Mathematics - Data Science Option, BS
Introduction
Please click here to see Mathematical and Statistical Sciences department information.
These degree requirements are subject to periodic revision by the academic department, and the College of Liberal Arts and Sciences reserves the right to make exceptions and substitutions as judged necessary in individual cases. Therefore, the College strongly urges students to consult regularly with their major advisor and CLAS advisor to confirm the best plans of study before finalizing them.
Program Delivery
- This is an on-campus program.
Declaring This Major
- Click here to go to information about declaring a major.
General Requirements
To earn a degree, students must satisfy all requirements in each of the three areas below, in addition to their individual major requirements.
- CU Denver General Graduation Requirements
- CU Denver Core Curriculum
- College of Liberal Arts & Sciences Graduation Requirements
- Click here for information about Academic Policies
Program Requirements
- Students must complete a total of 67-68 credit hours, from approved courses.
- Students must complete at least 30 upper-division (3000-level and above) credit hours in the major.
- Students must earn a minimum grade of C- (1.7) in all courses that apply to the major and must achieve a minimum cumulative major GPA of 2.25. Courses taken using P+/P/F or S/U grading cannot apply to major requirements.
- Students must complete a minimum of 15 upper-division level MATH credit hours with CU Denver faculty.
Program Restrictions, Allowances and Recommendations
- Students may not use any of the following MATH courses to count toward major requirements:
- MATH 3195 Linear Algebra and Differential Equations
- MATH 3800 Probability and Statistics for Engineers
- MATH 4830 Applied Statistics
NEW COURSE LIST:
| Code | Title | Hours |
|---|---|---|
| Complete the following required courses: | 43-44 | |
| Fundamentals of Computing | ||
| Data Governance and Ethics | ||
| Calculus I | ||
| Calculus II | ||
| Calculus III | ||
| Data Wrangling & Visualization | ||
| Statistical Theory | ||
| Introduction to Probability | ||
| Applied Regression Analysis | ||
| Choose one: | 3 | |
| Foundations of Data Science | ||
| Introductory Statistics | ||
| Choose one: | 3-4 | |
| Applied Linear Algebra | ||
| Linear Algebra and Differential Equations | ||
| Choose one path: | 3 | |
| College Success | ||
| Cultivating Emotional Intelligence | ||
| Career and Professional Development | ||
| OR | ||
| Career and Professional Development | ||
| Choose one (All courses but MATH 4779 requires the approval of the Director of Data Science and an advisor for the courses. CSCI 4840 or 4939 will only be approved for the Computer Science option. Must be taken for 3 credit hours) | 3 | |
| Independent Study | ||
| Internship | ||
| Internship | ||
| Independent Study | ||
| Internship | ||
| Math Clinic | ||
| Independent Study | ||
| Complete the additional required courses: | 15-18 | |
| Options: General, Business, Chemistry, Economics, Geography, or Mathematics | ||
| Introduction to Business | ||
| Business Problem Solving Tools | ||
| Business Data and Database Management | ||
| Machine Learning Methods | ||
| Choose one: | 3 | |
| Business Analytics Process | ||
| Forecasting Techniques | ||
| Statistics for Business Analytics | ||
| Computing for Business Analytics | ||
| Prescriptive Analytics with Optimization | ||
| Causal Analytics | ||
| Evaluative Analytics | ||
| Option: Computer Science | ||
| Object Oriented Programming | ||
| Data Structures and Program Design | ||
| Discrete Structures | ||
| Algorithms | ||
| Database System Concepts | ||
| Data Science | ||
OLD COURSE LIST:
| Code | Title | Hours |
|---|---|---|
| Complete the following program requirements: | 54 | |
| Complete one of the following programming options: | 3-4 | |
| Programming for Data Science | ||
| Fundamentals of Computing and Fundamentals of Computing Laboratory | ||
| Complete all of the following required Mathematics courses: | 33 | |
| Calculus I | ||
| Calculus II | ||
| Calculus III | ||
| Introduction to Abstract Mathematics | ||
| Applied Linear Algebra | ||
| Introduction to Real Analysis I | ||
| Data Wrangling & Visualization | ||
| Statistical Theory | ||
| Applied Regression Analysis | ||
| Math Clinic | ||
| Complete one of the following Machine Learning courses: | 3 | |
| Intro to Statistical and Machine Learning | ||
| Machine Learning Methods | ||
| Complete two MATH elective courses (at least six credit hours) above the 3000 level, excluding MATH 3041, MATH 3195, MATH 3511, MATH 3800, MATH 4015, and MATH 4830. | 6 | |
| Complete 9 additional credit hours (typically 3 courses), countable towards a major in one of the following subjects, at any level: | 9 | |
| Business | ||
| Biology | ||
| Chemistry | ||
| Computer Science | ||
| Economics | ||
| Geography and Environmental Science | ||
| Health and Behavioral Science | ||
| Physics | ||
| Sociology | ||
Business
Biology
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Chemistry
Computer Science
Economics
Geography and Environmental Science
Health and Behavioral Science
Physics
Sociology
Other areas allowable on a case-by-case basis.
To learn more about the Student Learning Outcomes for this program, please visit our website.
To review the Degree Map for this program, please visit our website.