Statistics and Data Science

The Statistics and Data Science major is designed for students pursuing a career as a data scientist or statistician. It combines cutting-edge techniques in data science with mathematically rigorous statistics. Statistics courses in the curriculum are project-driven with an emphasis on the analysis of real-world data using statistical methods implemented by powerful statistical software. This program prepares students for a career in business or industry utilizing statistics or data science but is also sufficiently rigorous to prepare a student for graduate work in a related field.

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.

Career Opportunities

Decision-makers have access to more data than ever before, but 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.

Curriculum

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.

Details on program requirements and course descriptions can also be found in the catalog.

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Additional Information

Sample 4-year Plan Beginning in an Odd Year

With Harlaxton

Fall Spring
Freshman MATH 221 – Calculus I
STAT 166 – Intro to R for Data Science		 MATH 222 – Calculus II
STAT 266 – Introductory Statistics with R
Sophomore CS 210 – Fund. of Programming I
STAT 267 – Experimental Design
MATH 365 – Probability		 MATH 341 – Linear Algebra
Math 466 – Statistics
CS 215 – Fund. of Programming II
Junior STAT 361 – Linear Models Harlaxton
Senior STAT 300 – Data Analysis in Real World
STAT 474 – Techniques for Large Data Sets		 STAT 362 – Machine Learning
STAT 493 – Statistical Modeling

Without Harlaxton

Fall Spring
Freshman MATH 221 – Calculus I
STAT 166 – Intro to R for Data Science		 MATH 222 – Calculus II
STAT 266 – Introductory Statistics with R
CS 210 – Fund. of Programming I
Sophomore STAT 267 – Experimental Design
MATH 365 – Probability
CS 215 – Fund. of Programming II		 MATH 341 – Linear Algebra
MATH 466 – Statistics
Junior STAT 361 – Linear Models STAT 362 – Machine Learning
Senior STAT 300 – Data Analysis in Real World
STAT 474 – Techniques for Large Data Sets		 STAT 493 – Statistical Modeling

Sample 4-year Plan Beginning in an Even Year

With Harlaxton

Fall Spring
Freshman MATH 221 – Calculus I
STAT 166 – Intro to R for Data Science		 MATH 222 – Calculus II
STAT 266 – Introductory Statistics with R
CS 210 – Fund. of Programming I
Sophomore STAT 267 – Experimental Design
MATH 365 – Probability
CS 215 – Fund. of Programming II		 MATH 341 – Linear Algebra
MATH 466 – Statistics
Junior STAT 361 – Linear Models
STAT 474 – Techniques for Large Data Sets		 Harlaxton
Senior STAT 300 – Data Analysis in Real World STAT 362 – Machine Learning
STAT 493 – Statistical Modeling

Without Harlaxton

Fall Spring
Freshman MATH 221 – Calculus I
STAT 166 – Intro to R for Data Science		 MATH 222 – Calculus II
STAT 266 – Introductory Statistics with R
CS 210 – Fund. of Programming I
Sophomore STAT 267 – Experimental Design
MATH 365 – Probability
CS 215 – Fund. of Programming II		 MATH 341 – Linear Algebra
MATH 466 – Statistics
Junior STAT 361 – Linear Models
STAT 474 – Techniques for Large Data Sets		 STAT 300 – Data Analysis in Real World
STAT 362 – Machine Learning
Senior STAT 300 – Data Analysis in Real World
 STAT 493 – Statistical Modeling

Note: CS 215* can be replaced by a computer-based course. Harlaxton: STAT 361 can be taking in the fall of the senior year.

STAT Course Frequency
STAT 166 – Intro to R for Data Science Annually in Fall
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 Spring
STAT 474 - Techniques for Large Data Sets Every even Fall
STAT 493 - Statistical Modeling Annually in Spring
MATH and CS Course Frequency
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
CS 210, 215 - Introduction to Programming Every Fall and Spring

Mathematics Course Dependency Chart