Data Science, Certificate of Achievement (C)
The Data Science Certificate of Achievement will prepare students for today’s data-driven world. The program is designed to enhance career opportunities in data science areas, including data and system analysis, data science research, business analytics, data engineering, database administration, statistical assistance, software engineering, and management. The core sequence covers data science foundational concepts, core programming practices, database management and an introduction to version control software. Skills mastered in this sequence of courses can be a first step toward a career in data science.
Certificate of Achievement Requirements
Complete all Department Requirements for the Certificate of Achievement with a cumulative grade point average (GPA) of 2.0 or better. Candidates for a Certificate of Achievement are required to complete at least 20% of the department requirements through SBCC.
Code | Title | Units |
---|---|---|
Department Requirements | ||
Core Courses | ||
CIS 107 | Introduction to Database Systems | 2-4 |
or CIS 117 | Introduction to SQL Programming | |
CS 106 | Theory and Practice II | 3 |
or CS 114 | Intermediate Python | |
CS/MATH 118 | Data Science for All | 4 |
CS 134 | Version Control with Git | 2.5 |
Complete 3 courses from the following (not used to satisfy the Core Courses above) | 8-14 | |
Communication Research Methods | ||
Introduction to Programming | ||
Theory and Practice I | ||
Theory and Practice II | ||
Discrete Structures | ||
Intermediate Python | ||
Introduction to Programming for Engineers | ||
C Programming | ||
Object-Oriented Programming Using C++ | ||
Introduction To Geographic Information Systems And Maps | ||
Elementary Statistics | ||
or PSY 150 | Statistics for the Behavioral Sciences | |
or SOC 125 | Introduction to Statistics in Sociology | |
Calculus with Analytic Geometry I | ||
Calculus with Analytic Geometry II | ||
Transition to Advanced Mathematics | ||
Multivariable Calculus | ||
Linear Algebra | ||
Differential Equations | ||
Research Methods and Experimental Design in Psychology | ||
Introduction To Social Research | ||
Total Units | 19.50-27.50 |
- 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.
- Design, create, query, and manage databases for analytic processing using SQL.
- Understand and employ proper version-control configuration and operations using a version control system such as Git.
- Understand limitations and issues surrounding data analysis in terms of bias, ethics, establishing causality and privacy.