Department of Mathematics and Statistics
BA in Mathematics - Statistics Concentration
The Department of Mathematics and Statistics offers a B.A. degree in Mathematics. The program is designed to provide students with a broad background in mathematics and statistics. Students select one of three concentrations: pure mathematics, applied mathematics/operations research, or statistics. Students who would like to become certified to teach may select the Secondary Teacher Education track. The Department also offers an accelerated, flexible 4+1 master's program in statistics, where students can earn both an undergraduate degree and the M.S. degree in statistics in five years by carefully selecting their courses.
The Statistics concentration is aimed at preparing undergraduates to pursue a career as a statistician in government or industrial jobs, or to pursue a higher degree in statistics or allied fields. Majors intending to pursue graduate work in statistics are urged to take Real Analysis and Abstract Algebra.
The minimum number of credits (exclusive of the University's Core curriculum) required for a bachelor of arts in mathematics: 49. Each student must have a cumulative grade point average of at least 2.0 in major courses before being considered for a baccalaureate degree in mathematics.
A. Foundations (34 credits)
All students are required to successfully complete the foundations sequence listed below.
MAT 152 Calculus A (4 cr)
MAT 153 Calculus B (4 cr)
MAT 252 Calculus C (4 cr)
MAT 281 Introduction to Probability (3 cr)
MAT 282 Statistical Inference (3 cr)
MAT 290 Foundations of Mathematics (4 cr)
MAT 295 Linear Algebra (4 cr)
MAT 350 Differential Equations (4 cr)
COS 160 Structured Problem Solving: Java (3 cr)
COS 170 Structured Programming Laboratory (1 cr)
B. Concentration (15 credits)
In addition to the requirements listed above, the following items are required for the Statistics Concentration.
a. Successful completion of three courses listed below:
MAT 264 Statistical Software Packages
MAT 383 System Modeling and Simulation
MAT 386 Sampling Techniques
MAT 387 Introduction to Applied/Biostatistical Methods
MAT 388 Statistical Quality Control
MAT 484 Design and Analysis of Experiments
MAT 485 Introduction to Applied Regression
MAT 486 Introduction to Big Data Analytics
MAT 487 Introduction to Categorical Data Analysis
MAT 488 Introduction to Data Mining
b. Successful completion of at least two additional mathematics courses numbered 260 or above, excluding MAT 380 Probability and Statistics.