**Undergraduate majors:**actuarial science (B.S.); statistics (B.S.)

**Undergraduate minor:**statistics

**Graduate degrees:**M.S. in actuarial science; M.S. in statistics; Ph.D. in statistics

**Faculty:**https://stat.uiowa.edu/people

**Website:**https://stat.uiowa.edu/

The Department of Statistics and Actuarial Science offers undergraduate majors, an undergraduate minor, and graduate degree programs. They partner with other departments to offer the undergraduate Certificate in Large Data Analysis and the Certificate in Social Science Analytics (see below). The department offers courses that undergraduate students in all majors may use to satisfy the GE CLAS Core Quantitative or Formal Reasoning requirement.

Probability and statistics are important scientific disciplines essential to all fields of study that rely on information obtained from data. In a world bombarded with numerical information, informed decisions rely on the ability to separate fact from fiction by applying valid statistical analyses and visualizations. Statisticians can provide crucial guidance in determining what information is reliable and which predictions may be trusted. They often help search for clues to the solution of a scientific mystery and sometimes keep investigators from being misled by false impressions.

The work of a statistician may range from the theoretical (developing new methodologies and statistical theory) to the applied (working with scientists and decision makers to collect, analyze, and interpret data). Regardless of the areas in which they work, statisticians need strong mathematical, computational, and communication skills. Because uncertainty and data arise in many settings, statisticians have the opportunity to work on a variety of projects in industry, education, government, and research. Thousands of statisticians work in medicine, law, agriculture, public policy, marketing, manufacturing, engineering, and other fields in the social and natural sciences. The diversity of applications is an exciting aspect of the field and is one reason why the demand for well-trained statisticians continues to be strong.

An actuary is a business executive, professionally trained in the mathematical sciences. Actuaries specialize in the evaluation of financial risk—most often in the context of life, health, and casualty insurance, where they design, analyze, and refine varied programs to meet the insurance needs of society. Many actuaries are employed by insurance companies, where they have responsibilities for all phases of the development and maintenance of their company's products. They have considerable influence on the financial soundness of their company through work in pricing insurance policies and in compiling data for financial statements.

Many actuaries are employed as consultants. Their actuarial services are used by smaller insurance companies and by individual employers who need actuarial guidance in establishing insurance and retirement programs for their employees. A growing number of actuaries work in the areas of asset/liability management and risk management. Some of these actuaries are employed by investment and consulting firms; others are employed by insurance companies.

Actuaries have been called financial architects and social mathematicians, because their combined analytical and business skills help solve a growing variety of financial and social problems. The actuarial profession is a demanding yet rewarding career choice.

## Related Major and Certificates

### Major: Data Science

The B.S. in data science produces graduates with the sophisticated analytical and computational skills required to thrive in a field where new problems are encountered at an ever-increasing rate. A highly technical program, the major emphasizes the statistical/probabilistic and algorithmic (e.g., machine and statistical learning) methods that underlie the analysis process, developing data preparation skills (which generally involve writing software to obtain, extract, merge, clean, and/or transform the raw data), and the creation and implementation of new or special-purpose analysis tools.

Statistics majors may not earn a major in data science. The Department of Statistics and Actuarial Science and the Department of Computer Science collaborate to offer the major in data science. The B.S. in data science is administered by the Department of Statistics and Actuarial Science; see the B.S. in data science in the Catalog.

### Certificate: Large Data Analysis

The Certificate in Large Data Analysis can be earned in addition to a B.S. degree in statistics. The certificate focuses on handling, processing, and extracting information from large data sets. As computers have become faster and smaller, more information can be gathered and used for a large range of applications, such as for weather forecasting, identifying people and trends utilizing Facebook or other social media, understanding the genome, and searching for disease causes and cures, as well as many other areas of study. The certificate is interdisciplinary, requiring courses from three areas of study—computer science, mathematics, and statistics. Computer science teaches students how to handle large amounts of data and how to implement the algorithms to process them, while statistics helps students to understand what can and cannot be legitimately inferred from the data. Mathematics focuses on algorithms and methods for connecting these important areas of data collection.

### Certificate: Social Science Analytics

The growth of big data and informatics calls for a new set of skills for social science students and an increased understanding of the logic of data collection and analysis. The certificate focuses on the application side of data analysis and allows focus to be on the specific research methods and quantitative skills using data-driven methods effective for more understanding in an increasingly complicated social-political world. The certificate offers an opportunity for interdisciplinary training on how data can be used to address important questions in the social sciences. The Department of Statistics and Actuarial Science collaborates with the Departments of Geographical and Sustainability Sciences, Political Science, and Sociology to offer the undergraduate program in social science analytics; see the Certificate in Social Science Analytics in the Catalog.

The Department of Statistics and Actuarial Science is housed in Schaeffer Hall, adjacent to Old Capitol, a National Historic Landmark and the center of campus. The department operates two computer labs in Schaeffer Hall. One, which also is used as an electronic classroom, contains 28 Windows PCs. The second houses 18 high-end UNIX workstations. Students use these labs for class work and research.

## Undergraduate Duplication and Regression Policy

Undergraduate students should be aware of the duplication and regression policies concerning the following courses.

Students may earn credit for only two of these:

STAT:1010 Statistics and Society,

STAT:1020/PSQF:1020 Elementary Statistics and Inference,

STAT:1030 Statistics for Business, and

STAT:2010 Statistical Methods and Computing.

Credit for STAT:1010 Statistics and Society may be earned only if the course is taken before any of these:

STAT:1020/PSQF:1020 Elementary Statistics and Inference,

STAT:1030 Statistics for Business, and

STAT:2010 Statistical Methods and Computing.

Students may receive credit for only one course from each of these pairs:

STAT:2010 Statistical Methods and Computing and STAT:4200/IGPI:4200 Statistical Methods and Computing,

STAT:3100/IGPI:3100 Introduction to Mathematical Statistics I and STAT:3120/IGPI:3120 Probability and Statistics, and

STAT:3510/IGPI:3510 Biostatistics and STAT:4143/PSQF:4143 Introduction to Statistical Methods.

Students may not take STAT:3101/IGPI:3101 Introduction to Mathematical Statistics II and STAT:4101/IGPI:4101 Mathematical Statistics II at the same time and get credit for both (nor go back to STAT:3101/IGPI:3101 after taking STAT:4101/IGPI:4101).

## Statistics Courses

**STAT:1000 First-Year Seminar1 s.h.**

Small discussion class taught by a faculty member; topics chosen by instructor; may include outside activities (e.g., films, lectures, performances, readings, visits to research facilities). Requirements: first- or second-semester standing.

**STAT:1010 Statistics and Society3 s.h.**

Statistical ideas and their relevance to public policy, business, humanities, and the social, health, and physical sciences; focus on critical approach to statistical evidence. Requirements: one year of high school algebra or MATH:0100. GE: Quantitative or Formal Reasoning.

**STAT:1020 Elementary Statistics and Inference3 s.h.**

Graphing techniques for presenting data, descriptive statistics, correlation, regression, prediction; logic of statistical inference, elementary probability models, estimation and tests of significance. Requirements: one year of high school algebra or MATH:0100. GE: Quantitative or Formal Reasoning. Same as PSQF:1020.

**STAT:1030 Statistics for Business4 s.h.**

Descriptive statistics, graphical presentation, elementary probability, estimation and testing, regression, correlation; statistical computer packages. GE: Quantitative or Formal Reasoning.

**STAT:2010 Statistical Methods and Computing3 s.h.**

Methods of data description and analysis using SAS; descriptive statistics, graphical presentation, estimation, hypothesis testing, sample size, power; emphasis on learning statistical methods and concepts through hands-on experience with real data. Recommendations: undergraduate standing. GE: Quantitative or Formal Reasoning.

**STAT:2020 Probability and Statistics for the Engineering and Physical Sciences3 s.h.**

Probability, random variables, important discrete and continuous distributions, joint distributions, transformations of random variables, descriptive statistics, point and interval estimation, tests of hypotheses, regression. Prerequisites: MATH:1560 or MATH:1860.

**STAT:3100 Introduction to Mathematical Statistics I3 s.h.**

Descriptive statistics, probability, discrete and continuous distributions, sampling, sampling distributions. Prerequisites: MATH:1860 or MATH:1560. Same as IGPI:3100.

**STAT:3101 Introduction to Mathematical Statistics II3 s.h.**

Estimation, testing statistical hypotheses, linear models, multivariate distributions, nonparametric methods. Prerequisites: STAT:3100. Same as IGPI:3101.

**STAT:3120 Probability and Statistics4 s.h.**

Models, discrete and continuous random variables and their distributions, estimation of parameters, testing statistical hypotheses. Prerequisites: MATH:1560 or MATH:1860. Same as IGPI:3120.

**STAT:3200 Applied Linear Regression3 s.h.**

Regression analysis with focus on applications; model formulation, checking, selection; interpretation and presentation of analysis results; simple and multiple linear regression; logistic regression; ANOVA; hands-on data analysis with computer software. Prerequisites: STAT:2020 or STAT:2010. Same as IE:3760, IGPI:3200.

**STAT:3210 Experimental Design and Analysis3 s.h.**

Single- and multifactor experiments; analysis of variance; multiple comparisons; contrasts; diagnostics; fixed, random, and mixed effects models; designs with blocking and/or nesting; two-level factorials and fractions thereof; use of statistical computing packages. Prerequisites: STAT:3200.

**STAT:3510 Biostatistics3 s.h.**

Statistical concepts and methods for the biological sciences; descriptive statistics, elementary probability, sampling distributions, confidence intervals, parametric and nonparametric methods, one-way ANOVA, correlation and regression, categorical data. Requirements: MATH:0100 or MATH:1005 or ALEKS score of 30 or higher. Same as IGPI:3510.

**STAT:3620 Quality Control3 s.h.**

Basic techniques of statistical quality control; application of control charts for process control variables; design of inspection plans and industrial experimentation; modern management aspects of quality assurance systems. Offered fall semesters. Prerequisites: STAT:2020 or (STAT:3100 and STAT:3101 and STAT:3200). Same as CEE:3142, IE:3600.

**STAT:4100 Mathematical Statistics I3 s.h.**

Probability, conditional probability, random variables, distribution and density functions, joint and conditional distributions, various families of discrete and continuous distributions, mgf technique for sums, convergence in distribution, convergence in probability, central limit theorem. Prerequisites: MATH:2850 and MATH:2700. Same as IGPI:4100.

**STAT:4101 Mathematical Statistics II3 s.h.**

Transformations, order statistics, point estimation, sufficient statistics, Rao-Blackwell Theorem, delta method, confidence intervals, likelihood ratio tests, applications. Prerequisites: STAT:4100. Same as IGPI:4101.

**STAT:4143 Introduction to Statistical Methods3 s.h.**

Analysis, interpretation of research data; descriptive statistics; introduction to probability, sampling theory, statistical inference (binomial, normal distribution, t-distribution models); linear correlation, regression. Same as PSQF:4143.

**STAT:4200 Statistical Methods and Computing3 s.h.**

Methods of data description and analysis using SAS; descriptive statistics, graphical presentation, estimation, hypothesis testing, sample size, power; emphasis on learning statistical methods and concepts through hands-on experience with real data. Recommendations: graduate standing in non-statistics or less quantitative major. Same as IGPI:4200.

**STAT:4510 Regression, Time Series, and Forecasting3 s.h.**

Regression analysis, forecasting, time series methods; use of statistical computing packages. Prerequisites: STAT:3101 with a minimum grade of C+.

**STAT:4520 Bayesian Statistics3 s.h.**

Bayesian statistical analysis, with focus on applications; Bayesian and frequentist methods compared; Bayesian model specification, choice of priors, computational methods; hands-on Bayesian data analysis using appropriate software; interpretation and presentation of analysis results. Prerequisites: STAT:3200 and (STAT:3101 or STAT:4101 or STAT:3120). Same as IGPI:4522, PSQF:4520.

**STAT:4540 Statistical Learning3 s.h.**

Introduction to supervised and unsupervised statistical learning, with a focus on regression, classification, and clustering; methods will be applied to real data using appropriate software; supervised learning topics include linear and nonlinear (e.g., logistic) regression, linear discriminant analysis, cross-validation, bootstrapping, model selection, and regularization methods (e.g., ridge and lasso); generalized additive and spline models, tree-based methods, random forests and boosting, and support-vector machines; unsupervised learning topics include principal components and clustering. Requirements: an introductory statistics course and a regression course. Recommendations: prior exposure to programming and/or software, such as R, SAS, and Matlab. Same as IGPI:4540.

**STAT:4560 Statistics for Risk Modeling3 s.h.**

Theory and applications of general linear models, generalized linear models, and regression-based time series models; emphasis on parameter estimation, variable selection, and diagnostic checking for these models and their use for statistical inference and prediction; demonstration of practical implementations of these models with real data in actuarial and financial contexts. Prerequisites: STAT:4101 with a minimum grade of C+ or STAT:5101 with a minimum grade of C+. Requirements: knowledge of computer programming.

**STAT:4580 Data Visualization and Data Technologies3 s.h.**

Introduction to common techniques for visualizing univariate and multivariate data, data summaries, and modeling results; students learn to create and interpret these visualizations, and assess effectiveness of different visualizations based on an understanding of human perception and statistical thinking; data technologies for obtaining and preparing data for visualization and further analysis. Requirements: an introductory statistics course and a regression course. Recommendations: prior exposure to basic use of statistical programming software (e.g., R or SAS) as obtained from a regression course strongly recommended. Same as IGPI:4580.

**STAT:4740 Large Data Analysis3 s.h.**

Current areas that deal with problem of Big Data; techniques from computer science, mathematics, statistics; high performance and parallel computing, matrix techniques, cluster analysis, visualization; variety of applications including Google PageRank, seismology, Netflix-type problems, weather forecasting; fusion of data with simulation; projects. Prerequisites: (CS:1210 with a minimum grade of C- or ENGR:2730 with a minimum grade of C-) and (MATH:3800 or CS:3700) and (STAT:3200 or IE:3760 or IGPI:3200). Same as CS:4740, IGPI:4740, MATH:4740.

**STAT:4880 Data Science Creative Component1 s.h.**

Independent project under a faculty advisor's supervision; emphasis on communication of ideas learned in student's data science course work or internship.

**STAT:4890 Data Science Practicum2 s.h.**

On- or off-campus internship or group-based consulting project that provides experience in a real-world setting and introduces ethical and confidentiality issues related to data collection, storage, and sharing.

**STAT:5090 ALPHA Seminar1 s.h.**

Resources available to students, program requirements, tips for academic success, professional statistical organizations, library and career center resources, statistical computing, scientific document preparation, history of statistics. Requirements: graduate standing in statistics.

**STAT:5100 Statistical Inference I3 s.h.**

Review of probability, distribution theory (multiple random variables, moment-generating functions, transformations, conditional distributions), sampling distributions, order statistics, convergence concepts, generating random samples. Prerequisites: MATH:2850 and STAT:3101.

**STAT:5101 Statistical Inference II3 s.h.**

Continuation of STAT:5100; principles of data reduction, point estimation theory (MLE, Bayes, UMVU), hypothesis testing, interval estimation, decision theory, asymptotic evaluations. Prerequisites: STAT:5100.

**STAT:5120 Mathematical Methods for Statistics3 s.h.**

Real numbers, point set theory, limit points, limits, metric spaces, continuity, sequences and series, Taylor series (multivariate), uniform convergence, Riemann-Stieltjes integrals. Requirements: statistics graduate standing.

**STAT:5200 Applied Statistics I4 s.h.**

Introduction to computing environments and statistical packages, descriptive statistics, basic inferential methods (confidence intervals, chi-square tests); linear models (regression and ANOVA models—specification and assumptions, fitting, diagnostics, selection, testing, interpretation). Prerequisites: STAT:3101. Corequisites: STAT:4100 or STAT:5100. Requirements: facility with matrix algebra. Same as IGPI:5199.

**STAT:5201 Applied Statistics II3 s.h.**

Design of experiments and analysis of designed experiments; models for fixed and random effects; mixed models; design and analysis of complex plans; sample-size methods. Prerequisites: STAT:5200. Recommendations: prior exposure to SAS software.

**STAT:5400 Computing in Statistics3 s.h.**

R; database management; graphical techniques; importing graphics into word-processing documents (e.g., LaTeX); creating reports in LaTeX; SAS; simulation methods (Monte Carlo studies, bootstrap, etc.). Prerequisites: CS:1210 and STAT:3200 and (STAT:3120 or STAT:3101 or STAT:4101). Corequisites: STAT:5100 and STAT:5200 if not already completed. Same as IGPI:5400.

**STAT:5610 Regression Modeling and ANOVA in the Health Sciences3 s.h.**

Continuation of BIOS:4120; correlation, simple and multiple linear regression, confounding, interactions, model selection, single and multiple factor ANOVA (analysis of variance) models, contrasts, multiple comparisons, nested and block designs, and an introduction to mixed models; designed for non-biostatistics majors. Offered spring semesters and summer sessions. Prerequisites: BIOS:4120. Same as BIOS:5120, IGPI:5120.

**STAT:5810 Research Data Management3 s.h.**

Introduction to data management techniques and problems encountered in gathering and processing data from biomedical investigations; introduction to SAS, techniques taught in SAS; designed for non-biostatistics majors. Offered fall and spring semesters. Recommendations: prior programming experience with C, C++, Python, Java, or other. Same as BIOS:5310, IGPI:5310.

**STAT:6220 Statistical Consulting3 s.h.**

Realistic supervised data analysis experiences, including statistical packages, statistical graphics, writing statistical reports, dealing with complex or messy data. Offered spring semesters. Prerequisites: (STAT:3200 and STAT:3210) or (STAT:5201 and STAT:5200). Requirements: for undergraduate majors—major g.p.a. of 3.00 or above, and grades of B or higher in STAT:3200 and STAT:3210.

**STAT:6300 Probability and Stochastic Processes I3 s.h.**

Conditional expectations; Markov chains, including random walks and gambler's ruin; classification of states; stationary distributions; branching processes; Poisson processes; Brownian motion. Prerequisites: STAT:4100.

**STAT:6301 Probability and Stochastic Processes II3 s.h.**

Markov chains with continuous state space, Martingales, random walks, Brownian motion and other continuous-time Markov chains, simulation methods. Prerequisites: STAT:6300.

**STAT:6510 Applied Generalized Regression3 s.h.**

Applications of semiparametric models, generalized linear models, nonlinear normal errors models, correlated response models; use of statistical packages, especially R and SAS. Requirements: introductory statistics and applied linear models. Same as IGPI:6511.

**STAT:6513 Intermediate Statistical Methods4 s.h.**

Foundation for more advanced applied courses; logic of statistical inference, chi-square, and other tests of statistical hypotheses; small sample error theory, interval estimates, introduction to analysis of variance, selected nonparametric methods. Requirements: for PSQF:6243—PSQF:4143; for STAT:6513—STAT:4143. Same as PSQF:6243.

**STAT:6514 Correlation and Regression4 s.h.**

Correlation techniques; selected bivariate procedures, multiple, partial, curvilinear correlation; multiple linear regression; sampling theory applied to regression analysis and correlation coefficients; simple causal models. Requirements: for PSQF:6244—PSQF:6243; for STAT:6514—STAT:6513. Same as PSQF:6244.

**STAT:6516 Design of Experiments4 s.h.**

Theory and methods in the planning and statistical analysis of experimental studies; testing of hypotheses about linear contrasts among means in single-factor and multifactor, completely randomized, and repeated measurement designs. Requirements: for PSQF:6246—PSQF:6243; for STAT:6516—STAT:6513. Same as PSQF:6246.

**STAT:6530 Environmental and Spatial Statistics3 s.h.**

Methods for sampling environmental populations, sampling design, trend detection and estimation, geostatistics, kriging, variogram estimation, lattice data analysis, analysis of spatial point patterns. Prerequisites: STAT:4101 and STAT:3200. Same as IGPI:6530.

**STAT:6540 Applied Multivariate Analysis3 s.h.**

Multivariate descriptive statistics, multivariate normal distribution, Hotelling's T-squared, MANOVA, multivariate regression, principal components, discrimination and classification, cluster analysis. Prerequisites: STAT:3200 and STAT:3210. Requirements: facility with matrix algebra. Same as PSQF:6245.

**STAT:6547 Nonparametric Statistical Methods3 s.h.**

Selected nonparametric methods; one- and two-sample location tests and estimation methods, measures of association, analyses of variance; emphasis on relationships to classical parametric procedures. Same as PSQF:6247.

**STAT:6550 Introductory Longitudinal Data Analysis3 s.h.**

Introduction to statistical models and estimation methods for outcome variables (normal and non-normal) clustered or measured repeatedly in time or space; focus on applications and computer software methods for ANOVA based methods, hierarchical linear models, linear mixed models, correlated regression models, generalized estimating equations, and generalized linear mixed models. Offered fall semesters. Prerequisites: BIOS:5120 or STAT:3200. Same as BIOS:6310, IGPI:6310.

**STAT:6560 Applied Time Series Analysis3 s.h.**

General stationary, nonstationary models, autocovariance autocorrelation functions; stationary, nonstationary autoregressive integrated moving average models; identification, estimation, forecasting in linear models; use of statistical computer packages. Offered spring semesters. Prerequisites: (STAT:5200 or STAT:3200) and STAT:3101.

**STAT:6970 Topics in Statistics3 s.h.**

**STAT:6990 Readings in Statisticsarr.**

**STAT:7100 Advanced Inference I3 s.h.**

Concepts of convergence, asymptotic methods including the delta method, sufficiency, asymptotic efficiency, Fisher information and information bounds for estimation, maximum likelihood estimation, the EM-algorithm, Bayes estimation, decision theory. Prerequisites: STAT:5101 and STAT:5120.

**STAT:7101 Advanced Inference II3 s.h.**

Hypothesis testing, asymptotics of the likelihood ratio test, asymptotic efficiency, statistical functionals, robustness, bootstrap and jackknife, estimation with dependent data. Prerequisites: STAT:7100.

**STAT:7190 Seminar: Mathematical Statisticsarr.**

**STAT:7200 Linear Models4 s.h.**

Linear spaces and selected topics in matrix algebra, multivariate normal distribution and distributions of quadratic forms, full rank and non-full rank linear models, estimability, least squares and best linear unbiased estimation, interval estimation, hypothesis testing, random and mixed models, best linear unbiased prediction, variance component estimation. Prerequisites: STAT:5101 and STAT:5200 and STAT:5201.

**STAT:7290 Seminar: Applied Statisticsarr.**

**STAT:7300 Foundations of Probability I3 s.h.**

Probability theory, with emphasis on constructing rigorous proofs; measure spaces, measurable functions, random variables and induced measures, distribution functions, Lebesque integral, product measure and independence, Borel Cantelli lemma, modes of convergence. Prerequisites: STAT:5120.

**STAT:7301 Foundations of Probability II3 s.h.**

Laws of large numbers, characteristic functions and properties, central limit theorem, Radon-Nikodym derivatives, conditional expected value and martingales. Prerequisites: STAT:7300.

**STAT:7390 Seminar: Probabilityarr.**

**STAT:7400 Computer Intensive Statistics3 s.h.**

Computer arithmetic; random variate generation; numerical optimization; numerical linear algebra; smoothing techniques; bootstrap methods; cross-validation; MCMC; EM and related algorithms; other topics per student/instructor interests. Prerequisites: (BIOS:5710 or STAT:5200) and STAT:3101. Requirements: proficiency in Fortran or C or C++ or Java. Same as IGPI:7400.

**STAT:7510 Analysis of Categorical Data3 s.h.**

Models for discrete data, distribution theory, maximum likelihood and weighted least squares estimation for categorical data, tests of fit, models selection. Offered spring semesters. Prerequisites: (BIOS:5720 or STAT:5200) and (STAT:5101 or STAT:4101). Same as BIOS:7410.

**STAT:7520 Bayesian Analysis3 s.h.**

Decision theory, conjugate families, structure of Bayesian inference, hierarchical models, asymptotic approximations for posterior distributions, Markov chain Monte Carlo methods and convergence assessment, model adequacy and model choice. Prerequisites: STAT:5101 and STAT:5400 and STAT:5200.

**STAT:7560 Time Series Analysis3 s.h.**

Stationary time series, ARIMA models, spectral representation, linear prediction inference for the spectrum, multivariate time series, state space models and processes, nonlinear time series. Prerequisites: STAT:4101 and (STAT:3200 or STAT:6560).

**STAT:7570 Survival Data Analysis3 s.h.**

Types of censoring and truncation; survival function estimation; parametric inference using exponential, Weibull, and accelerated failure time models; nonparametric tests; sample size calculation; Cox regression with stratification and time-dependent covariates; regression diagnostics; competing risks; topics may include analysis of correlated survival data and/or recurrent events; designed for biostatistics and statistics majors. Offered fall semesters. Prerequisites: BIOS:5720 and ((STAT:4100 and STAT:4101) or (STAT:5100 and STAT:5101)). Same as BIOS:7210, IGPI:7210.

**STAT:7990 Reading Researcharr.**

## Actuarial Science Courses

**ACTS:1000 First-Year Seminar1 s.h.**

Small discussion class taught by a faculty member; topics chosen by instructor; may include outside activities (e.g., films, lectures, performances, readings, visits to research facilities). Requirements: first- or second-semester standing.

**ACTS:1001 Introductory Seminar on Actuarial Science1 s.h.**

Introduction to actuarial science; U.S. actuarial organizations and actuarial qualification process; program requirements and tips for academic success; career center, actuarial club, and internships; actuarial career; ethics; communication; introduction to actuarial computing. Requirements: actuarial science interest major and first-year standing.

**ACTS:3080 Mathematics of Finance I3 s.h.**

Mathematics of compound interest, annuities certain, amortization schedules, yield rates, sinking funds, and bonds. Prerequisites: STAT:3100 with a minimum grade of B-. Requirements: meet the prerequisite or have graduate standing.

**ACTS:3110 Actuarial Exam P Preparation1 s.h.**

Preparation for the Society of Actuaries exam P.

**ACTS:3210 Actuarial Exam FM Preparation1 s.h.**

Preparation for the Society of Actuaries exam FM. Corequisites: ACTS:3080, if not taken as a prerequisite.

**ACTS:4110 Actuarial Exam LTAM Preparation1 s.h.**

Preparation for the Society of Actuaries exam. Corequisites: ACTS:4280, if not taken as a prerequisite.

**ACTS:4130 Quantitative Methods for Actuaries3 s.h.**

Survival distributions, life tables, life insurance, introductory stochastic processes. Offered fall semesters. Prerequisites: STAT:3100 with a minimum grade of B- and ACTS:3080 with a minimum grade of C+. Corequisites: STAT:4100 or STAT:5100. Requirements: multivariate calculus and linear algebra.

**ACTS:4160 Topics in Actuarial Sciencearr.**

Selected topics in actuarial science, financial mathematics, and quantitative risk management not covered in other courses.

**ACTS:4180 Life Contingencies I3 s.h.**

Life annuities, net and gross premiums, net and gross premium reserves, modified reserve methods, and Markov chains. Offered spring semesters. Prerequisites: ACTS:3080 with a minimum grade of C+ and ACTS:4130 with a minimum grade of C+ and (STAT:4100 with a minimum grade of C+ or STAT:5100 with a minimum grade of C+).

**ACTS:4280 Life Contingencies II3 s.h.**

Multilife models, multiple-decrement models, continuous-time Markov chain models, profit testing, and profit measures. Offered fall semesters. Prerequisites: ACTS:4180 with a minimum grade of C+.

**ACTS:4380 Mathematics of Finance II3 s.h.**

Derivatives markets, forwards, options, pricing models, and actuarial applications. Prerequisites: ACTS:3080 with a minimum grade of C+. Corequisites: STAT:4100 or STAT:5100. Requirements: multivariate calculus and linear algebra.

**ACTS:4990 Readings in Actuarial Sciencearr.**

**ACTS:6160 Topics in Actuarial Sciencearr.**

Selected topics in actuarial science, financial mathematics, and quantitative risk management not covered in other courses; a required course for all final-year M.S. students in actuarial science. Prerequisites: ACTS:4180 with a minimum grade of C+ and ACTS:4380 with a minimum grade of C+.

**ACTS:6480 Loss Distributions3 s.h.**

Severity, frequency, and aggregate models and their modifications; risk measures; construction of empirical models. Offered spring semesters. Prerequisites: STAT:5101 or STAT:4101. Corequisites: ACTS:6580.

**ACTS:6580 Credibility and Survival Analysis3 s.h.**

Construction and selection of parametric models, credibility, and simulation. Offered spring semesters. Prerequisites: STAT:5101 or STAT:4101. Corequisites: ACTS:6480.

**ACTS:6990 Readings in Actuarial Sciencearr.**

Supervised reading and research in actuarial science, financial mathematics, or quantitative risk management.

**ACTS:7730 Advanced Topics in Actuarial Science/Financial Mathematicsarr.**

Selected topics in actuarial science, financial mathematics, and quantitative risk management not covered in other courses.