STOR Courses

Here is information about STOR class enrollment for fall 2023. Classes with no meeting time listed are not shown. Feel free to contact me with any questions/comments/issues.

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Data also available for: COMP, AAAD, AMST, APPL, BIOL, BIOS, BMME, CHEM, CMPL, COMM, DRAM, EDUC, EPID, INLS, MATH, MEJO, PHIL, PSYC, STOR, WGST

Data last updated: 2023-11-06 16:30:44.778336

Class Number	Class	Meeting Time	Instructor	Room	Unreserved Enrollment	Reserved Enrollment	Wait List
4314	STOR 113 - 001 Decision Models for Business and Economics	TuTh 2:00PM - 3:15PM	Nilay Argon, Andrew Nguyen	Hanes Hall - Rm 0120	Seats filled (100 total)	Seats filled	0/10
Description: Prerequisite, MATH 110. An introduction to multivariable quantitative models in economics. Mathematical techniques for formulating and solving optimization and equilibrium problems will be developed, including elementary models under uncertainty. 3 units.
6823	STOR 113 - 002 Decision Models for Business and Economics	MoWeFr 8:00AM - 8:50AM	Grigory Terlov	Hanes Hall - Rm 0120	65/66 (80 total)	Seats filled	0/5
Description: Prerequisite, MATH 110. An introduction to multivariable quantitative models in economics. Mathematical techniques for formulating and solving optimization and equilibrium problems will be developed, including elementary models under uncertainty. 3 units.
10196	STOR 120 - 001 Foundations of Statistics and Data Science	MoWeFr 8:00AM - 8:50AM	Oluremi Abayomi	Gardner - Rm 0105	Seats filled (100 total)	Seats filled	0/10
Description: 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. 4 units.
10673	STOR 120 - 002 Foundations of Statistics and Data Science	MoWeFr 10:10AM - 11:00AM	Jeff McLean	Hanes Hall - Rm 0120	Seats filled (100 total)	Seats filled	0/10
Description: 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. 4 units.
11160	STOR 120 - 03F Foundations of Statistics and Data Science	MoWeFr 10:10AM - 11:00AM	SERHAN ZIYA	Hanes Hall - Rm 0130	Seats filled (35 total)	Seats filled
Description: 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. 4 units.
10201	STOR 120 - 400 Foundations of Statistics and Data Science	Tu 8:00AM - 8:50AM	Kyung Rok Kim	Hanes Hall - Rm 0107	29/30 (30 total)	Seats filled	0/5
Description: 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. 0 units.
11768	STOR 120 - 401 Foundations of Statistics and Data Science	Th 8:00AM - 8:50AM	Kyung Rok Kim	Hanes Hall - Rm 0107	20/30 (30 total)	Seats filled	0/5
Description: 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. 0 units.
10197	STOR 120 - 402 Foundations of Statistics and Data Science	We 1:25PM - 2:15PM	Kyung Rok Kim	Hanes Hall - Rm 0107	Seats filled (30 total)	Seats filled	0/5
Description: 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. 0 units.
10198	STOR 120 - 403 Foundations of Statistics and Data Science	Fr 1:25PM - 2:15PM	Kendall Thomas	Hanes Hall - Rm 0107	29/30 (30 total)	Seats filled	0/5
Description: 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. 0 units.
10674	STOR 120 - 404 Foundations of Statistics and Data Science	Tu 9:30AM - 10:20AM	Xianwen He	Hanes Hall - Rm 0107	Seats filled (25 total)	Seats filled	0/5
Description: 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. 0 units.
11769	STOR 120 - 405 Foundations of Statistics and Data Science	Th 9:30AM - 10:20AM	Xianwen He	Hanes Hall - Rm 0107	26/30 (30 total)	Seats filled	0/5
Description: 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. 0 units.
10675	STOR 120 - 406 Foundations of Statistics and Data Science	We 4:40PM - 5:30PM	Xianwen He	Hanes Hall - Rm 0112	Seats filled (25 total)	Seats filled	0/5
Description: 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. 0 units.
10676	STOR 120 - 407 Foundations of Statistics and Data Science	Fr 4:40PM - 5:30PM	Kendall Thomas	Alumni - Rm 0205	Seats filled (25 total)	Seats filled	0/5
Description: 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. 0 units.
11161	STOR 120 - 408 Foundations of Statistics and Data Science	Tu 2:00PM - 2:50PM	Kendall Thomas	Greenlaw - Rm 0222	Seats filled (35 total)	Seats filled	0/5
Description: 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. 0 units.
4315	STOR 151 - 001 Introduction to Data Analysis	MoWeFr 1:25PM - 2:15PM	Oluremi Abayomi	Hanes Hall - Rm 0120	Seats filled (100 total)	Seats filled	0/10
Description: Prerequisite, MATH 110. 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. 3 units.
4316	STOR 155 - 001 Introduction to Data Models and Inference	MoWeFr 9:05AM - 9:55AM	Oluremi Abayomi	Hanes Hall - Rm 0120	Seats filled (100 total)	Seats filled	0/10
Description: Prerequisite, MATH 110. 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. 3 units.
4317	STOR 155 - 002 Introduction to Data Models and Inference	MoWeFr 10:10AM - 11:00AM	WILLIAM LASSITER	Gardner - Rm 0105	Seats filled (100 total)	Seats filled	0/10
Description: Prerequisite, MATH 110. 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. 3 units.
4318	STOR 155 - 003 Introduction to Data Models and Inference	MoWeFr 12:20PM - 1:10PM	Nicolas Fraiman	Hanes Hall - Rm 0120	Seats filled (100 total)	Seats filled	0/10
Description: Prerequisite, MATH 110. 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. 3 units.
10679	STOR 155 - 004 Introduction to Data Models and Inference	MoWeFr 8:00AM - 8:50AM	Andrew Ackerman	Gardner - Rm 0008	Seats filled (40 total)	Seats filled	0/5
Description: Prerequisite, MATH 110. 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. 3 units.
4319	STOR 155 - 005 Introduction to Data Models and Inference	MoWeFr 8:00AM - 8:50AM	Andrew Ackerman	Gardner - Rm 0008	Seats filled (40 total)	Seats filled	0/10
Description: Prerequisite, MATH 110. 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. 3 units.
8706	STOR 155 - 006 Introduction to Data Models and Inference	MoWeFr 2:30PM - 3:20PM	Hank Flury	Hanes Hall - Rm 0125	Seats filled (40 total)	Seats filled	0/5
Description: Prerequisite, MATH 110. 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. 3 units.
10678	STOR 155 - 007 Introduction to Data Models and Inference	MoWeFr 4:40PM - 5:30PM	Panagiotis Andreou	Hanes Hall - Rm 0125	Seats filled (40 total)	Seats filled	0/5
Description: Prerequisite, MATH 110. 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. 3 units.
13523	STOR 155 - 008 Introduction to Data Models and Inference	TuTh 8:00AM - 9:15AM	Emma Mitchell	Gardner - Rm 0105	Seats filled (50 total)	Seats filled	0/5
Description: Prerequisite, MATH 110. 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. 3 units.
10677	STOR 155 - 009 Introduction to Data Models and Inference	TuTh 8:00AM - 9:15AM	Emma Mitchell	Gardner - Rm 0105	Seats filled (50 total)	Seats filled	0/5
Description: Prerequisite, MATH 110. 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. 3 units.
8366	STOR 155 - 010 Introduction to Data Models and Inference	TuTh 3:30PM - 4:45PM	Sumit Kumar Kar	Hanes Hall - Rm 0130	Seats filled (40 total)	Seats filled	0/5
Description: Prerequisite, MATH 110. 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. 3 units.
13524	STOR 155 - 011 Introduction to Data Models and Inference	TuTh 5:00PM - 6:15PM	Adrian Allen	Hanes Hall - Rm 0130	Seats filled (40 total)	Seats filled
Description: Prerequisite, MATH 110. 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. 3 units.
17376	STOR 155 - 012 Introduction to Data Models and Inference	MoWeFr 9:05AM - 9:55AM	Souvik Ray	Peabody - Rm 3018	Seats filled (35 total)	Seats filled
Description: Prerequisite, MATH 110. 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. 3 units.
4320	STOR 215 - 001 Foundations of Decision Sciences	TuTh 9:30AM - 10:45AM	VIDYADHAR KULKARNI	Hanes Hall - Rm 0120	Seats filled (80 total)	Seats filled
Description: Prerequisite, MATH 110. 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. 3 units.
14618	STOR 235 - 001 Mathematics for Data Science	MoWeFr 9:05AM - 9:55AM	JINGFANG HUANG	Manning - Rm 0209	44/60 (60 total)	Seats filled
Description: Prerequisites, MATH 231 and MATH 232. This course introduces students to some of the key mathematical tools underlying algorithmic data science. The primary focus of the course is matrix algebra and multivariable calculus. The mathematical topics covered in the course will be motivated and connected by concrete applications in data science, with an emphasis on machine learning and optimization. 4 units.
14619	STOR 235 - 600 Mathematics for Data Science	Th 8:00AM - 8:50AM	Han Cao	Phillips - Rm 0385	7/15 (15 total)	Seats filled
Description: Prerequisites, MATH 231 and MATH 232. This course introduces students to some of the key mathematical tools underlying algorithmic data science. The primary focus of the course is matrix algebra and multivariable calculus. The mathematical topics covered in the course will be motivated and connected by concrete applications in data science, with an emphasis on machine learning and optimization. 0 units.
14620	STOR 235 - 601 Mathematics for Data Science	Th 9:30AM - 10:20AM	Maddie Preston	Phillips - Rm 0228	13/15 (15 total)	Seats filled
Description: Prerequisites, MATH 231 and MATH 232. This course introduces students to some of the key mathematical tools underlying algorithmic data science. The primary focus of the course is matrix algebra and multivariable calculus. The mathematical topics covered in the course will be motivated and connected by concrete applications in data science, with an emphasis on machine learning and optimization. 0 units.
14621	STOR 235 - 602 Mathematics for Data Science	Th 12:30PM - 1:20PM	Maddie Preston	Phillips - Rm 0228	10/15 (15 total)	Seats filled
Description: Prerequisites, MATH 231 and MATH 232. This course introduces students to some of the key mathematical tools underlying algorithmic data science. The primary focus of the course is matrix algebra and multivariable calculus. The mathematical topics covered in the course will be motivated and connected by concrete applications in data science, with an emphasis on machine learning and optimization. 0 units.
14622	STOR 235 - 603 Mathematics for Data Science	Th 2:00PM - 2:50PM	Maddie Preston	Phillips - Rm 0228	14/15 (15 total)	Seats filled
Description: Prerequisites, MATH 231 and MATH 232. This course introduces students to some of the key mathematical tools underlying algorithmic data science. The primary focus of the course is matrix algebra and multivariable calculus. The mathematical topics covered in the course will be motivated and connected by concrete applications in data science, with an emphasis on machine learning and optimization. 0 units.
4321	STOR 305 - 001 Introduction to Decision Analytics	MoWeFr 12:20PM - 1:10PM	WILLIAM LASSITER	Gardner - Rm 0105	Seats filled (100 total)	Seats filled
Description: Prerequisite, STOR 120, 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. Students cannot enroll in STOR 305 if they have already taken STOR 415. 3 units.
10205	STOR 305 - 002 Introduction to Decision Analytics	MoWeFr 2:30PM - 3:20PM	WILLIAM LASSITER	Gardner - Rm 0105	45/50 (100 total)	Seats filled
Description: Prerequisite, STOR 120, 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. Students cannot enroll in STOR 305 if they have already taken STOR 415. 3 units.
14657	STOR 315 - 001 Discrete Mathematics for Data Science	TuTh 9:30AM - 10:45AM	Ali Mohammad Nezhad	Gardner - Rm 0105	31/35 (35 total)	Seats filled
Description: Prerequisite, MATH 232. The course gives a solid introduction to rigorous mathematical thinking and problem solving, all of which are fundamental in data science. It covers proofs, mathematical induction, counting, and the basics of graph theory. 4 units.
14686	STOR 315 - 600 Discrete Mathematics for Data Science	We 8:00AM - 8:50AM	Morgan Smith	Carolina Hall - Rm 0322	18/25 (25 total)	Seats filled
Description: Prerequisite, MATH 232. The course gives a solid introduction to rigorous mathematical thinking and problem solving, all of which are fundamental in data science. It covers proofs, mathematical induction, counting, and the basics of graph theory. 0 units.
14688	STOR 315 - 602 Discrete Mathematics for Data Science	Tu 5:00PM - 5:50PM	Morgan Smith	Dey Hall - Rm 0306	13/20 (20 total)	Seats filled
Description: Prerequisite, MATH 232. The course gives a solid introduction to rigorous mathematical thinking and problem solving, all of which are fundamental in data science. It covers proofs, mathematical induction, counting, and the basics of graph theory. 0 units.
8761	STOR 320 - 001 Introduction to Data Science	MoWeFr 2:30PM - 3:20PM	Jeff McLean	Hanes Hall - Rm 0120	Seats filled (100 total)	Seats filled
Description: 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. 4 units.
9248	STOR 320 - 002 Introduction to Data Science	TuTh 5:00PM - 6:15PM	Mario Giacomazzo	Hanes Hall - Rm 0120	Seats filled (65 total)	Seats filled
Description: 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. 4 units.
10119	STOR 320 - 400 Introduction to Data Science	We 8:00AM - 8:50AM	Grace Smith	Hanes Hall - Rm 0107	Seats filled (30 total)	Seats filled
Description: 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. 0 units.
10120	STOR 320 - 401 Introduction to Data Science	Fr 8:00AM - 8:50AM	Grace Smith	Hanes Hall - Rm 0107	16/30 (30 total)	Seats filled
Description: 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. 0 units.
10121	STOR 320 - 402 Introduction to Data Science	Tu 5:00PM - 5:50PM	Grace Smith	Hanes Hall - Rm 0107	29/30 (30 total)	Seats filled
Description: 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. 0 units.
11772	STOR 320 - 403 Introduction to Data Science	Th 5:00PM - 5:50PM	Daniel Meskill	Hanes Hall - Rm 0107	Seats filled (30 total)	Seats filled
Description: 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. 0 units.
10706	STOR 320 - 404 Introduction to Data Science	We 9:05AM - 9:55AM	Daniel Meskill	Phillips - Rm 0222	Seats filled (15 total)	Seats filled
Description: 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. 0 units.
11773	STOR 320 - 405 Introduction to Data Science	Fr 9:05AM - 9:55AM	Daniel Meskill	Phillips - Rm 0247	16/19 (19 total)	Seats filled
Description: 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. 0 units.
10122	STOR 320 - 406 Introduction to Data Science	We 4:40PM - 5:30PM	Yuhao Zhou	Hanes Hall - Rm 0107	Seats filled (19 total)	Seats filled
Description: 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. 0 units.
10583	STOR 320 - 407 Introduction to Data Science	Fr 4:40PM - 5:30PM	Yuhao Zhou	Hanes Hall - Rm 0107	17/19 (19 total)	Seats filled
Description: 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. 0 units.
9093	STOR 415 - 001 Introduction to Optimization	TuTh 11:00AM - 12:15PM	GABOR PATAKI	Hanes Hall - Rm 0120	Seats filled (80 total)	Seats filled
Description: Prerequisites, MATH 347 and STOR 315. Linear, integer, nonlinear, and dynamic programming, classical optimization problems, network theory. 3 units.
11774	STOR 415 - 002 Introduction to Optimization	TuTh 12:30PM - 1:45PM	Quoc Tran-Dinh	Gardner - Rm 0008	Seats filled (60 total)	Seats filled
Description: Prerequisites, MATH 347 and STOR 315. Linear, integer, nonlinear, and dynamic programming, classical optimization problems, network theory. 3 units.
4323	STOR 435 - 001 Introduction to Probability	MoWeFr 11:15AM - 12:05PM	Shankar Bhamidi	Hanes Hall - Rm 0120	Seats filled (80 total)	Seats filled
Description: Prerequisites, MATH/STOR 235 or MATH 233; and STOR 215 or MATH 381 or COMP 283. 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. Students may not receive credit for both STOR 435 and STOR 535. 3 units.
9472	STOR 435 - 002 Introduction to Probability	TuTh 3:30PM - 4:45PM	CHUANSHU JI	Gardner - Rm 0105	Seats filled (35 total)	Seats filled
Description: Prerequisites, MATH/STOR 235 or MATH 233; and STOR 215 or MATH 381 or COMP 283. 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. Students may not receive credit for both STOR 435 and STOR 535. 3 units.
11775	STOR 435 - 003 Introduction to Probability	TuTh 5:00PM - 6:15PM	Peter Rudzis	Gardner - Rm 0105	Seats filled (35 total)	Seats filled
Description: Prerequisites, MATH/STOR 235 or MATH 233; and STOR 215 or MATH 381 or COMP 283. 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. Students may not receive credit for both STOR 435 and STOR 535. 3 units.
4324	STOR 445 - 001 Stochastic Modeling	MoWeFr 9:05AM - 9:55AM	SERHAN ZIYA	Gardner - Rm 0105	Seats filled (100 total)	Seats filled
Description: Prerequisite, BIOS 660, STOR 435 or 535. 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. 3 units.
11776	STOR 445 - 002 Stochastic Modeling	TuTh 12:30PM - 1:45PM	VIDYADHAR KULKARNI	Hanes Hall - Rm 0120	Seats filled (60 total)	Seats filled
Description: Prerequisite, BIOS 660, STOR 435 or 535. 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. 3 units.
4325	STOR 455 - 001 Methods of Data Analysis	TuTh 12:30PM - 1:45PM	KAI ZHANG	Gardner - Rm 0105	Seats filled (100 total)	Seats filled
Description: Prerequisite, STOR 120, or 155. 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. 3 units.
9092	STOR 455 - 002 Methods of Data Analysis	TuTh 2:00PM - 3:15PM	Mario Giacomazzo	Gardner - Rm 0105	Seats filled (100 total)	Seats filled
Description: Prerequisite, STOR 120, or 155. 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. 3 units.
10199	STOR 475 - 001 Healthcare Risk Analytics	Tu 3:30PM - 6:30PM	Frederick Kelly	Hanes Hall - Rm 0125	Seats filled (47 total)	Seats filled
Description: Prerequisite, STOR 435, or 535. This course will introduce students to the healthcare industry and provide hands-on experience with key actuarial and analytical concepts that apply across the actuarial field. Using real world situations, the course will focus on how mathematics and the principles of risk management are used to help insurance companies and employers make better decisions regarding employee benefit insurance products and programs. 3 units.
10707	STOR 520 - 001 Statistical Computing for Data Science	TuTh 5:00PM - 6:15PM	Mario Giacomazzo	Hanes Hall - Rm 0120	Seats filled (10 total)	Seats filled
Description: Prerequisites, STOR 435 or 535, and STOR 455. This course provides hands-on experience working with data sets provided in class and downloaded from certain public websites. Lectures cover basic topics such as R programming, visualization, data wrangling and cleaning, exploratory data analysis, web scraping, data merging, predictive modeling, and elements of machine learning. Programming analyses in more advanced areas of data science. Students may not receive credit for both STOR 320 and STOR 520. 4 units.
13517	STOR 520 - 400 Statistical Computing for Data Science	We 9:05AM - 9:55AM	Daniel Meskill	Phillips - Rm 0222	2/5 (5 total)	Seats filled
Description: Prerequisites, STOR 435 or 535, and STOR 455. This course provides hands-on experience working with data sets provided in class and downloaded from certain public websites. Lectures cover basic topics such as R programming, visualization, data wrangling and cleaning, exploratory data analysis, web scraping, data merging, predictive modeling, and elements of machine learning. Programming analyses in more advanced areas of data science. Students may not receive credit for both STOR 320 and STOR 520. 0 units.
13516	STOR 520 - 401 Statistical Computing for Data Science	Fr 9:05AM - 9:55AM	Daniel Meskill	Phillips - Rm 0247	4/5 (5 total)	Seats filled
Description: Prerequisites, STOR 435 or 535, and STOR 455. This course provides hands-on experience working with data sets provided in class and downloaded from certain public websites. Lectures cover basic topics such as R programming, visualization, data wrangling and cleaning, exploratory data analysis, web scraping, data merging, predictive modeling, and elements of machine learning. Programming analyses in more advanced areas of data science. Students may not receive credit for both STOR 320 and STOR 520. 0 units.
10708	STOR 520 - 402 Statistical Computing for Data Science	We 4:40PM - 5:30PM	Yuhao Zhou	Hanes Hall - Rm 0107	5/10 (10 total)	Seats filled
Description: Prerequisites, STOR 435 or 535, and STOR 455. This course provides hands-on experience working with data sets provided in class and downloaded from certain public websites. Lectures cover basic topics such as R programming, visualization, data wrangling and cleaning, exploratory data analysis, web scraping, data merging, predictive modeling, and elements of machine learning. Programming analyses in more advanced areas of data science. Students may not receive credit for both STOR 320 and STOR 520. 0 units.
11779	STOR 520 - 403 Statistical Computing for Data Science	Fr 4:40PM - 5:30PM	Yuhao Zhou	Hanes Hall - Rm 0107	0/10 (10 total)	Seats filled
Description: Prerequisites, STOR 435 or 535, and STOR 455. This course provides hands-on experience working with data sets provided in class and downloaded from certain public websites. Lectures cover basic topics such as R programming, visualization, data wrangling and cleaning, exploratory data analysis, web scraping, data merging, predictive modeling, and elements of machine learning. Programming analyses in more advanced areas of data science. Students may not receive credit for both STOR 320 and STOR 520. 0 units.
10200	STOR 535 - 001 Probability for Data Science	MoWeFr 1:25PM - 2:15PM	Nicolas Fraiman	Gardner - Rm 0105	Seats filled (75 total)	Seats filled
Description: Prerequisites, MATH/STOR 235 or MATH 233; and STOR 215 or MATH 381 or COMP 283. 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. 3 units.
4326	STOR 555 - 001 Mathematical Statistics	TuTh 11:00AM - 12:15PM	Jan Hannig	Gardner - Rm 0105	Seats filled (25 total)	Seats filled
Description: Prerequisite, STOR 435, or 535. Functions of random samples and their probability distributions, introductory theory of point and interval estimation and hypothesis testing, elementary decision theory. 3 units.
10711	STOR 557 - 001 Advanced Methods of Data Analysis	TuTh 3:30PM - 4:45PM	RICHARD SMITH	Hanes Hall - Rm 0120	Seats filled (50 total)	Seats filled
Description: Prerequisites, STOR 435 or 535, and STOR 455. The course covers advanced data analysis methods beyond those in STOR 455 and how to apply them in a modern computer package, specifically R or R-Studio which are the primary statistical packages for this kind of analysis. Specific topics include (a) Generalized Linear Models; (b) Random Effects; (c) Bayesian Statistics; (d) Nonparametric Methods (kernels, splines and related techniques). 3 units.
9473	STOR 565 - 001 Machine Learning	MoWeFr 11:15AM - 12:05PM	YUFENG LIU	Gardner - Rm 0105	32/38 (85 total)	Seats filled
Description: Prerequisites, STOR 215 or MATH 381, and STOR 435 or 535. 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. 3 units.
4327	STOR 612 - 001 Foundations of Optimization	TuTh 9:30AM - 10:45AM	Michael O'Neill	Hanes Hall - Rm 0130	34/46 (46 total)	Seats filled	0/5
Description: Prerequisites, MATH 347 and 521 or permission of the instructor. STOR 612 consists of three major parts: linear programming, quadratic programming, and unconstrained optimization. Topics: Modeling, theory and algorithms for linear programming; modeling, theory and algorithms for quadratic programming; convex sets and functions; first-order and second-order methods such as stochastic gradient methods, accelerated gradient methods and quasi-Newton methods for unconstrained optimization. 3 units.
4375	STOR 634 - 001 Probability I	MoWe 11:15AM - 12:30PM	Xiangying Huang	Hanes Hall - Rm 0130	13/46 (46 total)	Seats filled	0/5
Description: Required preparation, advanced calculus. Lebesgue and abstract measure and integration, convergence theorems, differentiation. Radon-Nikodym theorem, product measures. Fubini theorems. Lp spaces. 3 units.
4328	STOR 641 - 001 Stochastic Modeling I	TuTh 11:00AM - 12:15PM	Mariana Olvera-Cravioto	Hanes Hall - Rm 0130	31/46 (46 total)	Seats filled	0/5
Description: Prerequisite, Probability background at the level of STOR 435 or STOR 535. The aim of this 3-credit graduate course is to introduce stochastic modeling that is commonly used in various fields such as operations research, data science, engineering, business, and life sciences. Although it is the first course in a sequence of three courses, it can also serve as a standalone introductory course in stochastic modeling and analysis. The course covers the following topics: discrete-time Markov chains, Poisson processes, and continuous-time Markov chains. 3 units.
4376	STOR 654 - 001 Statistical Theory I	TuTh 2:00PM - 3:15PM	Jan Hannig	Hanes Hall - Rm 0107	21/34 (34 total)	Seats filled	0/5
Description: Required preparation, two semesters of advanced calculus. Probability spaces. Random variables, distributions, expectation. Conditioning. Generating functions. Limit theorems: LLN, CLT, Slutsky, delta-method, big-O in probability. Inequalities. Distribution theory: normal, chi-squared, beta, gamma, Cauchy, other multivariate distributions. Distribution theory for linear models. 3 units.
4377	STOR 664 - 001 Applied Statistics I	TuTh 12:30PM - 1:45PM	RICHARD SMITH	Gardner - Rm 0210	23/47 (47 total)	Seats filled	0/5
Description: Permission of the instructor. Basics of linear models: matrix formulation, least squares, tests. Computing environments: SAS, MATLAB, S+. Visualization: histograms, scatterplots, smoothing, QQ plots. Transformations: log, Box-Cox, etc. Diagnostics and model selection. 3 units.
7620	STOR 701 - 001 Statistics and Operations Research Colloquium	Mo 3:30PM - 5:00PM	Quoc Tran-Dinh	Hanes Hall - Rm 0120	44/90 (100 total)	Seats filled	0/10
Description: This seminar course is intended to give Ph.D. students exposure to cutting edge research topics in statistics and operations research and assist them in their choice of a dissertation topic. The course also provides a forum for students to meet and learn from major researchers in the field. 1 units.
13667	STOR 743 - 001 Reinforcement Learning and Markov Decision Processes	MoWe 9:05AM - 10:20AM	Guanting Chen	Hanes Hall - Rm 0107	27/34 (34 total)	Seats filled
Description: Prerequisite, STOR 641 or permission of instructor. Markov decision processes (stochastic dynamic programming): finite horizon, infinite horizon, discounted and average-cost criteria; reinforcement learning(RL): design and analysis of model-free, model-based, value-based, and policy-based RL algorithms, RL algorithms in continuous and discrete state and action space, and RL with functional approximation. These algorithms include but are not limited to (deep) Q-learning, asynchronous advantage actor-critic, soft actor-critic, and proximal policy optimization. 3 units.
7999	STOR 765 - 001 Statistical Consulting	TuTh 3:30PM - 4:45PM	James Marron	Hanes Hall - Rm 0107	6/25 (25 total)	Seats filled	0/5
Description: Application of statistics to real problems presented by researchers from the University and local companies and institutes. (Taught over two semesters for a total of 3 credits.) 1.5 units.
10039	STOR 767 - 001 Advanced Statistical Machine Learning	MoWe 1:25PM - 2:40PM	ANDREW NOBEL	Hanes Hall - Rm 0130	8/35 (35 total)	Seats filled	0/5
Description: Prerequisites, STOR 654, 655, 664, 665 and permission of the instructor. This is a graduate course on statistical machine learning. 3 units.
9094	STOR 890 - 001 Special Problems	MoWe 11:15AM - 12:30PM	Sayan Banerjee	Hanes Hall - Rm 0107	8/34 (34 total)	Seats filled	0/5
Description: Permission of the instructor. 1 - 3 units.
10040	STOR 892 - 001 Special Topics in Operations Research and Systems Analysis	TuTh 3:30PM - 4:45PM	GABOR PATAKI	Dey Hall - Rm 0307	9/34 (34 total)	Seats filled	0/5
Description: Permission of the instructor. 1 - 3 units.
12894	STOR 893 - 001 Special Topics	MoWe 9:30AM - 10:45AM	Nicolas Fraiman	Graham Memorial - Rm 0035	7/20 (20 total)	Seats filled
Description: Advance topics in current research in statistics and operations research. 1 - 3 units.