Computational Data Science, B.S.
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 | 121 | |
| General Education Requirements | 35 Credits | |
| ENGL 1010 | Introduction to Academic Writing CC | 3 |
| or ENGH 1005 | Literacies and Composition Across Contexts CC | |
| ENGL 2010 | Intermediate Academic Writing CC | 3 |
| MATH 1210 | Calculus I QL * | 4 |
| American Institutions: Complete one of the following: | 3 | |
| American Civilization AS (3) | ||
| US Economic History AS (3) | ||
| US History to 1877 AS and US History since 1877 AS (6) | ||
| American Heritage AS (3) | ||
| American National Government AS (3) | ||
| PHIL 2050 | Ethics and Values IH | 3 |
| Complete the following: | ||
| HLTH 1100 | Personal Health and Wellness TE | 2 |
| or EXSC 1097 | Fitness for Life TE | |
| Distribution Courses: | ||
| COMM 1020 | Public Speaking HH * | 3 |
| COMM 2110 | Interpersonal Communication SS * | 3 |
| Biology (Choose from list) | 3 | |
| Fine Arts Distribution (Choose from list) | 3 | |
| PHYS 2210 & PHYS 2215 | Physics for Scientists and Engineers I PP and Physics for Scientists and Engineers I Lab * | 5 |
| Discipline Requirements | 86 Credits | |
| Complete one of the following GE course/lab combinations: | 5 | |
| College Biology I BB and College Biology I Laboratory (5) | ||
| Principles of Chemistry I PP and Principles of Chemistry I Laboratory (5) | ||
| College Physics II PP and College Physics II Lab (5) | ||
| Physics for Scientists and Engineers II PP and Physics for Scientists and Engineers II Lab (5) | ||
| Minimum grade of C- required in theses courses: | ||
| Computer Science | ||
| 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 WE | 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 3800 | Data Science Through Statistical Reasoning | 3 |
| CS 3810 | Applied Data Science | 3 |
| CS 3820 | Visualization Analytics for Data Science | 3 |
| CS 305G | Global Social and Ethical Issues in Computing GI WE | 3 |
| CS 4700 | Machine Learning I | 3 |
| CS 4710 | Machine Learning II | 3 |
| CS 4800 | Data Science Capstone WE | 3 |
| Mathematics | ||
| MATH 1220 | Calculus II | 4 |
| MATH 2210 | Calculus III | 4 |
| MATH 2270 | Linear Algebra | 3 |
| Statistics | ||
| STAT 2050 | Introduction to Statistical Methods | 4 |
| Elective Requirements: | ||
| Complete 12 credits from any of the following (A minimum grade of C- is required): | 12 | |
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 121 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 305G 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 QL | 4 |
| CS 1400 | Fundamentals of Programming | 3 |
| STAT 2050 | Introduction to Statistical Methods | 4 |
| ENGL 1010 or ENGH 1005 | Introduction to Academic Writing CC or Literacies and Composition Across Contexts CC | 3 |
| Credit Hours | 14 | |
| Semester 2 | ||
| MATH 1220 | Calculus II | 4 |
| CS 1410 | Object Oriented Programming | 3 |
| PHYS 2210 | Physics for Scientists and Engineers I PP | 4 |
| PHYS 2215 | Physics for Scientists and Engineers I Lab | 1 |
| ENGL 2010 | Intermediate Academic Writing CC | 3 |
| Credit Hours | 15 | |
| 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 |
| Biology Distribution | 3 | |
| American Institutions | 3 | |
| Credit Hours | 16 | |
| Semester 4 | ||
| MATH 2270 | Linear Algebra | 3 |
| CS 2450 | Software Engineering WE | 3 |
| CS 2700 | Causal Inference | 3 |
| HLTH 1100 or EXSC 1097 | Personal Health and Wellness TE or Fitness for Life TE | 2 |
| Third Science Distribution | 5 | |
| Credit Hours | 16 | |
| Third Year | ||
| Semester 5 | ||
| CS 3270 | Python Software Development | 3 |
| CS 3310 | Analysis of Algorithms | 3 |
| CS 3520 | Database Theory | 3 |
| COMM 2110 | Interpersonal Communication SS | 3 |
| CDS Elective | 3 | |
| Credit Hours | 15 | |
| Semester 6 | ||
| CS 3530 | Data Management For Data Sciences | 3 |
| CS 3800 | Data Science Through Statistical Reasoning | 3 |
| CS 3820 | Visualization Analytics for Data Science | 3 |
| Fine Arts Distribution | 3 | |
| CDS Elective | 3 | |
| Credit Hours | 15 | |
| Fourth Year | ||
| Semester 7 | ||
| CS 3100 | Data Privacy and Security | 3 |
| CS 3810 | Applied Data Science | 3 |
| CS 4700 | Machine Learning I | 3 |
| PHIL 2050 or PHIL 205G | Ethics and Values IH or Ethics and Values IH GI | 3 |
| CDS Elective | 3 | |
| Credit Hours | 15 | |
| Semester 8 | ||
| CS 4800 | Data Science Capstone WE | 3 |
| CS 4710 | Machine Learning II | 3 |
| CS 305G | Global Social and Ethical Issues in Computing GI WE | 3 |
| COMM 1020 | Public Speaking HH | 3 |
| CDS Elective | 3 | |
| Credit Hours | 15 | |
| Total Credit Hours | 121 | |
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.