Data Science Undergraduate Certificate
Please click here to see Mathematical and Statistical Sciences department information.
Introduction
Data scientists will have essential competencies in several areas related to analysis of data. In particular, a data scientist should: have strong programming ability in a language popular in data science (e.g., Python, R, Julia); be able to extract, manipulate, and visualize data; have an understanding of probability and statistics in order to quantify uncertainty; be able to build complex models for finding patterns and explaining data. This certificate should provide students with essential skills for introductory data science.
Program Delivery
- This is an on-campus program.
Declaring This Certificate
- See the program advisor for an application form.
Coordinator: Adam Spiegler Ph.D.
Telephone: 303-315-1700
E-mail: math.advising@ucdenver.edu
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 Data Science advisor to confirm the best plans of study before finalizing them.
General Requirements
Students must satisfy all requirements as outlined below and by the department offering the certificate.
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Program Requirements
- Students must complete a minimum of 12 credit hours from approved courses.
- Students must complete a minimum of six upper division (3000-level and above) credit hours.
- Students must earn a minimum grade of C- (1.7) in all courses that apply to the certificate and must achieve a minimum cumulative certificate GPA of 2.25. All graded attempts in required and elective courses are calculated in the certificate GPA. Courses taken using P+/P/F or S/U grading cannot apply to certificate requirements.
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Students must complete a minimum of nine upper division level credit hours with CU Denver faculty.
| Code | Title | Hours |
|---|---|---|
| In order to ensure adequate programming skills for data science, students complete one of the following courses that develops strong programming skills in a programming language popular in data science (e.g., Python, R, Julia). | 3 | |
| Programming for Data Science | ||
| Fundamentals of Computing and Fundamentals of Computing Laboratory | ||
| Numerical Analysis I | ||
| Programming Fundamentals with Python | ||
| In order to ensure that students can accurately quantify the likelihood of various outcomes and quantify uncertainty related to estimation and prediction, students complete one of the following courses that covers basic probability and statistics. | 3 | |
| Introductory Statistics | ||
| Statistical Theory | ||
| Probability and Statistics for Engineers | ||
| In order to ensure that students are able to comfortably work with and visualize data, students complete the following course, developing skills related to obtaining, manipulating, and visualizing data. | 3 | |
| Data Wrangling & Visualization | ||
| In order to ensure that students are able to build reasonably complex models for explaining or identifying patterns in data, students take one of the following courses that largely focuses on describing the behavior of data (whether synthetic or observed) via tools like simulation, direct model building, association, or a complementary approach. | 3 | |
| Introduction to Optimization | ||
| Applied Regression Analysis | ||
| Applied Statistics | ||
| Total Hours | 12 | |
To learn more about the Student Learning Outcomes for this program, please visit our website.