Undergraduate Certificate in Data Science Essentials
Director of Data Science: Joshua French, Ph.D.
Telephone: 303-315-1700
E-mail: data.science.advising@ucdenver.edu
Contact the Director of Data Science for advising, declaring your certificate or being certified once you have completed your coursework.
Program outcomes:
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. Additional training related to database management, highperformance computing, and modeling would likely be necessary for advanced data science analysis.
Learning outcomes:
Students completing this certificate will have essential competencies in several areas related to analysis of data.
- Have basic programming ability in a language popular in data science (e.g., Python, R, Julia)
- Be able to extract, manipulate, and visualize data
- Understand probability and statistics in order to quantify uncertainty
- Be able to build complex models for finding patterns and explaining data.
Course requirements:
Programming -- In order to ensure adequate programming skills for data science, students should take a course that develops strong programming skills in a programming language popular in data science (e.g., Python, R, Julia). The list of currently approved courses includes:
- MATH 1376 -- Programming for Data Science
- MATH 4650 -- Numerical Analysis I
- ISMG 4400 -- Web Application Development (Programming Fundamentals with Python)
- CSCI 1410 -- Fundamentals of Computing and CSCI 1411 -- Fundamentals of Computing Laboratory
Probability and statistics -- In order to ensure that students can accurately quantify the likelihood of various outcomes and quantify uncertainty related to estimation and prediction, students should take a course that covers basic probability and statistics. The list of currently approved courses includes:
- MATH 2830 -- Introductory Statistics (or equivalent coursework with Undergraduate Committee approval)
- MATH 3382 -- Statistical Theory
- MATH 3800 -- Probability and Statistics for Engineers
Data manipulation and visualization -- In order to ensure that students are able to comfortably work with and visualize data, students should take a course developing skills related to obtaining, manipulating, and visualizing data. The list of currently approved courses includes:
- MATH 3376 -- Data Wrangling & Visualization
Data modeling -- In order to ensure that students are able to build reasonably complex models for explaining or identifying patterns in data, students should take a course 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. The list of currently approved courses includes:
- MATH 3301 -- Introduction to Optimization in Operations Research
- MATH 4387 -- Applied Regression Analysis
- MATH 4830 -- Applied Statistics