Data Science Undergraduate Certificate

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Introduction

Coordinator: Adam Spiegler Ph.D.

Telephone: 303-315-1700

E-mail: math.advising@ucdenver.edu

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.

General Requirements

Click here for information about Academic Policies.

Program Requirements

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.

Four courses are required as detailed below.

One Programming Course

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 - Programming Fundamentals with Python
One Probability or Statistics Course

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
One Data Manipulation and Visualization Course

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 currently approved course is:
MATH 3376 - Data Wrangling & Visualization
One Data Modelling Course

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