Mission
The mission of the Computer Science in Data Analytics (CSDA) Department is to prepare students to become effective, ethical, and collaborative professionals in the fields of data analytics and data science.
Additional Learning Opportunities
Math, Science & Subject Tutoring
Tutoring support is available year-round to help students succeed in math, science, and a variety of other subjects. The University Tutoring Center offers assistance in all math and science courses, as well as in select courses across disciplines such as accounting, animation, architecture, interdisciplinary studies, and psychology. (Course availability may vary by semester.)
Students can schedule appointments by visiting the Math, Science & Subject Tutoring Center link located under the “Students” menu on the Woodbury University homepage.
Capstone Courses
In their senior year, all CSDA students are required to complete a personal data analytics project as part of CSDA 480: Senior Project. With instructor approval, students may work in collaborative teams, provided each student assumes a leadership role in a distinct and creative aspect of the project.
The capstone project serves as a culminating experience, demonstrating the student’s proficiency in programming, data analysis, and problem-solving. It represents a key component of the student’s professional portfolio and is expected to be of presentation-ready quality. Students are strongly encouraged to submit their completed projects to relevant computer science and data analytics conferences.
Technology and Computer Requirements
Computer Literacy Requirements
The CSDA Department requires all graduates to demonstrate proficiency in current digital tools for representation, communication, and research. These competencies are reflected in the following areas:
- Computer Systems Proficiency: Ability to operate, manage, and troubleshoot computer systems, including upgrades and communication tools; familiarity with multiple platforms available in Woodbury’s IT labs.
- Internet Research Skills: Successful completion of LSCI 105: Information Theory and Practice, or an approved equivalent, demonstrating effective online research techniques. Students are expected to properly cite all database and web-based sources for text and images in their coursework.
- Word Processing and Document Formatting: Proficiency in creating professional documents, including formatting, image integration, and color management for print.
Media and digital literacy are integrated throughout the CSDA curriculum, and students are expected to demonstrate these skills through the successful completion of their coursework.
Computer Science Data Analytics Program System Requirements
Students in the CSDA program are expected to have access to a personal computer capable of supporting industry-standard tools and software used throughout the curriculum. The following are the minimum system requirements by platform:
Windows
- Model: x86 (32-bit) or x86_64 (64-bit) compatible desktop or laptop
- Memory: Minimum 8 GB RAM (12 GB recommended)
- Operating System: Windows 11 or newer (32-bit or 64-bit)
macOS
- Model: 64-bit Intel-based or Apple Silicon (M1/M2) Macs
- Memory: Minimum 8 GB RAM (12 GB recommended)
- Operating System: macOS 15 Sequoia or newer
Linux
- Distribution: Ubuntu (Laptop/Desktop Edition) or equivalent
- Processor: Dual-core 2.0 GHz or higher
- Memory: Minimum 8 GB RAM
Students should ensure their systems support software commonly used in the program, such as Python, R, SQL, and relevant data visualization or machine learning tools. A stable internet connection and sufficient storage capacity are also essential for coursework and project work.
Program Learning Outcomes
Through coursework, collaborative projects, internships, and hands-on learning, students in the Computer Science in Data Analytics (CSDA) program develop a comprehensive foundation across computer science, mathematics, business, and communication. This interdisciplinary preparation equips students with in-demand technical and professional skills essential to success in the fast-paced data analytics and data science industry.
Graduates will be prepared to apply their expertise across diverse industries, demonstrating strengths in both core competencies—such as programming, statistics, machine learning, data wrangling, and visualization—and key soft skills, including communication, collaboration, and ethical reasoning.
Upon successful completion of the program, students will be able to:
- Problem Solving in Computer Science and Data Analysis: Apply computer science principles and statistical modeling to address data-intensive challenges both independently and collaboratively.
- Programming and Software Development: Design and implement data-driven solutions using software engineering practices and machine learning techniques, while ensuring data privacy and security.
- Career Preparation: Explore and pursue diverse career paths and advanced study opportunities in computer science, data analytics, and related fields.
- Communication: Effectively communicate technical concepts in computer science and data analytics through visual, symbolic, and narrative formats, with proper citation and respect for data ownership.
- Professional and Ethical Responsibility: Identify, evaluate, and respond to ethical issues in data analytics, with attention to transparency, reproducibility, and professional standards.
- Theoretical and Historical Context: Analyze and contextualize the foundational theories and historical development of the data analytics discipline through critical review of relevant literature.
Assessment Process
Placement Exam Requirements
Students in the Computer Science in Data Analytics (CSDA) program who have not received transfer credit for college-level Algebra and Trigonometry, or Pre-Calculus, are required to take the Math Placement Exam.
For details about placement policies and exam procedures, refer to the Academic Proficiencies and Placement section in the Academic Journey chapter of this catalog.
Program Specific Academic Standards
In addition to the University Academic Standards outlined in the Academic Journey section of this catalog, students in the CSDA program must earn a grade of “C” or higher in all core CSDA courses to progress through the curriculum.
Curriculum Summary
Program Major Curriculum
| Unit Type (UT) |
Number of Units (U) |
| Major (MA) |
64 |
| General Education (GE) |
46 |
| Unrestricted Design Elective (DE) |
N/A |
| Unrestricted Electives (UE) |
9 |
| Internship (IN) |
5 |
| Minimum Total Units Required |
124 |
Suggested Sequence of Courses
First Year
Fall Semester
| CORE 101 | Computer Science I | 3 |
LSCI ___
| Information Sources | 1 |
| MATH 226 | Business Statistics | 3 |
| MATH 260 | Analytic Geometry and Calculus I | 5 |
| WRIT 113 | First-Year Academic Writing | 3 |
| Total Credit Hours: | 15 |
Spring Semester
| MDST 120 | Public Speaking | 3 |
| CORE 102 | Computer Science II | 3 |
| ENVT 220 | Environmental Studies | 3 |
INDS ___
| Interdisciplinary Core Elective | 3 |
| MATH 261 | Analytic Geometry and Calculus II | 5 |
| Total Credit Hours: | 17 |
Second Year
Fall Semester
| CORE 201 | Data Structures and Algorithms | 3 |
INDS ___
| Interdisciplinary Core Elective | 3 |
| MATH 262 | Linear Algebra | 3 |
____ ___
| Art History Elective | 3 |
____ ___
| Social Science Elective | 3 |
| Total Credit Hours: | 15 |
Type:
CORE 201: MA.
INDS (Interdisciplinary Core Elective), MATH 262, Art History Elective, and Social Science Elective: GE.
Spring Semester
Third Year
Fall Semester
____ 3__
| Upper Division Art History Elective | 3 |
| CSDA 205 | Windows-Based Application Development | 3 |
| CSDA 320 | Advanced Data Structures and Algorithms | 3 |
| PHIL 210 | Ethical Systems | 3 |
| MATH 310 | Probability and Statistics I | 3 |
| Total Credit Hours: | 15 |
Spring Semester
| CORE 301 | Applied Artificial Intelligence | 3 |
____ 3__
| Upper Division Interdisciplinary Elective | 3 |
| MATH 311 | Probability and Statistics II | 3 |
____ 3__
| Upper Division General Education Elective | 3 |
____ ___
| Unrestricted Elective | 3 |
| Total Credit Hours: | 15 |
Type:
CORE 301, MATH 311, and Upper Division General Education Elective: MA.
Upper Division Interdisciplinary Elective: GE.
Unrestricted Elective: UE.
Fourth Year
Fall Semester
| CSDA 400 | ADVANCED DATABASE DEVELOPMENT | 3 |
| CSDA 415 | MACHINE LEARNING | 3 |
| MATH 312 | Applied Statistical Analysis | 3 |
____ 3__
| Upper Division General Education Elective | 3 |
____ 3__
| Upper Division Social Science Elective | 3 |
| Total Credit Hours: | 15 |
Type:
CSDA 400, CSDA 415, and MATH 312: MA.
Upper Division General Education Elective and Upper Division Social Science Elective: GE.
Spring Semester
| CSDA 410 | DATA MINING | 3 |
| CSDA 480 | SENIOR PROJECT | 3 |
| CSDA 490 | INTERNSHIP | 5 |
____ ___
| Unrestricted Elective | 3 |
____ ___
| Unrestricted Elective | 3 |
| Total Credit Hours: | 17 |
Program Minor Curriculum
Students must complete a total of 18 units from the courses listed below to earn a Minor in Computer Science and Data Analytics.
• Required Courses (12 units):
CORE 101, CORE 102, CORE 201, and MATH 226
• Elective Courses (at least 6 units):
CORE 301, CSDA 205, CSDA 209, CSDA 210, CSDA 410, CSDA 415, CSDA 480, CSDA 490,
MATH 252, MATH 260, MATH 261, MATH 262, MATH 310, MATH 311, and MATH 312
Students must complete at least 6 units from the elective courses in addition to the required courses to fulfill the minor requirements.