Actuarial Science Minor
Overview for Actuarial Science Minor
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: the Exam P: Probability, which assesses a candidate's knowledge of the fundamental probability tools for quantitatively assessing risk, and the 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.
Notes about this minor:
- Posting of the minor on the student's academic transcript requires a minimum GPA of 2.0 in the minor.
- Notations may appear in the curriculum chart below outlining pre-requisites, co-requisites, and other curriculum requirements (see footnotes).
- At least nine semester credit hours of the minor must consist of specific courses not required by the student’s degree program.
The plan code for Actuarial Science Minor is ACTS-MN.
Curriculum for 2024-2025 for Actuarial Science Minor
Current Students: See Curriculum Requirements
| Course | ||
|---|---|---|
| Prerequisites | ||
| Choose one of the following course sequences: | ||
| MATH-181 | Calculus I |
|
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. (Prerequisites: MATH-111 or (NMTH-220 and NMTH-260 or NMTH-272 or NMTH-275) or equivalent courses with a minimum grade of B-, or a score of at least 60% on the RIT Mathematics Placement Exam.) Lecture 4 (Fall, Spring). | ||
| MATH-182 | Calculus II |
|
This is the second in a two-course sequence. 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-181A or equivalent course.) Lecture 4 (Fall, Spring). | ||
| or | ||
| MATH-171 | Calculus A |
|
This is the first course in a three-course sequence (COS-MATH-171, -172, -173). This course includes a study of precalculus, polynomial, rational, exponential, logarithmic and trigonometric functions, continuity, and differentiability. Limits of functions are used to study continuity and differentiability. The study of the derivative includes the definition, basic rules, and implicit differentiation. Applications of the derivative include optimization and related-rates problems. (Prerequisites: Completion of the math placement exam or C- or better in MATH-111 or C- or better in ((NMTH-260 or NMTH-272 or NMTH-275) and NMTH-220) or equivalent course.) Lecture 5 (Fall, Spring). | ||
| MATH-172 | Calculus B |
|
This is the second course in three-course sequence (COS-MATH-171, -172, -173). The course includes Riemann sums, the Fundamental Theorem of Calculus, techniques of integration, and applications of the definite integral. The techniques of integration include substitution and integration by parts. The applications of the definite integral include areas between curves, and the calculation of volume. (Prerequisites: C- or better in MATH-171 or 1016-171T or 1016-281 or 1016-231 or equivalent course.) Lecture 5 (Fall, Spring). | ||
| MATH-173 | Calculus C |
|
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). | ||
| Choose one of the following: | ||
| MATH-219 | Multivariable Calculus |
|
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). | ||
| MATH-221 | Multivariable and Vector Calculus |
|
This course is principally a study of the calculus of functions of two or more variables, but also includes a study of vectors, vector-valued functions and their derivatives. The course covers limits, partial derivatives, multiple integrals, Stokes' Theorem, Green's Theorem, the Divergence Theorem, and applications in physics. Credit cannot be granted for both this course and MATH-219. (Prerequisite: C- or better MATH-173 or MATH-182 or MATH-182A or equivalent course.) Lecture 4 (Fall, Spring, Summer). | ||
| Choose one of the following: | ||
| STAT-205 | Applied Statistics |
|
This course covers basic statistical concepts and techniques including descriptive statistics, probability, inference, and quality control. The statistical package Minitab will be used to reinforce these techniques. The focus of this course is on statistical applications and quality improvement in engineering. This course is intended for engineering programs and has a calculus prerequisite. Note: This course may not be taken for credit if credit is to be earned in STAT-145 or STAT-155 or MATH 252.. (Prerequisite: MATH-173 or MATH-182 or MATH-182A or equivalent course.) Lecture 3 (Fall, Spring). | ||
| STAT-257 | Statistical Inference |
|
Learn how data furthers understanding of science and engineering. This course covers basic statistical concepts, sampling theory, hypothesis testing, confidence intervals, point estimation, and simple linear regression. A statistical software package such as MINITAB will be used for data analysis and statistical applications. (Prerequisites: MATH-251.
NOTE: Students cannot receive credit for both MATH-252 and STAT-257 nor for both STAT-205 and STAT-257.) Lecture 3 (Fall, Spring). | ||
| MATH-251 | Probability and Statistics |
|
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, Recitation 1 (Fall, Spring, Summer). | ||
| Required Courses (Group I) | ||
| MATH-255 | Actuarial Mathematics |
|
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). | ||
| MATH-261 | Topics in the Mathematics of 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 (Spring). | ||
| Group II | ||
| Choose at least one of the following: | ||
| ACCT-110 | Financial Accounting |
|
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). | ||
| FINC-220 | Financial Management |
|
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). | ||
| ECON-403 | Econometrics I |
|
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 ECON-101H) and (MATH-161 or MATH-171 or 1016-171T or MATH-181 or MATH-181A) and (STAT-145 or CQAS-251 or MATH-251 or STAT-205 or STAT-251) or equivalent courses.) Lecture 3 (Fall, Spring). | ||
| Group III† | ||
| STAT-305 | Regression Analysis |
|
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 STAT-205 or STAT-257) or equivalent courses.) Lecture 3 (Fall). | ||
| STAT-335 | 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 STAT-257 or equivalent course.) Lecture 3 (Fall). | ||
| STAT-405 | 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 STAT-257 or equivalent course.) Lecture 3 (Fall). | ||
| STAT-406 | 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). | ||
| STAT-547 | Data Mining |
|
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). | ||
| MATH-505 | Stochastic Processes |
|
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.
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