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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.
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 program advisor and CLAS advisor to confirm the best plans of study before finalizing them.
This is an on-campus program.
Declaring This Certificate
See the program advisor for an application form.
Coordinator: Adam Spiegler Ph.D.
Students must satisfy all requirements as outlined below and by the department offering the certificate.
- Students must complete a minimum of 12 credit hours from approved courses.
- Students must complete a minimum of 6 upper division (3000-level and above) credit hours.
- Students must earn a minimum grade of C- (1.7) in all certificate courses taken at CU Denver 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. Students cannot complete certificate or ancillary course requirements as pass/fail.
Students must complete a minimum of 9 upper division level credit hours with CU Denver faculty.
|In order to ensure adequate programming skills for data science, students take one of the following courses that develops strong programming skills in a programming language popular in data science (e.g., Python, R, Julia).||3|
|MATH 1376||Programming for Data Science||3|
|MATH 4650||Numerical Analysis I||3|
|ISMG 4400||Programming Fundamentals with Python||3|
|In order to ensure that students can accurately quantify the likelihood of various outcomes and quantify uncertainty related to estimation and prediction, students take one of the following courses that covers basic probability and statistics.||3|
|MATH 2830||Introductory Statistics (or equivalent coursework with Undergraduate Committee approval)||3|
|MATH 3382||Statistical Theory||3|
|MATH 3800||Probability and Statistics for Engineers||3|
|In order to ensure that students are able to comfortably work with and visualize data, students take the following course, developing skills related to obtaining, manipulating, and visualizing data.||3|
|MATH 3376||Data Wrangling & Visualization||3|
|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|
|MATH 3301||Introduction to Optimization||3|
|MATH 4387||Applied Regression Analysis||3|
|MATH 4830||Applied Statistics||3|
To learn more about the Student Learning Outcomes for this program, please visit our website.