Data Science Undergraduate Certificate

Certificate Requirements 

The undergraduate certificate in Data Science is a five-course (15 credit hour) program. It provides skills, both statistical and computational, and technologies for the growing and popular fields involving data science and analysis. A student pursuing this certificate can choose from one of the two tracks, the computational track or the statistical track. Each track consists of three required courses (9 credit hours) and two additional elective courses (6 credit hours).

Computational Track

Required Courses
CMP SCI 4200Python for Scientific Computing and Data Science3
CMP SCI 4340Introduction to Machine Learning3
CMP SCI 4342Introduction to Data Mining3
Electives
Choose two of the following courses:6
Introduction to Intelligent Web
Introduction to Artificial Intelligence
Introduction to Evolutionary Computation
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
Total Hours15

Statistical Track

Required Courses
MATH 4200Mathematical Statistics I3
MATH 4210Mathematical Statistics II3
MATH 4250Introduction to Statistical Methods in Learning and Modeling3
or CMP SCI 4340 Introduction to Machine Learning
Electives
Choose two of the following courses:6
Introduction to Intelligent Web
Python for Scientific Computing and Data Science
Introduction to Artificial Intelligence
Introduction to Evolutionary Computation
Introduction to Machine Learning (if course not used above)
Introduction to Data Mining
Introduction to Biological Data Science
Introduction to Deep Learning
Exploratory Data Analysis with R
Introduction to High-dimensional Data Analysis
Bayesian Statistical Methods
Introduction to Statistical Computing
Introduction to Statistical Methods in Learning and Modeling (if course not used above)
Introduction to Stochastic Processes
Total Hours15

A minimum of three courses must be taken from UMSL. Courses may be substituted with the permission of the certificate coordinator. For more information, contact the department chair or email info@arch.umsl.edu.

Learning Outcomes

Upon completion the program, certificate earners 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