Mission
The mission of the Computer Science in Data Analytics (CSDA) Department is to transform our students into effective, ethical, and collaborative data analytics/ science professionals.
Additional Learning Opportunities
Math, Science & Subject Tutoring
Tutoring for math, science, and other subjects is available throughout the school year. Tutoring assistance in all math and science courses—as well as for other available courses in accounting, animation, architecture, interdisciplinary studies, and psychology—may be found at the University Tutoring Center (available courses change each semester).
Make appointments by visiting the Math, Science & Subject Tutoring Center link under the "Students" menu on the Woodbury University home page.
Capstone Courses
As a senior, each CSDA student must complete a personal data analytics project as part of their CSDA 480, Senior Project course. Students may choose to work in collaborative teams with the permission of the course instructor, provided each student takes a leadership role in some creative aspect of the project.
This capstone project demonstrates the student’s mastery of programming languages and data analytics and constitutes the central work in their professional portfolio. Completed capstone projects are expected to be of presentation-level quality, and all students are encouraged to enter their projects into appropriate computer science conferences.
Technology and Computer Requirements
Computer Literacy Requirements
The CSDA Department requires its graduates to be literate in the current digital media of representation and communication, as demonstrated by the following:
- Proficiency in computer systems operations, including communication, upgrades, and management; familiarity with the multiple platforms available in Woodbury IT labs.
- Proficiency in internet research, through successful completion of LSCI 105, Information Theory and Practice, or an appropriate equivalent. Bibliographic documentation of database and web-based sources of all text and images is required in all Animation courses.
- Proficiency in word processing and document formatting, including image and color management for printing.
Media literacy is embedded in the curriculum at all levels and CSDA students are expected to demonstrate these proficiencies through successful completion of their coursework.
Computer Science Data Analytics Program System Requirements
Windows
You can use PCs and laptops that use a supported Microsoft Windows operating system.
Model
Standard x86 (32-bit) or x86 (64-bit) compatible desktop or laptop computer
Memory
At least 1GB of RAM
Operating Systems
The following operating systems are supported:
- Windows 10, 32- or 64-bit versions
Macintosh
The Mac must meet the following system requirements:
Model
64-bit Intel-based model
Memory
At least 2GB of RAM
Operating Systems
- Mac OS X Mavericks (10.9.x)
- Mac OS X Yosemite (10.10x)
- Mac OS X El Capitan (10.11)
- Mac OS Sierra (10.12)
Linux/Unix
The recommended minimum system requirements listed here should allow even someone fairly new to installing Ubuntu or GNU Linux to easily install a usable system with enough room to be comfortable.
- Ubuntu Laptop/Desktop Edition
- GHz dual core processor
- At least 1 GB RAM (system memory)
Program Learning Outcomes
Through collaborations with other students, internships, classroom teaching, and hands-on experience, students will immerse themselves in the fields of computer science, mathematics, business, and communications, and build a comprehensive skill set in data analytics.
This deep set of core competencies in multiple areas—programming, statistics, data analytics, machine learning, data wrangling, data visualization, communication, and ethics—will increase students' marketability in the fast-paced data analytics/science industry. With a working knowledge of these in-demand technical skills, as well as the soft skills employers seek, students will graduate prepared to apply their data analytics/science expertise to a wide range of industries.
- Problem Solving in Computer Science and Data Analysis: Apply computer science and statistical modeling for data-intensive problem solving and scientific discovery as individuals and in collaboration with others.
- Programming: Use software engineering and machine learning to design and implement data-driven solutions to real-world problems. Preserve security and sensitivity of data.
- Career: Explore careers and advanced studies in a wide range of computer science and data analytics.
- Communication: Develop, articulate, and present concepts of computer science and data analytics visually, symbolically, and narratively. Apply citation and data ownership.
- Professional and Ethical Responsibility: Identify and describe the ethical issues in a problematic situation. Apply professional ethics related to transparency and reproducibility.
- Theories and History of Data Analytics Discipline: Review the literature related to data analytics theories and history.
Assessment Process
Placement Exam Requirements
Computer Science in Data Analytics students who have not received transfer credit for college- level algebra and trigonometry, or college-level pre- calculus, are required to take the Math Placement Exam. See the Academic Proficiencies and Placement section of the Academic Journey chapter of this catalog for more information.
Program Specific Academic Standards
In addition to the University Academic Standards as detailed in the Academic Journey section of this catalog, students are expected to earn a "C" or better in core CSDA courses to advance through the curricula.
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 18 units consisting of the courses listed below.
CORE 101, CORE 102, CORE 201, and MATH 226: These courses are required to earn a minor in Computer Science and Data Analytics.
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 take at least 6 units of these courses to earn a minor in Computer Science and Data Analytics.