Statistics and Data Science
The digital revolution has created vast quantities of data. Extracting knowledge and insight from this avalanche of information is the goal of data science, a rapidly growing field with applications in such areas as marketing, education, and sports, as well as scientific fields such as genomics, neuroscience, and particle physics.
Decision-makers have access to more data than ever before, but the deriving meaning and actionable insights from that data requires specialized tools and expertise. For that reason, graduates with degrees in statistics and data science are in high demand.
Currently, there is a global data scientist shortage. It is estimated that within the next two years, there will be twice as many data science jobs as there will be people to fill those roles. This means extensive job opportunities for individuals with the necessary education and skills.
UE’s program in statistics and data science combines state-of-the-art tools and techniques from the field of data science with a mathematically rigorous tradition of classical applied statistics. Students in the program will…
Engage through project-driven courses. Data analysis projects offered throughout the curriculum expose students to the entire work cycle of predictive modeling, including problem formulation, acquisition, and cleaning of data, model selection, and fitting, interpretation, and reporting.
Master cutting-edge statistical software. Students gain fluency in the statistical software currently in use within business and industry, including R, Python, and BigQuery.
Receive a first-class liberal arts education. Working with “big data” requires more than quantitative and technological skills—it also requires an ability to frame questions, to bring diverse teams together, to make ethical and informed decisions, and to communicate results to decision-makers. A UE education provides students with broad foundational knowledge in the arts and sciences, as well as the critical thinking and communication skills that employers value.
STAT 266 - Introductory Statistics with R (Annually in Spring)
STAT 267 - Experimental Design (Annually in Fall)
STAT 300 - Data Analysis in Real World (Annually in Fall)
STAT 361 - Linear Models (Annually in Fall)
STAT 362 - Machine Learning (Every other Spring)
STAT 474 - Techniques for Large Data Sets(Every other Fall)
STAT 493 - Statistical Modeling (Annually in Spring)
MATH and CS Course
MATH 221, 222 - Calculus (Fall, Spring, and Summer)
MATH 365 - Probability (Annually in Fall)
Math 466 - Mathematical Statistics (Annually in Spring)
MATH 341 - Linear Algebra (Annually in Spring)
MATH 495 - Senior Seminar: Mathematical Modeling (Annually in Fall)
CS 210, 215 - Introduction to Programming (Every Fall and Spring)