Data Science, Associate of Science (AS)
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Program Overview
Data Science, Associate of Science (AS)
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.
Requirements
- Complete all of the following:
- All Department Requirements listed below with a “C” or better or “P” in each course (at least 20% of the department requirements must be completed through SBCC).
- One of the following three General Education options:
- OPTION 1: A minimum of 18 units of SBCC General Education Requirements (Areas A-D) and Institutional Requirements (Area E) and Information Competency Requirement (Area F) OR
- OPTION 2: IGETC Pattern OR
- OPTION 3: CSU GE Breadth Pattern
- A total of 60 degree-applicable units (SBCC courses numbered 100 and higher).
- Maintain a cumulative GPA of 2.0 or better in all units attempted at SBCC.
- Maintain a cumulative GPA of 2.0 or better in all college units attempted.
- A minimum of 12 units through SBCC.
Course List
| Code | Title | Units |
|---|---|---|
| CS 106 | Theory and Practice II | 3 |
| or CS 114 | Intermediate Python | |
| CS/MATH 118 | Data Science for All | 4 |
| MATH 150 | Calculus with Analytic Geometry I | 5 |
| MATH 160 | Calculus with Analytic Geometry II | 5 |
| Complete 3 courses from the following (not used to satisfy the Core Courses above) | 6.5-12 | |
| CIS 107 | Introduction to Database Systems | |
| CIS 117 | Introduction to SQL Programming | |
| COMM 288 | Communication Research Methods | |
| CS 104 | Introduction to Programming | |
| CS 105 | Theory and Practice I | |
| CS 106 | Theory and Practice II | |
| CS 108 | Discrete Structures | |
| CS 114 | Intermediate Python | |
| CS 133 | Introduction to Programming for Engineers | |
| CS 134 | Version Control with Git | |
| CS 137 | C Programming | |
| CS 140 | Object-Oriented Programming Using C++ | |
| ERTH/GEOG 171 | Introduction To Geographic Information Systems And Maps | |
| MATH 117 | Elementary Statistics | |
| or PSY 150 | Statistics for the Behavioral Sciences | |
| or SOC 125 | Introduction to Statistics in Sociology | |
| 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 | |
| Total Units | 23.50-29.00 |
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.
- Understand limitations and issues surrounding data analysis in terms of bias, ethics, establishing causality and privacy.