Bachelor of Science In Applied and Computational Mathematics
One Degree – Three Options
The B.S. in Applied and Computational Mathematics is one degree that consists of the following three options that a student may choose from:
 Mathematics of Data Science and Machine Learning
 Actuarial Science
 Applied Differential Equations and Scientific Computing
Requirements:
In addition to the courses listed below, students must complete all university general education requirements and sufficient additional free electives to total a minimum of 120 semester hours, including foreign language and physical education.
All Option Areas Must Take
 MAT 141 – Foundational Discrete Math
 MAT 150 – Math Seminar I
 MAT 151 – Math Seminar II
 MAT 181 – Calculus I
 MAT 182 – Calculus II
 MAT 207 – Proofs
 MAT 222 – Introductory Statistics
 MAT 272 – Linear Algebra
 MAT 281 – Calculus III
 MAT 282 – Ordinary Differential Equations
 MAT 322 – Probability
 MAT 332 – Applied Linear Algebra and Math of Machine Learning
 MAT 380 – Math Modeling with Symbolic and Scientific Computations
 MAT 383 – Introduction to Mathematical Analysis
 MAT 453 – Senior Seminar (CE,W3) (OR SIS with Project) (OR Senior Thesis) (OR Internship)
Further Requirements (Option Specific)
Mathematics of Data Science and Machine Learning Option:
Further Required MAT Courses

 MAT 422 – Statistics for Data/Actuarial Science and Machine Learning
 MAT 470 – Applications of Machine Learning and Wavelets
One of:

 MAT 468 – Partial Differential Equations
 MAT 469 – Numerical Methods for Ordinary and Partial Differential Equations (OPDEs)
Cognates

 PHI 227 – Ethics in Computing
 CS 140 – Introduction to Programming (Python)
Application Area Courses

 CS 172 – Intermediate Java Programming
 CS 205 – Data Modeling and Database Design
 CS 250 – Introduction to Data Structures, Algorithms and Complexity
 CS 303 – Introduction to Data Science with Python
Applied Differential Equations and Scientific Computing Option:
Currently, students interested in this option will need to pick one of the following sequences to pursue: a chemistry sequence (CHE) or meteorology sequence (MTR). For students interested in other application areas, with departmental and university approval, we hope to add a sequence in biology as well as other areas of interest. Consult with Drs Christofi, Shoushani, or Wang.
Further Required MAT Courses
Regardless of the sequence pursued, students interested in this option must take:

 MAT 468 – Partial Differential Equations
 MAT 469 – Numerical Methods for Ordinary and Partial Differential Equations (OPDEs)
 MAT 470 – Applications of Machine Learning and Wavelets
Cognates
Cognates (Sequence CHE)
CS 140 – Introduction to Programming (Python)
Cognates (Sequence MTR)


 CS 140 – Introduction to Programming (Python)
 PHY 110 – General Physics I (Calculus)

Application Area Courses
Sequence CHE
Lecture only:


 CHE 110 – General Chemistry I
 CHE 111 – General Chemistry II
 CHE 300 – Physical Chemistry I
 CHE 301 – Physical Chemistry II

Sequence MTR


 PHY 111 – General Physics II (Calculus),
 MTR 310 – Atmospheric Thermodynamics,
 MTR 311 – Atmospheric Dynamics,
 MTR 340 – Mesoscale Meteorology and Numerical Forecasting

Actuarial Science Option:
Further Required MAT Courses

 MAT 329 – Actuarial Mathematics
 MAT 422 – Statistics for Data/Actuarial Science and Machine Learning
One of:

 MAT 468 – Partial Differential Equations
 MAT 469 – Numerical Methods for Ordinary and Partial Differential Equations (OPDEs)
 MAT 470 – Applications of Machine Learning and Wavelets
Cognates

 CS 143 – Visual BASIC
Application Area Courses

 ACC 201 – Financial Accounting
 ECO 211 – Principles of Macroeconomics
 ECO 213 – Principles of Microeconomics
 FIN 310 – Principles of Finance
Learning Outcomes:
The BS in Applied and Computational Math program will graduate students who will:
1. Demonstrate possession of a resilient mathematical foundation that allows them to reason rigorously in mathematical arguments and that is adaptable to current and future trends. This foundation encompasses the core areas of:
a. Real and numerical analysis
b. Differential equations
c. Linear algebra
d. Probability and statistics
2. Connect different areas of mathematics with other disciplines and demonstrate proficiency in at least one of the following modern applications:
a. Data Science and Machine Learning
b. Scientific Computing (coupled with a specific discipline/application area)
c. Actuarial Science.
3. Demonstrate an ability to synthesize and apply major theoretical and/or computational techniques and concepts to analyze, construct, and solve realistic models of practical importance and:
a. Recognize the limitations of the theoretical concepts in building solutions to realworld problems
b. Adapt theoretical ideas to develop efficient numerical solutions to realworld problems
c. Adapt theoretical ideas to develop efficient algorithms that can be applied to realworld problems.
4. Use relevant software and technology (such as MATLAB, Mathematica, Python, and LaTeX) and/or write computer programs to construct, visualize, analyze, and interpret solutions to applied mathematical problems.
5. Be able to, working independently or collaboratively, apply concepts learned either from relevant coursework, possible internships, or research projects with faculty, to write mathematical reports that effectively communicate findings to others (for instance, other classmates and/or attendees at local/international conferences or research symposia such as Western Research Day), and that can serve as the basis for possible publications.
Program Sheets
 Full Program Sheet (one degree, 3 options program sheet)
 Mathematics of Data Science and Machine Learning Individual Option Program Sheet
 Applied Differential Equations and Scientific Computing Individual Option Program Sheet
 Actuarial Science Individual Option Program Sheet
Prerequisite Flowcharts
 MAT Courses Prerequisite Flowchart
 Mathematics of Data Science and Machine Learning Individual Option CS Courses Prerequisite Flowchart
Four Year Plan of Study:
Four year plans for each of the options can be found at the links below
 Data Science and Machine Learning starting odd year
 Data Science and Machine Learning starting even year
 Applied Differential Equations with Scientific Computing starting odd year (MTR sequence)
 Applied Differential Equations with Scientific Computing starting even year (MTR sequence)
 Applied Differential Equations with Scientific Computing starting odd year (CHE sequence)
 Applied Differential Equations with Scientific Computing starting even year (CHE sequence)
 Actuarial Science starting odd year
 Actuarial Science starting even year