Data Science, BS
Program Description
The Bachelor of Science in Data Science combines the computing, mathematical, and statistical skills necessary for modern fundamental data-oriented tasks including data processing, analysis, and presentation. Students will engage in data-driven decision making across various interdisciplinary contexts using computationally intensive approaches. After building a strong core of computing fundamentals including knowledge of data structures and algorithms, students will learn to build custom solutions to solve complex problems using skills such as: data acquisition, management, and governance; probability, statistics, modeling, and machine learning; as well as software construction and data visualization.
Admission Requirements
The admissions process works as follows:
- Student applies and is accepted to Utah Tech
- Student's major is designated as Associate of Programming
- Student completes the requirements in the Associate of Programming with a flat C or higher
- Student meets with the Computing Advisor to ensure that required courses are complete and to finalize an academic plan
- Student's major is switched from Associate of Programming to Data Science
Program Curriculum
120 credits
Utah Tech General Education Requirements
All Utah Tech General Education requirements must be fulfilled. A previously earned degree may fulfill those requirements, but courses must be equivalent to Utah Tech's minimum General Education standards in American Institutions, English, and Mathematics.
General Education Core Requirements
| Code | Title | Hours |
|---|---|---|
| English | 3-7 | |
| Mathematics | 3-5 | |
| American Institutions | 3-6 | |
| Life Sciences | 3-10 | |
| Physical Sciences | 3-5 | |
| Fine Arts | 3 | |
| Literature/Humanities | 3 | |
| Social & Behavioral Sciences | 3 | |
| Code | Title | Hours |
|---|---|---|
| Computing Core Requirements | ||
| CS 1400 | Fundamentals of Programming 1 | 3 |
| CS 1410 | Object Oriented Programming 1 | 3 |
| CS 2100 | Discrete Structures 1 | 3 |
| CS 2420 | Introduction to Algorithms and Data Structures 1 | 3 |
| CS 2810 | Computer Organization and Architecture | 3 |
| CS 3510 | Algorithms 1 | 3 |
| IT 1500 | Cloud Fundamentals 1 | 1 |
| SET 1000 | Graduation Planning & Career Prep I | 0 |
| Data Science Core Requirements | ||
| CS 2500 | Data Wrangling | 3 |
| CS 4400 | Data Mining | 3 |
| CS 4410 | Data Visualization | 3 |
| CS 4420 | Data Privacy, Security, and Ethics | 3 |
| CS 4480R | Data Science Practicum (To be taken twice (1 credit each) to fulfill Data Science Core requirement.) | 2 |
| CS 4490R | Data Science Capstone (To be taken twice (3 credits each) to fulfill Data Science Core requirement.) | 6 |
| Math Core Requirements | ||
| MATH 1210 | Calculus I (MA) 1 | 4 |
| MATH 1220 | Calculus II (MA) 1 | 4 |
| MATH 2270 | Linear Algebra 1 | 3 |
| MATH 3400 | Probability & Statistics | 3 |
Data Science Tracks: Complete two tracks from the following options:1
| Code | Title | Hours |
|---|---|---|
| AI/ML Track | ||
| Complete all courses from the following list: | ||
| CS 3005 | Programming in C++ 1 | 3 |
| CS 4300 | Artificial Intelligence 1 | 3 |
| CS 4320 | Machine Learning 1 | 3 |
| Code | Title | Hours |
|---|---|---|
| Data Engineering Track | ||
| Complete all courses from the following list: | ||
| CS 3150 | Computer Networks 1 | 3 |
| CS 3410 | Distributed Systems 1 | 3 |
| CS 4307 | Database Systems 1 | 3 |
| Code | Title | Hours |
|---|---|---|
| Software Engineering Track | ||
| Complete all courses from the following list: | ||
| CS 2450 | Software Engineering 1 | 3 |
| SE 3100 | Software Practices 1 | 3 |
| SE 3150 | Software Quality 1 | 3 |
Data Science Electives:1
| Code | Title | Hours |
|---|---|---|
| Complete 12 credits from the following: | 12 | |
| ACCT 2010 | Principles of Accounting I | 3 |
| BIOL 3300 | Introduction to Bioinformatics | 3 |
| CHEM 3000 | Quantitative Chemical Analysis | 3 |
| CHEM 3005 | Quantitative Chemical Analysis Laboratory 1 | 1 |
| CS 2450 | Software Engineering 1 | 3 |
| CS 3005 | Programming in C++ 1 | 3 |
| CS 3150 | Computer Networks 1 | 3 |
| CS 3400 | Operating Systems 1 | 3 |
| CS 3410 | Distributed Systems 1 | 3 |
| CS 4300 | Artificial Intelligence 1 | 3 |
| CS 4307 | Database Systems 1 | 3 |
| CS 4320 | Machine Learning 1 | 3 |
| CS 4480R | Data Science Practicum (Up to 4 credits can be applied to elective once 2 credits of core is met) | 4 |
| ECON 2010 | Micro Economics (SS, GC) | 3 |
| ECON 3010 | Managerial Economics | 3 |
| HLTH 4010 | Biostatistics & Epidemiology | 3 |
| MATH 2210 | Multivariable Calculus (MA) 1 | 4 |
| MATH 2280 | Ordinary Differential Equations 1 | 3 |
| MATH 3050 | Stochastic Modeling and Applications 1 | 3 |
| MATH 3450 | Advanced Statistical Learning | 3 |
| MATH 3500 | Numerical Analysis | 3 |
| MATH 3700 | Mathematical Modeling | 3 |
| MATH 4800 | Industrial Careers in Mathematics | 3 |
| PSY 3000 | Statistical Methods/Psychology | 4 |
| RSM 3210 | Sports Information Strategies 1 | 3 |
| RSM 4100 | Financial Management in Recreation and Sport 1 | 3 |
| SE 3010 | Mobile Application Development 1 | 3 |
| SE 3100 | Software Practices 1 | 3 |
| SE 3150 | Software Quality 1 | 3 |
| SOC 3112 | Social Statistics | 3 |
1. COURSE FEE REQUIRED. See course fee tab for details.
2A course may only be used to fulfill one program requirement. Dual-listed courses may only be used once to fill requirements. Consult course descriptions in the current catalog to verify dual-listed courses.
Graduation Requirements
- Complete a minimum of 120 college-level credits (1000 and above).
- Complete at least 40 upper-division credits (3000 and above).
- Complete at least 30 upper-division credits at Utah Tech for institutional residency.
- Cumulative GPA 2.0 or higher.
- Grade C or higher in each Core Requirement, Track Requirement, and Elective Requirement course.