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

1. Students must complete a minimum of 18 credit hours, including a minimum of 9 MATH credit hours.
2. 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.
3. 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.
4. Students must complete a minimum of six upper-division level MATH credit hours with CU Denver faculty.

Program Restrictions, Allowances and Recommendations

1. 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.

Course List

Code	Title	Hours
Complete the following required courses:		12
MATH 1376	Programming for Data Science
or CSCI 1410 & CSCI 1411	Fundamentals of Computing and Fundamentals of Computing Laboratory
or ISMG 4400	Programming Fundamentals with Python
MATH 2830	Introductory Statistics
or MATH 3382	Statistical Theory
or MATH 3800	Probability and Statistics for Engineers
or BANA 2010	Business Statistics
or BIOL 3763
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
MATH 3376	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
or ISMG 3500	Business Data and Database Management
or ISMG 3050 & ISMG 3070 & ISMG 3080	Intermediate Excel for Business and Introduction to Tableau and SQL Foundations
MATH 4830	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
BANA 4110	Business Analytics Process
BANA 4120	Forecasting Techniques
CHEM 4521	Physical Chemistry: Quantum and Spectroscopy
CHEM 4580	Molecular Informatics
CHEM 4640	Artificial Intelligence in Chemistry and Biochemistry
CHEM 4845	Molecular Modeling and Drug Design
CSCI 4455	Data Mining
CSCI 4702	Big Data Mining
CSCI 4788	Bioinformatics
CSCI 4800	Special Topics (must be relevant to Data Science) 1
CSCI 4930	Machine Learning
CSCI 4931	Deep Learning
CSCI 4951	Big Data Systems
ECON 4030	Data Analysis with SAS
ECON 4811	Introduction to Econometrics
ECON 4812	Advanced Econometric Methods
GEOG 4060	Remote Sensing I: Introduction to Environmental Remote Sensing
GEOG 4070	Remote Sensing II: Advanced Remote Sensing
GEOG 4080	Introduction to GIS
GEOG 4081	Cartography
GEOG 4085	GIS Applications for the Urban Environment
GEOG 4090	Environmental Modeling with Geographic Information Systems
GEOG 4091	Open Source Software for Geospatial Applications
GEOG 4092	GIS Programming and Automation
GEOG 4095	Deploying GIS Functionality on the Web
GEOG 4235	GIS Applications in the Health Sciences
ISMG 3000	Technology In Business
ISMG 3600	System Strategy, Architecture and Design
ISMG 4300	Information Systems Security and Privacy
ISMG 4750	Business Intelligence for Financial Modeling
ISMG 4900	Project Management and Practice
ISMG 6080	Database Management Systems
ISMG 6470	Text Data Analytics
MATH 3200	Elementary Differential Equations
MATH 3301	Introduction to Optimization
MATH 4390	Game Theory
MATH 4408	Applied Graph Theory
MATH 4650	Numerical Analysis I
MATH 4660	Numerical Analysis II
MATH 4733	Partial Differential Equations
MATH 4792	Probabilistic Modeling
MKTG 3100	Marketing Research
Total Hours		18

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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.