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Applied Mathematics – Data Analysis

Do you have a passion for uncovering insights, solving problems, and making data-driven decisions? McDaniel College's Data Analysis degree program, a specialization of the Applied Mathematics major, will prepare you for data analytics jobs and data analytics graduate programs — including McDaniel's own M.S. in Data Analytics.

Degree Types
Major , Specialization
Complementary Programs
Distinctive Requirements
One Writing Course
Graduate School Opportunity

Why study Data Analysis at McDaniel?

McDaniel College's Applied Mathematics - Data Analysis specialization will provide you with hands-on courses in analysis, advanced mathematics, statistics, mathematical modeling, and more. With faculty mentorship and opportunities to conduct research and attend conferences, you'll be equipped for your future data analysis career or graduate program.


Increase in demand for Data Scientists.

Data Analytics Career Outlooks

In today's data-driven world, the need for professionals skilled in data analysis has never been greater. As organizations strive to make data-informed decisions, the demand for individuals with expertise in interpreting and harnessing the power of data is on the rise. With industries like finance, healthcare, retail, and manufacturing, organizations have been increasingly adopting data analytics to gain a competitive edge.

Are you ready to take advantage of this growing field?

Data are from the Occupational Employment and Wage Statistics program, U.S. Bureau of Labor Statistics.

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Median Annual Wage for Data Scientists

Earn an advanced degree in Data Analytics at McDaniel

The Data Analytics M.S. program at McDaniel College provides graduate learners with the skills needed to be successful data analysts, operational analysts and leaders in a competitive global Information Technology arena.

Learn about the M.S. in Data Analytics Program

Distinctive Courses in Mathematics - Data Analysis

MAT 3201 - Data Analysis

Data analysis is the study of the extraction of knowledge from data. This course is an introduction to the mathematical methods behind the scientific techniques developed for extracting information from large data sets. We will explore several fundamental topics in computational data analysis, including basic concepts in probability, Bayes’ rule, central limit theory, linear regression, dimension reduction, gradient descent, principal component analysis, clustering and classification. We will use Python to demonstrate and explore basic concepts, but programming will not be the main focus of the course.

MAT 1112 - Introduction to Math Modeling in STEM

Making good decisions requires understanding the consequences of those decisions. In this course we will explore how to use various mathematical models to  predict outcomes of decisions in fields as diverse as environmental conservation, business management, and engineering. Students will also gain experience  using software to both analyze their models and communicate their results and recommendations. Emphasis will be on providing quantitative evidence for a  decision using mathematical tools including basic probability, calculus, graph theory, and game theory.

MAT 2210 - Numerical Methods

An introduction to numerical methods for solving problems from calculus and linear algebra, including the solution of a single nonlinear equation, the solution of linear systems, interpolation and approximation, differentiation and integration, and the solution of eigenvalue problems.

Special Opportunities

Professor announcing winner at Estimathon.

Student-Faculty Research in Math Maximize Your Summers

The annual Student-Faculty Collaborative Summer Research Program gives students an opportunity to work on a unique project with peers and faculty mentors. Previous Mathematics research projects include:

  • Generalizations of Waring's Problem with Professor Spencer Hamblen

  • Sign Language Recognition and Translation using Machine Learning with Associate Professor Paul Lin

  • Applications of Entropy to Gerrymandering with Lecturer Jonathan Epstein

  • Hierarchies in Fibonacci Word Fractals with Associate Professor Benjamin Steinhurst

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Conference Experiences in Math Network with Professionals

During Jan Term (and practically any other time of the year), students attend conferences to showcase their original research and get the full experience of mingling with high-profile mathematicians. Mark your calendar for:

  • Jan Term Conference Experience in Mathematics 
  • Mathematics Association of America (MAA) conference / MathFest
  • Maryland Collegiate Honors Conference
  • And more!

Pictured: Students attended the Joint Mathematics Meetings in January 2023, the largest mathematics conference in the world.

McDaniel Commitment in Action

The McDaniel Commitment—a series of opportunities guaranteed to all students—provides enhanced mentoring and coaching, and ensures every undergraduate student completes at least two meaningful experiential learning opportunities.

Jonathan Epstein Headshot

Jon Epstein, Ph.D. Faculty Spotlight

One of Jon Epstein’s core lessons to his students is that mathematics is not just a tool for understanding the world, but a human endeavor undertaken together. Epstein came to McDaniel after a postdoctoral fellowship at the University of Oklahoma. He holds a Ph.D. from Dartmouth College, a master’s degree from New York University, and a bachelor’s degree from Columbia University, all in Mathematics.

Portrait of Professor Kevin McIntyre.

Meet the Faculty Kevin McIntyre, Ph.D.

Kevin McIntyre is known as an enthusiastic teacher and is described as quite the character. He believes in forming witty and dynamic relationships with students that make his courses fun to remember. McIntyre teaches almost everything in McDaniel’s economics and statistics offerings from introductory and first-year seminars to upper-level economics and graduate courses in Data Analytics.