The actuarial science minor prepares students for work in insurance companies, investment firms, banks, for the government, and in the health care industry where there is a need to analyze the financial consequences of risk. The actuarial science minor prepares students for two exams administered by the Society of Actuaries. Those exams are Exam P: Probability, which assesses a candidate's knowledge of the fundamental probability tools for quantitatively assessing risk, and Exam FM: Financial Mathematics, which assesses a candidate's understanding of the fundamental concepts of financial mathematics and how those concepts are applied in a variety of areas.
This is the first in a two-course sequence intended for students majoring in mathematics, science, or engineering. It emphasizes the understanding of concepts, and using them to solve physical problems. The course covers functions, limits, continuity, the derivative, rules of differentiation, applications of the derivative, Riemann sums, definite integrals, and indefinite integrals. (Prerequisite: A- or better in MATH-111 or A- or better in ((NMTH-260 or NMTH-272 or NMTH-275) and NMTH-220) or a math placement exam score greater than or equal to 70 or department permission to enroll in this class.) Lecture 6 (Fall, Spring, Summer).
Calculus A/Calculus B
Choose one of the following:
Project-Based Calculus II
This is the second in a two-course sequence intended for students majoring in mathematics, science, or engineering. It emphasizes the understanding of concepts, and using them to solve physical problems. The course covers techniques of integration including integration by parts, partial fractions, improper integrals, applications of integration, representing functions by infinite series, convergence and divergence of series, parametric curves, and polar coordinates. (Prerequisites: C- or better in (MATH-181 or MATH-173 or 1016-282) or (MATH-171 and MATH-180) or equivalent course(s).) Lecture 6 (Fall, Spring, Summer).
This is the third course in three-course sequence (COS-MATH-171, -172, -173). The course includes sequences, convergence and divergence of series, representations of functions by infinite series, curves defined by parametric equations, and polar coordinates. Also included are applications of calculus to curves expressed in parametric and polar form. (Prerequisites: C- or better in MATH-172 or equivalent course.) Lecture 5 (Fall, Spring).
This course is principally a study of the calculus of functions of two or more variables, but also includes the study of vectors, vector-valued functions and their derivatives. The course covers limits, partial derivatives, multiple integrals, and includes applications in physics. Credit cannot be granted for both this course and MATH-221. (Prerequisite: C- or better MATH-173 or MATH-182 or MATH-182A or equivalent course.) Lecture 3 (Fall, Spring, Summer).
Probability and Statistics I
This course introduces sample spaces and events, axioms of probability, counting techniques, conditional probability and independence, distributions of discrete and continuous random variables, joint distributions (discrete and continuous), the central limit theorem, descriptive statistics, interval estimation, and applications of probability and statistics to real-world problems. A statistical package such as Minitab or R is used for data analysis and statistical applications. (Prerequisites: MATH-173 or MATH-182 or MATH 182A or equivalent course.) Lecture 3 (Fall, Spring, Summer).
Probability and Statistics II
This course covers basic statistical concepts, sampling theory, hypothesis testing, confidence intervals, point estimation, and simple linear regression. The statistical software package MINITAB will be used for data analysis and statistical applications. (Prerequisites: STAT-251 or MATH-251 or equivalent course.) Lecture 3 (Fall, Spring).
Required Courses (Group I)
This course provides challenging problems in probability whose solutions require a combination of skills that one acquires in a typical mathematical statistics curriculum. Course work synthesizes basic, essential problem-solving ideas and techniques as they apply to actuarial mathematics and the first actuarial exam. (Prerequisites: MATH-251 or 1016-345 or equivalent course.) Lecture 3 (Spring).
Topics in Math Finance
This course examines concepts in finance from a mathematical viewpoint. It includes topics such as the Black-Scholes model, financial derivatives, the binomial model, and an introduction to stochastic calculus. Although the course is mathematical in nature, only a background in calculus (including Taylor series) and basic probability is assumed; other mathematical concepts and numerical methods are introduced as needed. (Prerequisites: (MATH-219 or MATH-221 or MATH-221H) and (STAT-145 or STAT-145H or MATH-251) or equivalent courses.) Lecture 3 .
Choose at least one of the following:
An introduction to the way in which corporations report their financial performance to interested stakeholders such as investors and creditors. Coverage of the accounting cycle, generally accepted accounting principles, and analytical tools help students become informed users of financial statements. (This course is available to RIT degree-seeking undergraduate students.) Lecture 3 (Fall, Spring, Summer).
Basic course in financial management. Covers business organization, time value of money, valuation of securities, capital budgeting decision rules, risk-return relation, Capital Asset Pricing Model, financial ratios, global finance, and working capital management. (Prerequisites: (ECON-101 or ECON-201) and ACCT-110 and (STAT-145 or STAT-251 or CQAS-251 or MATH-251 or MATH-252 or STAT-205) or equivalent courses.) Lecture 3 (Fall, Spring, Summer).
Econometrics I provides students with the opportunity to develop their skills in applied regression analysis. It covers various regression estimation techniques, data preparation and transformation, and the interpretation of regression results. There is particular emphasis on the dangers of misuse of regression techniques. The course covers regression analysis for both cross-sectional and time series data. (Prerequisites: ECON-101 or completion of one (1) 400 or 500 level ECON course and (MATH-171 or 1016-171T or MATH-181 or MATH-181A) and (STAT-145 or STAT/CQAS-251 or MATH-251 or STAT-205 or equivalent courses.) Lecture 3 (Fall, Spring).
This course covers regression techniques with applications to the type of problems encountered in real-world situations. It includes use of the statistical software SAS. Topics include a review of simple linear regression, residual analysis, multiple regression, matrix approach to regression, model selection procedures, and various other models as time permits. (Prerequisites: MATH-241 and MATH-252 or equivalent courses.) Lecture 3 (Spring).
Introduction to Time-series
This course is a study of the modeling and forecasting of time series. Topics include ARMA and ARIMA models, autocorrelation function, partial autocorrelation function, detrending, residual analysis, graphical methods, and diagnostics. A statistical software package is used for data analysis. (Prerequisites: STAT-205 or MATH-252 or equivalent courses.) Lecture 3 (Spring).
Mathematical Statistics I
This course provides a brief review of basic probability concepts and distribution theory. It covers mathematical properties of distributions needed for statistical inference. (Prerequisites: STAT-205 or MATH-252 or equivalent courses.) Lecture 3 (Fall).
Mathematical Statistics II
This course is a continuation of STAT-405 covering classical and Bayesian methods in estimation theory, chi-square test, Neyman-Pearson lemma, mathematical justification of standard test procedures, sufficient statistics, and further topics in statistical inference. (Prerequisites: STAT-405 or equivalent course.) Lecture 3 (Spring).
The use of statistical models in computer algorithms allows users to make decisions and predictions, and to perform tasks that traditionally require human cognitive abilities. Data mining and Machine learning are interdisciplinary fields at the intersection of statistics, computer science, applied mathematics which develops such statistical models and interweaves them with computer algorithms. It underpins many modern technologies, such as speech recognition, Internet search, bioinformatics and computer vision. The course will provide an introduction to Statistical Machine Learning and its core models and algorithms. (Prerequisites: STAT-305 or equivalent courses.) Lecture 3 (Spring).
This course explores Poisson processes and Markov chains with an emphasis on applications. Extensive use is made of conditional probability and conditional expectation. Further topics, such as renewal processes, Brownian motion, queuing models and reliability are discussed as time allows. (Prerequisites: (MATH-241 or MATH-241H) and MATH-251 or equivalent courses.) Lecture 3 (Spring).
* At least two courses must be taken at the 300-level or higher.
† Students must complete two courses from Group III. Students may elect to complete additional course from Group II to satisfy this requirement.