Computational Data Science, B.S.
Visit the Computer Science Department page for more information on the program and access to advising.
Program Description
The Computational Data Science Degree develops strong interdisciplinary skills in mathematics, statistics, computer science and big data processing. Create algorithms, write code and scripts to solve problems beyond the basic use of existing tools in support of an industrial, enterprise-level big data pipeline. The mix of competencies and experiences required for Data Science differs significantly from those developed in the individual degree programs in the four areas mentioned above. Gain real-world experience as a springboard to working in industry as a Data Scientist or to pursue a graduate degree.
Program Requirements
| Code | Title | Credit Hours |
|---|---|---|
| Total Credit Hours | 120 | |
| General Education Requirements | 31 Credits | |
| ENGL 1010 | Introduction to Academic Writing | 3 |
| or ENGH 1005 | Literacies and Composition Across Contexts | |
| ENGL 2010 | Intermediate Academic Writing | 3 |
| MATH 1210 | Calculus I * | 4 |
| American Institutions | 3 | |
| American History (3) | ||
| US Economic History (3) | ||
| US History to 1877 and US History since 1877 (6) | ||
| American Heritage (3) | ||
| American National Government (3) | ||
| Art | 3 | |
| Humanities | 3 | |
| Life Sciences | 3 | |
| Physical Sciences | 3 | |
| Social & Behavioral Sciences | 3 | |
| Personal, Professional, and Civic Growth | 3 | |
| Discipline Requirements | 89 Credits | |
| Minimum grade of C- required in theses courses: | ||
| Complete one of the following: | 6 | |
| Fundamentals of Programming and Object Oriented Programming (6) | ||
| Accelerated Introduction to Programming (undefined) (and an additional 3 credit CS elective not already completed) 1 | ||
| CS 2420 | Introduction to Algorithms and Data Structures | 3 |
| CS 2300 | Discrete Mathematical Structures I | 3 |
| CS 2450 | Software Engineering | 3 |
| CS 2700 | Causal Inference | 3 |
| CS 3100 | Data Privacy and Security | 3 |
| CS 3520 | Database Theory | 3 |
| CS 3270 | Python Software Development | 3 |
| CS 3310 | Analysis of Algorithms | 3 |
| CS 3530 | Data Management For Data Sciences | 3 |
| CS 3810 | Applied Data Science | 3 |
| CS 3820 | Visualization Analytics for Data Science | 3 |
| CS 3050G | Global Social and Ethical Issues in Computing | 3 |
| CS 4700 | Machine Learning I | 3 |
| CS 4710 | Machine Learning II | 3 |
| CS 4800 | Data Science Capstone | 3 |
| MATH 1220 | Calculus II | 4 |
| MATH 2210 | Calculus III | 4 |
| MATH 2270 | Linear Algebra | 3 |
| MATH 3640 | Introduction to Optimization | 3 |
| ECE 3710 | Applied Probability and Statistics for Engineers and Scientists | 3 |
| or STAT 2050 | Introduction to Statistical Methods | |
| Complete 12 credits from any of the following (A minimum grade of C- is required): | 21 | |
4 courses from another discipline, at least 6 hours of which must be 3000 level or higher. Requires department head approval. | ||
Any CS 3000 or 4000 level course not already required | ||
- 1
If students choose CS 1420, please see advisor.
Graduation Requirements
- Completion of a minimum of 120 semester credits, with a minimum of 40 upper-division credits.
- Overall grade point average of 2.0 or above.
- Must have a minimum grade of C- with a combined GPA of 2.5 or higher in all discipline requirements and the General Education requirements that are marked with an *.
- Residency hours - - minimum of 30 credit hours through course attendance at UVU. 10 of these hours must be within the last 45 hours earned. At least 12 of the credit hours earned in residence must be in approved Computational Data Science (CDS) courses.
- All transfer credit must be approved in writing by UVU.
- No more than 80 semester hours and no more than 20 hours in CDS-type courses of transfer credit from a two-year college.
- No more than 30 semester hours may be earned through independent study and/or extension classes.
- Successful completion of at least one Global/Intercultural course. CS 3050G satisfies this requirement.
- Successful completion of at least two Writing Enriched courses.
Graduation Plan
This graduation plan is a sample plan and is intended to be a guide. Your specific plan may differ based on your Math and English placement and/or transfer credits applied. You are encouraged to meet with an advisor and set up an individualized graduation plan in Wolverine Track.
| First Year | ||
|---|---|---|
| Semester 1 | Credit Hours | |
| MATH 1210 | Calculus I | 4 |
| CS 1400 | Fundamentals of Programming | 3 |
| ENGL 1010 or ENGH 1005 | Introduction to Academic Writing or Literacies and Composition Across Contexts | 3 |
| GE | 3 | |
| GE | 3 | |
| Credit Hours | 16 | |
| Semester 2 | ||
| MATH 1220 | Calculus II | 4 |
| CS 1410 | Object Oriented Programming | 3 |
| ENGL 2010 | Intermediate Academic Writing | 3 |
| GE | 3 | |
| GE | 3 | |
| Credit Hours | 16 | |
| Second Year | ||
| Semester 3 | ||
| CS 2300 | Discrete Mathematical Structures I | 3 |
| CS 2420 | Introduction to Algorithms and Data Structures | 3 |
| MATH 2210 | Calculus III | 4 |
| GE | 3 | |
| GE | 3 | |
| Credit Hours | 16 | |
| Semester 4 | ||
| MATH 2270 | Linear Algebra | 3 |
| CS 2450 | Software Engineering | 3 |
| CS 2700 | Causal Inference | 3 |
| GE | 3 | |
| CDS Elective | 3 | |
| Credit Hours | 15 | |
| Third Year | ||
| Semester 5 | ||
| CS 3270 | Python Software Development | 3 |
| CS 3310 | Analysis of Algorithms | 3 |
| CS 3520 | Database Theory | 3 |
| ECE 3710 or STAT 2050 | Applied Probability and Statistics for Engineers and Scientists or Introduction to Statistical Methods | 3 |
| CDS Elective | 3 | |
| Credit Hours | 15 | |
| Semester 6 | ||
| CS 3100 | Data Privacy and Security | 3 |
| CS 3530 | Data Management For Data Sciences | 3 |
| MATH 3640 | Introduction to Optimization | 3 |
| CDS Elective | 3 | |
| Credit Hours | 12 | |
| Fourth Year | ||
| Semester 7 | ||
| CS 3810 | Applied Data Science | 3 |
| CS 3820 | Visualization Analytics for Data Science | 3 |
| CS 4700 | Machine Learning I | 3 |
| CDS Elective | 3 | |
| CDS Elective | 3 | |
| Credit Hours | 15 | |
| Semester 8 | ||
| CS 4800 | Data Science Capstone | 3 |
| CS 4710 | Machine Learning II | 3 |
| CS 3050G | Global Social and Ethical Issues in Computing | 3 |
| CDS Elective | 3 | |
| CDS Elective | 3 | |
| Credit Hours | 15 | |
| Total Credit Hours | 120 | |
Program Learning Outcomes
- Analyze a complex computing problem and apply principles of computing and other relevant disciplines to identify solutions.
- Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline.
- Communicate effectively in a variety of professional contexts.
- Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles.
- Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline.
- Apply theory, techniques, and tools throughout the data analysis lifecycle and employ the resulting knowledge to satisfy stakeholders’ needs.
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