Courses
Mathematical Sciences
The study of mathematics encourages the development of skills in analytical thinking and problem solving that have wide applicability. Students who graduate with a major in the department have continued their educations in fields as disparate as mathematics, computer science, physics, operations research, law, business, music, religion, dentistry and medicine; others have accepted employment in a wide variety of occupations. The department has a long tradition of successfully preparing students for the actuarial profession.
Mathematics
A major and minor is offered in Mathematics. The basic sequence of courses for Mathematics majors is MATH 151, 152, 223, 251 and 270. Advanced placement and credit can be granted for satisfactory performance on national or departmental examinations.
Actuarial Science and Risk Management
The actuarial science and risk management major integrates business, mathematics, statistics, finance and computer science to prepare you for high-impact opportunities in increasingly high-demand fields. The program delivers both technical and business skills, offering robust industry connections and comprehensive career development support. With a rigorous curriculum and intimate learning environments, actuarial science and risk management is an ideal choice for those seeking an extensive knowledge of insurance and finance, as well as the soft skills of communication, leadership and management. Frequent faculty advising and active student organizations such as Gamma Iota Sigma (GIS) and the 91传媒 Actuarial Student Organization (DASO) create a supportive and collaborative learning environment that pave the way for your success.
Data Science
Numerous inquiries today are advanced through finding the story behind the data; frequently, Data Science builds the road from the what to the why. Through an interdisciplinary approach using Statistics, Mathematics, and Computer Science, this program delivers principles, methodology, and guidelines for conducting data analysis by providing tools, values, and insights. Data Science helps prepare students for success in an increasingly data-driven world, enhances analytical and problem-solving skills, and strengthens communication skills.
Mathematical Sciences Program Outcomes
Math major:
1. Students will be able to set up and solve idealized problems of optimization set in the real world.
2. Students will be able to make general statements about specific circumstances or collections of data.
3. Students will recognize and apply mathematical theorems and concepts to solve problems.
4. Students will be able to read and write solutions to problems in mathematics.
5. Students will understand how mathematics is applied to other disciplines.
Actuarial Science major:
1. Demonstrate the ability to apply the concepts of actuarial science in solving problems related to financial security.
2. a) Demonstrate their knowledge of Macro and Micro Economics, Accounting, Financial Methods and Mathematical Statistics.
b) Demonstrate their understanding of the content covered in the first two professional actuarial science examinations.
3. Demonstrate proficiency in the use of computer software such as EXCEL, and R/RStudio or Python. Students will also be able to do basic computer programming for data visualization and mathematical modeling.
4. a) Demonstrate the ability to communicate the results of quantitative analysis effectively, both in writing and orally.
b) Demonstrate the ability to work cooperatively with others.
Requirements for a major
Total Courses Required
Ten
Core Courses
MATH 151, MATH 152, ECON 100
Other Required Courses
- One mathematics course at the 200 level (MATH 223, MATH 247, MATH 251 or MATH 270)
- MATH 331 & MATH 332
- MATH 441 & MATH 442
- Either ECON 294 or ECON 295
- One elective from the following courses: MATH 336, MATH 341, MATH 348, ECON 385, ECON 393 or ECON 450
Number 300 & 400 Level Courses
Five
Senior Requirement & Capstone Experience
The senior requirement consists of MATH 494 or MATH 495.
Additional Information
MATH 332 and MATH 442 are one-half credit courses and will be offered in the same semester as MATH 331 and MATH 441 respectively.
A student may not major in both Actuarial Science and in Mathematics. A student may not major in Actuarial Science and minor in Mathematics.
Recent changes in the major
Add three 300-level courses in the elective course list and one course in the Senior requirement and Capstone Experience.
Writing in the major
Actuarial Science majors develop their writing expertise by taking the classes Math 223, or Foundations of Advanced Mathematics, and Math 270, Linear Algebra or Math 336, An introduction to Financial Engineering. In lower level courses, significant emphasis will be placed on what it means to express mathematical thoughts and concepts through writing. In Math 336, emphasis will be placed on writing the project process and analyze the financial data by applying the theorems and techniques learned in class. Students are expected to explain the core mathematical tools and fundamental concepts of financial engineering in their papers. Discussion of writing in Actuarial Science takes place throughout the Actuarial Science curriculum, but receives special emphasis in these courses, where students have many opportunities to revise their writing after receiving feedback from the instructor and to integrate mathematical and financial symbols and prose writing, in the form of a cogent argument. The writing in the major requirement in Actuarial Science culminates in the math senior seminar, where students produce an expository paper of approximately twenty pages.
Total Courses Required
Ten
Core Courses
- MATH 151
- MATH 152
- ECON 100
- MATH 331/BUSA 331
- MATH 332/BUSA 332 (0.5 credit)
- MATH 441
- MATH 442 (0.5 credit)
Other Required Courses
Two (one from each list):
- One mathematics course at the 200 level (MATH 223, MATH 247 or MATH 270)
- One course in economics, finance or business at the 200 level (ECON 294, ECON 295, FIN 220 or BUSA 240)
One from the following courses (listed by area of specialization)
- Data Analytics: MATH 261, MATH 348, BUSA 210, BUSA 310, FIN 355, or CSC 370
- Finance: MATH 336/BUSA 336, FIN/ECON 293
- Statistics: MATH 341, ECON 385, ECON 450, FIN/ECON 451
- Actuarial Mathematics: MATH 451, MATH 452
Number 300 & 400 Level Courses
Five
Senior Requirement & Capstone Experience
The senior requirement consists of MATH 494.
Additional Information
MATH 332/BUSA 332 and MATH 442 are one-half credit courses and will be offered in the same semester as MATH 331/BUSA 331 and MATH 441 respectively.
A student may not major in both Actuarial Science and in Mathematics. A student may not major in Actuarial Science and minor in Mathematics.
Writing in the major
Actuarial Science majors develop their writing expertise by taking the classes MATH 270, Linear Algebra, or MATH/BUSA 336, Quantitative Risk Analysis. In lower level courses, significant emphasis will be placed on what it means to express mathematical thoughts and concepts through writing. In MATH/BUSA336, emphasis will be placed on writing the project process and analyzing the financial data by applying the theorems and techniques learned in class. Students are expected to explain the core mathematical tools and fundamental concepts of risk analysis in their papers. Discussion of writing in Actuarial Science takes place throughout the Actuarial Science curriculum, but receives special emphasis in these courses, where students have many opportunities to revise their writing after receiving feedback from the instructor and to integrate mathematical and financial symbols and prose writing, in the form of a cogent argument. The writing in the major requirement in Actuarial Science culminates in the Actuarial and Risk Management senior seminar, where students produce an expository paper of approximately twenty pages.
Total Courses Required
Ten
Core Courses
MATH 151, MATH 152, MATH 223, MATH 251, MATH 270, MATH 495
Other Required Courses
Students planning graduate work in mathematics should include MATH 361 and MATH 371. Students concentrating in actuarial mathematics should include MATH 331 and MATH 441. MATH 341 or MATH 348 is also highly recommended.
Number 300 & 400 Level Courses
Four (not including MATH 495)
Senior Requirement & Capstone Experience
MATH 495
Writing in the major
Mathematics majors develop their writing expertise by taking the classes Math 223, Foundations of Advanced Mathematics, and Math 270, Linear Algebra. In these courses, significant emphasis will be placed on what it means to express mathematical thoughts and concepts through writing. Discussion of writing in Mathematics takes place throughout the mathematics curriculum, but receives special emphasis in these courses, where students have many opportunities to revise their writing after receiving feedback from the instructor and to integrate mathematical symbols and prose writing, in the form of a cogent argument. The writing in the major requirement in Mathematics culminates in the senior seminar, where students produce an expository paper of approximately twenty pages.
Requirements for a minor
Total Courses Required
5
Core Courses
MATH 141, MATH 151, MATH 341. (ECON 350, BIO 275, PSY 214 may be substituted for MATH 141)
Other Required Courses
Two courses from: MATH 247, MATH 261, MATH 340, MATH 348, MATH 441, MATH 423, ECON 450.
Number 300 & 400 Level Courses
2
Total Courses Required
5
Core Courses
MATH 141 or PSY 214 or ECON 350 or BIO 375, MATH 261 or CSC 370 or CSC 380, MATH 341 or ECON 385 or ECON 450, CSC 121,CSC 125.
Other Required Courses
Number 300 & 400 Level Courses
Total Courses Required
Five
Core Courses
MATH 151, MATH 152, MATH 223, MATH 270
Other Required Courses
Number 300 & 400 Level Courses
One
Courses in Honors
Course Description
Interdisciplinary, discussion-based seminars that provide students with a foundation in the theory and methods of: science and mathematics; These interdisciplinary seminars offer students opportunities via discussion and/or writing to explore implications, connections, and other perspectives (e.g., philosophy, ethics, law, arts, policy, history, politics, medicine/health, the other sciences, etc.). By design they explicitly connect two areas of the 91传媒 curriculum, i.e., two disciplines within the CLAS, the CLAS with the SBL, or the CLAS with the Creative School. This course will encourage the understanding of quantitative and mathematical concepts, representational formats, and methodologies of multiple disciplines; the evaluation of quantitative evidence and arguments; the use of quantitative information to make decisions; and the use of [problem-solving, laboratory experiments, projects] for deeper learning. These courses are open to all 91传媒 students, although priority is given to students enrolled in the Honor Scholar Program. Students in the Honor Scholar Program take one course each in AH, SM, and SS; each of these courses can be used to fulfill 91传媒's distribution requirements. May not be taken Pass/Fail.
Distribution Area
Science and Math
Credits
1 course
Course Description
Interdisciplinary, discussion-based seminars that provide students with a foundation in the theory and methods of the social sciences. These interdisciplinary seminars offer students opportunities via discussion and/or writing to explore implications, connections, and other perspectives (e.g., philosophy, ethics, law, arts, policy, history, politics, medicine/health, the other sciences, etc.). By design they explicitly connect two areas of the 91传媒 curriculum, i.e., two disciplines within the CLAS, the CLAS with the SBL, or the CLAS with the Creative School. This course will encourage the understanding of quantitative and mathematical concepts, representational formats, and methodologies of multiple disciplines; the evaluation of quantitative evidence and arguments; the use of quantitative information to make decisions; and the use of [problem-solving, laboratory experiments, projects] for deeper learning. These courses are open to all 91传媒 students, although priority is given to students enrolled in the Honor Scholar Program. Students in the Honor Scholar Program take one course each in AH, SM, and SS; each of these courses can be used to fulfill 91传媒's distribution requirements. May not be taken Pass/Fail.
Distribution Area
Social Science
Credits
1 course
Courses in Mathematics
Course Description
An introduction to the concepts of discrete mathematics with an emphasis on problem solving and computation. Topics are selected from Boolean algebra, combinatorics, functions, graph theory, matrix algebra, number theory, probability, relations and set theory. This course may have a laboratory component.
Distribution Area
Science and Math
Credits
1 course
Course Description
An introduction to the concepts of discrete mathematics with an emphasis on problem solving and computation. Topics are selected from Boolean algebra, combinatorics, functions, graph theory, matrix algebra, number theory, probability, relations and set theory. This course may have a laboratory component.
Distribution Area
Science and Math
Credits
1 course
Course Description
Extensive review of topics from algebra, trigonometry, analytic geometry, graphing and theory of equations. A study of functions, limits, continuity and differentiability of algebraic and transcendental functions with applications. Not open to students with credit in MATH 151 or any higher level calculus course.
Credits
1 course
Course Description
A continuation of MATH 135. Topics include further study of differentiation, integration of algebraic and transcendental functions with applications, and techniques of integration. Completion of this course is equivalent to completing MATH 151 and is adequate preparation for any course requiring MATH 151.
Distribution Area
Science and Math
Prerequisites
MATH 135.
Credits
1 course
Course Description
A continuation of MATH 135. Topics include further study of differentiation, integration of algebraic and transcendental functions with applications, and techniques of integration. Completion of this course is equivalent to completing MATH 151 and is adequate preparation for any course requiring MATH 151.聽
Distribution Area
Science and Math
Prerequisites
MATH 135.
Credits
1 course
Course Description
This course introduces students to elementary probability and data analysis via visual presentation of data, descriptive statistics and statistical inference. Emphasis will be placed on applications with examples drawn from a wide range of disciplines in both physical and behavioral sciences and humanities. Topics of statistical inference include: confidence intervals, hypothesis testing, regression, correlation, contingency tales, goodness of fit and ANOVA. The course will also develop familiarity with the most commonly encountered tables for probability distributions: binomial, normal, chi-squared, student-t and F. Students who have completed or are concurrently enrolled in ECON 350 will only receive one-half credit for MATH 141.
Distribution Area
Science and Math
Credits
1 course
Course Description
This course introduces students to elementary probability and data analysis via visual presentation of data, descriptive statistics and statistical inference. Emphasis will be placed on applications with examples drawn from a wide range of disciplines in both physical and behavioral sciences and humanities. Topics of statistical inference include: confidence intervals, hypothesis testing, regression, correlation, contingency tales, goodness of fit and ANOVA. The course will also develop familiarity with the most commonly encountered tables for probability distributions: binomial, normal, chi-squared, student-t and F.聽Students who have completed or are concurrently enrolled in ECON 350 will only receive one-half credit for MATH 141.
Distribution Area
Science and Math
Credits
1 course
Course Description
This interdisciplinary course will be an engaging and lively look into modeling of phenomena (like voting theory, game theory, traveling salesman problem, population growth/decay etc.) in natural and social sciences. This course will emphasize relationships between the world in which we live and mathematics and is aimed to develop one's mathematical and problem-solving skills in the process. Topics covered will include Modeling Change, Modeling Process and Proportionality, Model Fitting, Probabilistic Modeling, Modeling with Decision Theory, Optimization of Discrete Models, Game Theory and Modeling Using Graph Theory. It will be beneficial for the student to have knowledge in Algebra and Trigonometry for this course.
Distribution Area
Science and Math
Credits
1 course
Course Description
A study of functions, limits, continuity, differentiation and integration of algebraic and transcendental functions with elementary applications.
Distribution Area
Science and Math
Credits
1 course
Course Description
A study of functions, limits, continuity, differentiation and integration of algebraic and transcendental functions with elementary applications.
Distribution Area
Science and Math
Credits
1 course
Course Description
Techniques of integration, parametric equations, infinite series and an introduction to the calculus of several variables.
Distribution Area
Science and Math
Prerequisites
MATH 136 or MATH 151.
Credits
1 course
Course Description
Techniques of integration, parametric equations, infinite series and an introduction to the calculus of several variables.聽
Distribution Area
Science and Math
Prerequisites
MATH 136 or MATH 151.
Credits
1 course
Course Description
An on-campus course offered during the Winter or May term. May be offered for .5 course credits or as a co-curricular (0 credit). Counts toward satisfying the Extended Studies requirement.
Credits
0.5 course
Course Description
Student-initiated independent project under faculty guidance. Offered as a co-curricular (0 credit) Extended Studies experience.
Course Description
The basic approach in this course will be to present mathematics in a more humanistic manner and thereby provide an environment where students can discover, on their own, the quantitative ideas and mathematical techniques used in decision-making in a diversity of disciplines. Students work with problems obtained from industry and elsewhere.
Credits
1 course
Course Description
An introduction to concepts and methods that are fundamental to the study of advanced mathematics. Emphasis is placed on the comprehension and the creation of mathematical prose, proofs, and theorems. Topics are selected from Boolean algebra, combinatorics, functions, graph theory, matrix algebra, number theory, probability, relations, and set theory.
Distribution Area
Science and Math
Prerequisites
MATH 123 or MATH 136 or MATH 151.
Credits
1 course
Course Description
An introduction to concepts and methods that are fundamental to the study of advanced mathematics. Emphasis is placed on the comprehension and the creation of mathematical prose, proofs, and theorems. Topics are selected from Boolean algebra, combinatorics, functions, graph theory, matrix algebra, number theory, probability, relations, and set theory.聽
Distribution Area
Science and Math
Prerequisites
MATH 123 or MATH 136 or MATH 151.
Credits
1 course
Course Description
This course introduces students to the theory behind standard statistical procedures. The course presumes a working knowledge of single-variable calculus on the part of the student. Students are expected to derive and apply theoretical results as well as carry out standard statistical procedures. Topics covered will include moment-generating functions, Gamma distributions, Chi-squared distributions, t-distributions, and F-distributions, sampling distributions and the Central Limit Theorem, point estimation, confidence intervals, and hypothesis testing.
Distribution Area
Science and Math
Prerequisites
MATH 136 or MATH 151.
Credits
1 course
Course Description
This course introduces students to the theory behind standard statistical procedures. The course presumes a working knowledge of single-variable calculus on the part of the student. Students are expected to derive and apply theoretical results as well as carry out standard statistical procedures. Topics covered will include moment-generating functions, Gamma distributions, Chi-squared distributions, t-distributions, and F-distributions, sampling distributions and the Central Limit Theorem, point estimation, confidence intervals, and hypothesis testing.聽
Distribution Area
Science and Math
Prerequisites
MATH 136 or MATH 151.
Credits
1 course
Course Description
An introduction to the calculus of several variables. Topics include vectors and solid analytic geometry, multidimensional differentiation and integration, and a selection of applications.
Distribution Area
Science and Math
Prerequisites
MATH 152.
Credits
1 course
Course Description
An introduction to the calculus of several variables. Topics include vectors and solid analytic geometry, multidimensional differentiation and integration, and a selection of applications.聽
Distribution Area
Science and Math
Prerequisites
MATH 152.
Credits
1 course
Course Description
This course provides an introduction to the field of data science from data to knowledge and gives students' hands-on experience with tools and methods. This course focuses on using computational, statistical, and mathematical tools for data acquisition, exploration, manipulation, visualization, analysis, modeling, and classification, as well as the communication of results.
Prerequisites
MATH 141 (or equivalent) or permission of instructor.
Credits
1 course
Course Description
This course provides an introduction to the field of data science from data to knowledge and gives students' hands-on experience with tools and methods. This course focuses on using computational, statistical, and mathematical tools for data acquisition, exploration, manipulation, visualization, analysis, modeling, and classification, as well as the communication of results.聽
Prerequisites
MATH 141 (or equivalent) or permission of instructor.
Credits
1 course
Course Description
Vector spaces, linear transformations, matrices, determinants, eigenvalues and eigenvectors and applications.
Prerequisites
MATH 152 or permission of instructor.
Credits
1 course
Course Description
Vector spaces, linear transformations, matrices, determinants, eigenvalues and eigenvectors and applications.聽This course will encourage the understanding of quantitative and mathematical concepts, representational formats, and methodologies of mathematics; the evaluation of quantitative evidence and arguments; the use of quantitative information to make decisions; and the use of problem-solving, laboratory experiments, or projects for deeper learning.
Prerequisites
MATH 152 or permission of instructor.
Credits
1 course
Course Description
Vector spaces, linear transformations, matrices, determinants, eigenvalues and eigenvectors and applications.聽 This course will encourage the logical development of argument, clear and precise diction, and a coherent prose style; the development of expository writing skills as they apply to mathematics; and the responsible, appropriate, and effective use of sources and special or technical language.
Prerequisites
MATH 152 or permission of instructor.
Credits
1 course
Course Description
Selections from advanced plane, differential, non-Euclidean or projective geometry.
Prerequisites
either MATH 223 or MATH 270.
Credits
1 course
Course Description
Algorithmic Graph Theory is that branch of Mathematics that deals with mathematical structures that are used to model pairwise relations between objects from a certain collection, together with algorithms used to manipulate these models. Algorithmic Graph Theory is used to model many types of relations and process dynamics in physical, biological and social systems. This course helps students develop the mathematical underpinnings of the theory of graphs and algorithms, a branch of discrete mathematics. This course provides an excellent background to an exciting area of mathematics that has applications in fields like computer science, economics, and engineering.
Distribution Area
Science and Math
Prerequisites
CSC 233, foundations of computation or MATH 270, linear algebra or MATH 223, foundations of advanced mathematics. It will be beneficial for the student to be fluent in a programming language for this course.
Credits
1 course
Course Description
Dive into the world of financial mathematics and unlock the power of money over time. This course equips students with advanced mathematical skills to navigate complex financial landscapes. Master the art of calculating the time value of money, analyzing investment opportunities, and understanding the intricacies of loans and bonds. Explore yield curves, portfolio management, and asset-liability matching techniques used by financial professionals. Gain hands-on experience with real-world applications in investment analysis, capital budgeting, and risk management. Upon completing this course, students will be able to navigate financial decisions confidently and strategically in an ever-evolving economic landscape.
Prerequisites
ECON 100 and MATH 136 or MATH 151 or ECON 375
Credits
1 course
Course Description
This problem-solving seminar emphasizes professional development in actuarial science and risk management. Students engage with complex actuarial problems, enhancing their understanding of the field through practical applications and exam preparation resources. The seminar introduces financial instruments, explores determinants of interest rates, and examines methods to approximate the effects of interest rate changes. Participants also analyze and discuss actuarial risk management articles. This course is particularly beneficial for students preparing for the Financial Mathematics (FM) actuarial exam.
Prerequisites
MATH/BUSA 331 which may be taken concurrently.
Credits
0.5 course
Course Description
This course in Quantitative Risk Analysis provides students with a comprehensive interdisciplinary foundation in quantitative techniques for financial risk assessment and management. The curriculum encompasses the principles of fixed-income securities, Modern Portfolio Theory (MPT) and advanced topics like the Black-Scholes formula and the Binomial Tree method for derivatives pricing. The course emphasizes practical skills in identifying, calculating, and mitigating risks associated with fixed-income securities, equities, options, and futures. Students will work on projects that simulate real-world scenarios,gaining experience in managing the interest rate risk of fixed-income securities by controlling durations, applying MPT to create efficient portfolios, using stock index futures to manage market exposure, and leveraging stock options to hedge individual stock risks.
Prerequisites
MATH 136 or MATH 151 or ECON 375, ECON 100, and either MATH 141 or ECON 350.
Credits
1 course
Course Description
(Cross-listed with BUSA 336) This course in Quantitative Risk Analysis provides students with a comprehensive interdisciplinary foundation in quantitative techniques for financial risk assessment and management. The curriculum encompasses the principles of fixed-income securities, Modern Portfolio Theory (MPT) and advanced topics like the Black-Scholes formula and the Binomial Tree method for derivatives pricing. The course emphasizes practical skills in identifying, calculating, and mitigating risks associated with fixed-income securities, equities, options, and futures. Students will work on projects that simulate real-world scenarios, gaining experience in managing the interest rate risk of fixed-income securities by controlling durations, applying MPT to create efficient portfolios, using stock index futures to manage market exposure, and leveraging stock options to hedge individual stock risks.聽This course will encourage the logical development of argument, clear and precise diction, and a coherent prose style; the development of expository writing skills as they apply to business analytics; and the responsible, appropriate, and effective use of sources and special or technical language.
Prerequisites
MATH 136 or MATH 151 or ECON 375, ECON 100, and either MATH 141 or ECON 350.
Credits
1 course
Course Description
Topics in statistics.
Credits
1 course
Course Description
Topics in statistics. This course will encourage the understanding of quantitative and mathematical concepts, representational formats, and methodologies of mathematics; the evaluation of quantitative evidence and arguments; the use of quantitative information to make decisions; and the use of problem-solving, laboratory experiments, and/or projects for deeper learning.
Credits
1 course
Course Description
Topics in statistics. This course will encourage the understanding of quantitative and mathematical concepts, representational formats, and methodologies of mathematics; the evaluation of quantitative evidence and arguments; the use of quantitative information to make decisions; and the use of problem-solving, laboratory experiments, and/or projects for deeper learning.
Credits
1 course
Course Description
This course is designed to provide students with a solid overview of basic and advanced topics in regression analysis. This course mainly covers the simple and multiple linear regression models--method of least squares, model and assumptions; testing hypotheses; estimation of parameters and associated standard errors; correlations between parameter estimates; standard error of predicted response values; inverse prediction; regression through the origin; matrix approach; extra sum of squares principle as used in model building; partial F-tests and sequential F-tests. More advanced topics in regression analysis, such as selecting the 'best' regression equation, classical approaches: all possible regressions; backward elimination; forward selection; stepwise regression; indicator (dummy) variables in regression also introduces in this course. Additionally, nonlinear (binary) logistic regression model with qualitative independent variables discusses in this course. A statistical computing package, such as R, is used throughout the course.
Distribution Area
Science and Math
Prerequisites
MATH 141 or ECON 350 or PSY 214 or BIO 275
Credits
1 course
Course Description
This course is designed to provide students with a solid overview of basic and advanced topics in regression analysis. This course mainly covers the simple and multiple linear regression models--method of least squares, model and assumptions; testing hypotheses; estimation of parameters and associated standard errors; correlations between parameter estimates; standard error of predicted response values; inverse prediction; regression through the origin; matrix approach; extra sum of squares principle as used in model building; partial F-tests and sequential F-tests. More advanced topics in regression analysis, such as selecting the 'best' regression equation, classical approaches: all possible regressions; backward elimination; forward selection; stepwise regression; indicator (dummy) variables in regression also introduces in this course. Additionally, nonlinear (binary) logistic regression model with qualitative independent variables discusses in this course. A statistical computing package, such as R, is used throughout the course.聽
Distribution Area
Science and Math
Prerequisites
MATH 141 or ECON 350 or PSY 214 or BIO 275
Credits
1 course
Course Description
This course is designed to provide students with an introduction to statistical computing using RStudio. This course will have two components. In the first part of the course, students will learn data manipulations, data structures, matrix manipulation, database operation, and functions. In the second part of the course, students will learn statistical computing topics including simulation studies and Monte Carlo methods, numerical optimization, Bootstrap resampling methods, and visualization. Students will be introduced to some packages and technologies that are useful for statistical computing. Through producing numerical summaries and creating customized graphs, students will be able to discuss the results obtained from their analyses and to generate dynamic and reproducible documents.
Distribution Area
Science and Math
Prerequisites
Math 141 (or ECON 350/BIO 375/PSY 214) and Math 151 (or MATH 135-136).
Credits
1 course
Course Description
This course is designed to provide students with an introduction to statistical computing using RStudio. This course will have two components. In the first part of the course, students will learn data manipulations, data structures, matrix manipulation, database operation, and functions. In the second part of the course, students will learn statistical computing topics including simulation studies and Monte Carlo methods, numerical optimization, Bootstrap resampling methods, and visualization. Students will be introduced to some packages and technologies that are useful for statistical computing. Through producing numerical summaries and creating customized graphs, students will be able to discuss the results obtained from their analyses and to generate dynamic and reproducible documents.聽
Distribution Area
Science and Math
Prerequisites
Math 141 (or ECON 350/BIO 375/PSY 214) and Math 151 (or MATH 135-136).
Credits
1 course
Course Description
A study of the theory of limits, continuity, differentiation, integration, sequences and series.
Prerequisites
MATH 152 and either MATH 223 or MATH 270.
Credits
1 course
Course Description
Equations of the first degree, linear differential equations, systems of equations with matrix methods and applications. Selected topics from power series solutions, numerical methods, boundary-value problems and non-linear equations.
Prerequisites
MATH 152 and MATH 270.
Credits
1 course
Course Description
Analysis of algorithms frequently used in mathematics, engineering and the physical sciences. Topics include sources of errors in digital computers, fixed point iteration, interpolation and polynomial approximation, numerical differentiation and integration, direct and iterative methods for solving linear systems, and iterative methods for nonlinear systems. Numerical experiments will be conducted using FORTRAN, C, or another appropriate high-level language.
Prerequisites
MATH 270 and CSC 121 or permission of instructor.
Credits
1 course
Course Description
Analysis of algorithms frequently used in mathematics, engineering and the physical sciences. Topics include sources of errors in digital computers, fixed point iteration, interpolation and polynomial approximation, numerical differentiation and integration, direct and iterative methods for solving linear systems, and iterative methods for nonlinear systems. Numerical experiments will be conducted using FORTRAN, C, or another appropriate high-level language. This course will help students develop the presentation of logical arguments and refutation; the ability to distinguish and identify important substantive arguments; the ability to skillfully analyze, evaluate, and integrate supporting material; the selection and implementation of effective presentation style; the ability to adapt the manner of delivery to specific audiences and situations; the demonstration of critical listening skills; the demonstration of effective and reflective listening; and the knowledge of the ethical obligations of speakers, discussants, and listeners.
Prerequisites
MATH 270 and CSC 121聽or permission of instructor.
Credits
1 course
Course Description
The structure of groups, group homomorphisms and selected topics from other algebraic structures, such as rings, fields and modules.
Prerequisites
MATH 270.
Credits
1 course
Course Description
Divisibility and factorization of integers, linear and quadratic congruences. Selected topics from diophantine equations, the distribution of primes, number-theoretic functions, the representation of integers and continued fractions.
Prerequisites
MATH 270 or permission of instructor.
Credits
1 course
Course Description
A. Actuarial Mathematics; B. Algebra; C. Analysis; D. Foundations of Mathematics; E. Geometry; F. Applied Mathematics; G. Special Topics.
Credits
1 course
Course Description
A. Actuarial Mathematics; B. Algebra; C. Analysis; D. Foundations of Mathematics; E. Geometry; F. Applied Mathematics; G. Special Topics.
Credits
1 course
Course Description
Topics selected from linear and dynamic programming, network analysis, game theory and queueing theory are applied to problems in production, transportation, resource allocation, scheduling and competition.
Prerequisites
MATH 270.
Credits
1 course
Course Description
This advanced probability course integrates rigorous mathematical theory with practical applications. Students will explore combinatorial analysis, conditional probability, discrete and continuous univariate and multivariate distributions, random variables, moments, covariance, independence, order statistics, and the Central Limit Theorem. The course emphasizes developing quantitative tools for risk assessment applicable across various fields.
Prerequisites
MATH 152
Credits
1 course
Course Description
The seminar will include the topics of multivariate distributions, order statistics, the law of large numbers, basic insurance policies, frequency of loss, frequency distribution, severity, severity distribution, characteristics of an insurable risk, measurement of risk, economics risk, expected value of loss, loss distribution, premium payment, claim payment distribution, limits on policy benefit (deductible, maximum, benefit limits) and role of actuaries. After studying, students will be able to demonstrate a solid foundation in probability by their ability to solve a variety of basic and advanced actuarial practical problems.
Prerequisites
MATH 441 which may be taken concurrently.
Credits
0.5 course
Course Description
A. Actuarial Mathematics; B. Algebra; C. Analysis; D. Foundations of Mathematics; E. Geometry; F. Probability and Statistics; G. Applied Mathematics; H. Special Topics.
Prerequisites
permission of instructor. May be repeated for credit with different topics.
Credits
1 course
Course Description
A. Actuarial Mathematics; B. Algebra; C. Analysis; D. Foundations of Mathematics; E. Geometry; F. Probability and Statistics; G. Applied Mathematics; H. Special Topics.
Prerequisites
permission of instructor. May be repeated for credit with different topics.
Credits
1 course
Course Description
A. Actuarial Mathematics; B. Algebra; C. Analysis; D. Foundations of Mathematics; E. Geometry; F. Probability and Statistics; G. Applied Mathematics; H. Special Topics.
Prerequisites
permission of instructor. May be repeated for credit with different topics.
Credits
1 course
Course Description
A. Actuarial Mathematics; B. Algebra; C. Analysis; D. Foundations of Mathematics; E. Geometry; F. Probability and Statistics; G. Applied Mathematics; H. Special Topics.
Prerequisites
permission of instructor. May be repeated for credit with different topics.
Credits
1 course
Course Description
A. Actuarial Mathematics; B. Algebra; C. Analysis; D. Foundations of Mathematics; E. Geometry; F. Probability and Statistics; G. Applied Mathematics; H. Special Topics.
Prerequisites
permission of instructor. May be repeated for credit with different topics.
Credits
1 course
Course Description
This senior capstone course emphasizes group work and discussions, allowing students to apply actuarial models, risk analysis techniques, and risk management strategies to various insurance domains, including Auto, Health, Life, and Property & Casualty, etc. Students will collaborate in groups on projects utilizing public data from the Society of Actuaries, Casualty Actuarial Society, and other industry resources to model, price insurance products, mitigate risks through risk identification, assessment, and control measures, and develop risk management plans. Through these projects, students will leverage statistical, analytical, and data analytics methods to conduct research on real-world datasets, gaining practical experience in addressing complex actuarial, risk management, and risk financing challenges while enhancing problem-solving, teamwork, communication, and risk management skills essential for future careers in the insurance and financial industry.
Prerequisites
MATH/BUSA 331 or MATH/BUSA 336, MATH 441, and one upper-level statistics course from the list (MATH 341, MATH 348, BUSA 305, ECON 385, ECON 450, FIN 451).
Credits
1 course
Course Description
Advanced topics considered individually or in small groups. Open only to senior Mathematics majors or by permission of the Department of Mathematics.
Credits
1 course
Course Description
This course will encourage the understanding of quantitative and mathematical concepts, representational formats, and methodologies of mathematics; the evaluation of quantitative evidence and arguments; the use of quantitative information to make decisions; and the use of problem-solving, laboratory experiments, and/or projects for deeper learning.
Credits
1 course