Data Science Undergraduate Certificate

Certificate Requirements 

The certificate program provides basic training on skills required for working in growing and popular fields involving data and data analysis. It provides both statistical and computational background while also allowing to focus on specific technologies. A student pursuing this certificate can choose from one of the two tracks, the computational track and the statistical track. Each track consists of three required courses (9 credit hours) plus three additional elective courses (9 credit hours).

Required Courses for the Computational Track:

CMP SCI 4340Introduction to Machine Learning3
CMP SCI 4342Introduction to Data Mining3
Choose one from the following:3
Exploratory Data Analysis with R
Mathematical Statistics I

Required Courses for the Statistical Track:

MATH 4200Mathematical Statistics I3
MATH 4210Mathematical Statistics II3
Choose one from the following:3
Introduction to Statistical Methods in Learning and Modeling
Introduction to Machine Learning

Electives for both tracks:

Select additional three courses from the following:9
Introduction to Intelligent Web
Introduction to Artificial Intelligence
Introduction to Evolutionary Computation
Introduction to Machine Learning
Introduction to Data Mining
Introduction to Biological Data Science
Introduction to Deep Learning
Exploratory Data Analysis with R
Introduction to High-dimensional Data Analysis
Mathematical Statistics I
Mathematical Statistics II
Bayesian Statistical Methods
Introduction to Statistical Computing
Introduction to Statistical Methods in Learning and Modeling
Introduction to Stochastic Processes

Residency requirement: of the required six courses at least five must be taken at the University of Missouri – St. Louis. Elective courses may be substituted with the permission of the program director. For more information, contact the department chair or email

Learning Outcomes

Upon completion of a Certificate in Data Science at the University of Missouri St. Louis, students will be able to:

  • Identify, interpret, and manage the computational issues involved in the handling of large volumes of data
  • Apply algorithmic principles and statistical theories to analyze data-sets
  • Build and evaluate data-based models
  • Apply machine learning techniques to data-mining problems