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 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.
- 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.
Students must satisfy all requirements as outlined below and by the department offering the minor.
- Students must complete a minimum of 18 credit hours, including a minimum of 12 MATH credit hours.
- Students must complete a minimum of 9 upper division (3000-level and above) MATH credit hours. Most upper-division courses have lower-division pre-requisites.
- Students must earn a minimum grade of C- (1.7) in all major courses taken at CU Denver and must achieve a minimum cumulative minor GPA of 2.0. All graded attempts in required and elective courses are calculated in the minor GPA. Students cannot complete minor or ancillary course requirements as pass/fail.
- Students must complete a minimum of 6 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 4810 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.
|Take the following||12|
|MATH 1376||Programming for Data Science||3|
|or ISMG 4400||Programming Fundamentals with Python|
|MATH 2830||Introductory Statistics (or equivalent coursework with Undergraduate Committee approval)||3|
|or MATH 3382||Statistical Theory|
|or MATH 3800||Probability and Statistics for Engineers|
|MATH 3376||Data Wrangling & Visualization||3|
|MATH 4830||Applied Statistics||3|
|or MATH 4387||Applied Regression Analysis|
|Take two courses from the following list of approved courses:||6|
|CSCI 3287||Database System Concepts||3|
|CSCI 3963||Network Structures||3|
|CSCI 4455||Data Mining||3|
|CSCI 4580||Data Science||3|
|CSCI 4930||Machine Learning||3|
|CSCI 4931||Deep Learning||3|
|CSCI 4951||Big Data Systems||3|
|ECON 4030||Data Analysis with SAS||3|
|ECON 4811||Introduction to Econometrics||3|
|GEOG 4070||Remote Sensing II: Advanced Remote Sensing||3|
|GEOG 4080||Introduction to GIS||3|
|GEOG 4081||Cartography and Computer Mapping||3|
|GEOG 4085||GIS Applications for the Urban Environment||3|
|GEOG 4090||Environmental Modeling with Geographic Information Systems||3|
|GEOG 4091||Open Source Software for Geospatial Applications||3|
|GEOG 4092||GIS Programming and Automation||3|
|GEOG 4095||Deploying GIS Functionality on the Web||3|
|GEOG 4235||GIS Applications in the Health Sciences||3|
|ISMG 3000||Technology In Business||3|
|ISMG 3500||Enterprise Data and Content Management||3|
|MATH 3191||Applied Linear Algebra||3|
|MATH 3195||Linear Algebra and Differential Equations||4|
|MATH 3200||Elementary Differential Equations||3|
|MATH 3301||Introduction to Optimization||3|
|MATH 3302||Simulation in Operations Research||3|
|MATH 4337||Intro to Statistical and Machine Learning||3|
|MATH 4388||Machine Learning Methods||3|
|MATH 4390||Game Theory||3|
|MATH 4394||Experimental Designs||3|
|MATH 4408||Applied Graph Theory||3|
|MATH 4650||Numerical Analysis I||3|
|MATH 4660||Numerical Analysis II||3|
|MATH 4733||Partial Differential Equations||3|
|MATH 4791||Continuous Modeling||3|
|MATH 4792||Probabilistic Modeling||3|
|MATH 4793||Discrete Math Modeling||3|
|MATH 4794||Optimization Modeling||3|
|MATH 4810||Introduction to Probability||3|
|MATH 6330||Workshop in Statistical Consulting||3|
To learn more about the Student Learning Outcomes for this program, please visit our website.