Data Sciences 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.
- Students must complete a minimum of 9 upper-division (3000-level and above) credit hours. 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 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 ENGR 1100 | Fundamentals of Computational Innovation | |
or ISMG 4400 | Programming Fundamentals with Python | |
| Introductory Statistics | ||
or MATH 3382 | Statistical Theory | |
or MATH 3800 | Probability and Statistics for Engineers | |
or BANA 2010 | Business Statistics | |
or BIOL 3762 | Biostatistics | |
or CRJU 3150 | Statistics for Criminal Justice | |
or CSCI 2980 | Foundations of Data Science | |
or ECON 3811 | Statistics with Computer Applications | |
or PSYC 2090 | Statistics and Research Methods | |
| Data Wrangling & Visualization | ||
or CSCI 3287 | Database System Concepts | |
or ECON 4030 | Data Analysis with SAS | |
or GEOG 4080 | Introduction to GIS | |
or HIST 4261 | Working With Data | |
or ISMG 2050 | Business Problem Solving Tools | |
| Intermediate Excel for Business and Introduction to Tableau and SQL Foundations | ||
| Applied Statistics | ||
or MATH 3301 | Introduction to Optimization | |
or MATH 4387 | Applied Regression Analysis | |
or MATH 4388 | Machine Learning Methods | |
or BANA 4120 | Forecasting Techniques | |
or CSCI 4455 | Data Mining | |
or CSCI 4580 | Data Science | |
or CSCI 4930 | Machine Learning | |
or ECON 4811 | Introduction to Econometrics | |
or ELEC 3701 | Machine Learning for Engineers | |
| Complete six credit hours of electives from the following list of approved courses: | 6 | |
| Business Analytics Process | ||
| Forecasting Techniques | ||
| Physical Chemistry: Quantum and Spectroscopy | ||
| Molecular Informatics | ||
| Artificial Intelligence in Chemistry and Biochemistry | ||
| Molecular Modeling and Drug Design | ||
| Database System Concepts | ||
| Probability and Computing | ||
| Network Structures | ||
| Social Networks & Informatics | ||
| Natural Language Processing with Generative AI | ||
| Applied Graph Theory | ||
| Data Mining | ||
| Big Data Mining | ||
| Bioinformatics | ||
| Special Topics (must be relevant to Data Science) 1 | ||
| Machine Learning | ||
| Deep Learning | ||
| Big Data Systems | ||
| Data Analysis with SAS | ||
| Introduction to Econometrics | ||
| Advanced Econometric Methods | ||
| Machine Learning for Engineers | ||
| Remote Sensing I: Introduction to Environmental Remote Sensing | ||
| 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 | ||
| System Strategy, Architecture and Design | ||
| Information Systems Security and Privacy | ||
| Business Intelligence for Financial Modeling | ||
| Project Management and Practice | ||
| Database Management Systems | ||
| Text Data Analytics | ||
| Applied Linear Algebra | ||
| Linear Algebra and Differential Equations | ||
| Elementary Differential Equations | ||
| Introduction to Optimization | ||
| Introduction to Probability | ||
| Machine Learning Methods | ||
| Game Theory | ||
| Applied Graph Theory | ||
| Numerical Analysis I | ||
| Numerical Analysis II | ||
| Partial Differential Equations | ||
| Probabilistic Modeling | ||
| Marketing Research | ||
| Total Hours | 18 | |
- 1
Subject to pre-requisite requirements as well as approval of the Director of Data Science and course instructor
To learn more about the Student Learning Outcomes for this program, please visit our website.