STOR 113 Decision Models for Business and Economics (3)
Prerequisite, Math 110 or exemption. An Introduction to multi-variable quantitative models in economics. Mathematical techniques for formulating and solving optimization and equilibrium problems will be developed, including elementary models under uncertainty. Fall, Spring, Summer. Staff.
STOR 120 Foundations of Statistics and Data Science (4)
The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social issues surrounding data analysis such as privacy and design. Lab enrollment required.
STOR 151 Introduction to Data Analysis (3)
Prerequisite, Math 110 or exemption. Elementary introduction to statistical reasoning, including sampling, elementary probability, statistical inference, and data analysis. STOR 151 may not be taken for credit by students who have credit for ECON 400 or PSYC 210. Fall, Spring. Staff.
STOR 155 Introduction to Data Models and Inference (3)
Prerequisite, Math 110 or exemption. Data analysis; correlation and regression; sampling and experimental design; basic probability (random variables, expected values, normal and binomial distributions); hypothesis testing and confidence intervals for means, proportions, and regression parameters; use of spreadsheet software.Fall, Spring, Summer. Staff.
STOR 215 Foundations of Decision Sciences (3)
Prerequisite, Math 110 or exemption. Introduction to basic concepts and techniques of discrete mathematics with applications to business and social and physical sciences. Topics include logic, sets, functions, combinatorics, discrete probability, graphs, and networks. Fall, Spring. Staff.
STOR 305 Decision Making Using Spreadsheet Models (3)
Prerequisite: STOR 155 or MATH 152. The use of mathematics to describe and analyze large-scale decision problems. Situations involving the allocation of resources, making decisions in a competitive environment, and dealing with uncertainty are modeled and solved using suitable software packages. Fall, Cunningham.
STOR 320 Introduction to Data Science (4)
Prerequisite, STOR 120 or 155. Development of basic skill set for data analysis from obtaining data to data carpentry, exploration, modeling, and communication. Topics covered include regression, clustering, classification, algorithmic thinking, and non-standard data objects (networks and text data). Students may not receive credit for both STOR 320 and STOR 520. Lab enrollment required.
STOR 415 Introduction to Optimization (3)
Prerequisite: MATH 547. Linear, integer, nonlinear, and dynamic programming, classical optimization problems, network theory. Fall, Spring. Lu, Tran-Dinh.
STOR 435 Introduction to Probability (MATH 535) (3)
Prerequisites: MATH 233. Introduction to mathematical theory of probability covering random variables; moments; binomial, Poisson, normal and related distributions; generating functions; sums and sequences of random variables; and statistical applications. Fall, Spring, Summer. Pipiras, Kulkarni, Bhamidi.
STOR 445 Stochastic Modeling (3)
Prerequisite: STOR 435 or BIOS 660. Introduction to Markov chains, Poisson process, continuous-time Markov chains, renewal theory. Applications to queueing systems inventory, and reliability, with emphasis on systems modeling, design, and control. Fall, Spring. Kulkarni, Ziya.
STOR 455 Statistical Methods I (3)
Prerequisite: STOR 155 or equivalent. Some familiarity with matrix algebra recommended, but not required. Review of basic inference; two-sample comparisons; correlation; introduction to matrices; simple and multiple regression (including significance tests, diagnostics, variable selection); analysis of variance; use of statistical software. Fall, Spring, Summer. Cunningham.
STOR 465 Simulation for Analytics (3)
Prerequisites, STOR 155 and 435. Introduces concepts of random number generation, random variate generation, and discrete event simulation of stochastic systems. Students perform simulation experiments using standard simulation software. Alternating Spring. Argon.
STOR 471 Long Term Actuarial Models (3)
Prerequisites: STOR 435. Probability models for long-term insurance and pension systems that involve future contingent payments and failure-time random variables. Introduction to survival distributions and measures of interest and annuities-certain. Fall. Dunn.
STOR 472 Short Term Actuarial Models (3)
Prerequisite, STOR 435. Short term probability models for potential losses and their applications to both traditional insurance systems and conventional business decisions. Introduction to stochastic process models of solvency requirements. Spring. Dunn.
STOR 535 Probability for Data Science (3)
Prerequisite, MATH 233. This course is an advanced undergraduate course in probability with the aim to give students the technical and computational tools for advanced courses in data analysis and machine learning. It covers random variables, moments, binomial, Poisson, normal and related distributions, generating functions, sums and sequences of random variables, statistical applications, Markov chains, multivariate normal and prediction analytics. Students may not receive credit for both STOR 435 and STOR 535.
STOR 493 Internship in Statistics and Operations Research (3)
Requires permission of the department. Statistics and analytics majors only. An opportunity to obtain credit for an internship related to statistics, operations research, or actuarial science. Pass/Fail only. Does not count toward the statistics and analytics major or minor.
STOR 496 Undergraduate Reading and Research in Statistics and Operations Research (1-3)
Permission of the director of undergraduate studies. This course is intended mainly for students working on honors projects. May be repeated for credit.
STOR 555 Mathematical Statistics (3)
Prerequisite: STOR 435 or equivalent.Functions of random samples and their probability distributions, introductory theory of point and interval estimation and hypothesis testing, elementary decision theory. Fall. Carlstein..
STOR 556 Advanced Methods of Data Analysis (3)
Prerequisite: STOR 435 and STOR 455.Topics selected from: design of experiments, sample surveys, nonparametrics, time-series, multivariate analysis, contingency tables, logistic regression, and simulation. Use of statistical software packages. Spring. Zhang.
STOR 565 Machine Learning (3)
Prerequisites, STOR 215 or MATH 381, and STOR 435. Introduction to theory and methods of machine learning including classification; Bayes risk/rule, linear discriminant analysis, logistic regression, nearest neighbors, and support vector machines; clustering algorithms; overfitting, estimation error, cross validation. Alternating Spring, Nobel.