Data Science - Graduate Certificate

Online (Code 181-601)

The multidisciplinary field of data science is one of the fastest growing professions and academic disciplines in the 21st century. The curriculum of this fully online, graduate certificate program is designed primarily for working adults and nontraditional students who desire to continue their education, primarily to expand their knowledge and specialized skills in this area for career advancement.

This program provides the student those fundamental data science tools required to analyze real-world datasets and to communicate effectively about data science findings. The program features a multidisciplinary curriculum that draws primarily from computer science, statistics, and communication.

Students completing the program will have expertise in data warehousing, database infrastructures, data mining, machine learning, and analytics-based decision making.

15 credits required:

Required Courses
DS 701Exploratory Data Analysis3
DS 705Statistical Methods3
DS 710Programming for Data Science3
Two Courses Selected from Elective List:
DS 716
Data Management for Data Science
DS 730
Big Data: High Performance Computing
DS 740
Data Mining & Machine Learning
DS 750
Data Storytelling
DS 770
Ethical Decision-Making Using Data
DS 776
Deep Learning

Completion of the certificate will require a GPA of 3.0. 

Requirements for Admission to the Graduate Certificate in Data Science Program:

  1. A bachelor’s degree from a regionally accredited university with a cumulative GPA of 3.0 or higher. Students with a GPA of less than 3.0 may be considered for provisional admission based on a review of all application materials.
  2. Coursework or relevant work experience that evidences fundamental programming skills.

Program Learning Outcomes

Students completing this program will be expected to meet the following learning outcomes: 

  • Collect, prepare, store and manage data to devise solutions to data science tasks.
  • Manage and use data in various forms, from traditional databases to big data.
  • Design and implement algorithms to translate data into actionable insights.
  • Create, write, and orally communicate technical materials for diverse audiences
  • Help technical and non-technical professionals visualize, explore, interpret, and act on data science findings.