Data Sciences Minor
Introduction
Please click here to see Mathematical and Statistical department information.
The demand for employees trained in data science has grown considerably in recent years. This minor will serve students by offering them specific training in data science.
Data science training should include components related to statistics, computing, and preferably, a specific field of application (e.g., business, biology, health, etc.). The minor is flexible in that it allows a student to get core training in data science programming and statistics, while allowing students to develop additional data science-related skills from other disciplines, or to focus on specific skills within data science.
These program 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, minor and CLAS advisors to confirm the best plans of study before finalizing them.
Program Delivery
- This is an on-campus program.
Declaring This Minor
- Please see your CLAS advisor.
- Click here to go to information about declaring a major/minor.
General Requirements
Students must satisfy all requirements as outlined below and by the department offering the minor.
- Click here for information about Academic Policies
Program Requirements
- Students must complete a minimum of 18 credit hours, including a minimum of 9 MATH credit hours.
- Students must complete a minimum of 12 upper-division (3000-level and above) credit hours, including a minimum of six upper-division MATH credits. Most upper-division courses have lower-division pre-requisites.
- Students must earn a minimum grade of C- (1.7) in all courses that apply to the minor and must achieve a minimum cumulative minor GPA of 2.0. Courses taken using P+/P/F or S/U grading cannot apply to minor requirements.
- Students must complete a minimum of six upper-division level MATH credit hours with CU Denver faculty.
Program Restrictions, Allowances and Recommendations
- Be aware of no co-credit policies. Here is a non-exclusive list of our most common no co-credit policies: no co-credit between:
- MATH 3800 Probability and Statistics for Engineers and MATH 3810 Introduction to Probability,
- MATH 3195 Linear Algebra and Differential Equations and MATH 3200 Elementary Differential Equations,
- MATH 3191 Applied Linear Algebra and MATH 3195 Linear Algebra and Differential Equations,
- MATH 4387 Applied Regression Analysis and MATH 4830 Applied Statistics.
| Code | Title | Hours |
|---|---|---|
| Complete the following required courses: | 12 | |
| Programming for Data Science | ||
| Fundamentals of Computing and Fundamentals of Computing Laboratory | ||
or ISMG 4400 | Programming Fundamentals with Python | |
| Introductory Statistics | ||
or MATH 3382 | Statistical Theory | |
or MATH 3800 | Probability and Statistics for Engineers | |
| Data Wrangling & Visualization | ||
| Applied Statistics | ||
or MATH 4387 | Applied Regression Analysis | |
| Complete six credit hours of electives from the following list of approved courses: | 6 | |
| Applied Linear Algebra | ||
| Linear Algebra and Differential Equations | ||
| Elementary Differential Equations | ||
| Introduction to Optimization | ||
| Introduction to Probability | ||
| Intro to Statistical and Machine Learning | ||
| Machine Learning Methods | ||
| Game Theory | ||
| Applied Graph Theory | ||
| Numerical Analysis I | ||
| Numerical Analysis II | ||
| Partial Differential Equations | ||
| Probabilistic Modeling | ||
| Workshop in Statistical Consulting | ||
| Data Analysis with SAS | ||
| Introduction to Econometrics | ||
| Physical Chemistry: Quantum and Spectroscopy | ||
| Molecular Informatics | ||
| Artificial Intelligence in Chemistry and Biochemistry | ||
| Molecular Modeling and Drug Design | ||
| Database System Concepts | ||
| Network Structures | ||
| Data Mining | ||
| Data Science | ||
| Bioinformatics | ||
| Machine Learning | ||
| Deep Learning | ||
| Big Data Systems | ||
| Remote Sensing II: Advanced Remote Sensing | ||
| Introduction to GIS | ||
| Cartography | ||
| GIS Applications for the Urban Environment | ||
| Environmental Modeling with Geographic Information Systems | ||
| Open Source Software for Geospatial Applications | ||
| GIS Programming and Automation | ||
| Deploying GIS Functionality on the Web | ||
| GIS Applications in the Health Sciences | ||
| Technology In Business | ||
| Business Data and Database Management | ||
| Total Hours | 18 | |
To learn more about the Student Learning Outcomes for this program, please visit our website.