STOR Courses

Here is information about STOR class enrollment for spring 2024. Classes with no meeting time listed are not shown. Feel free to contact me with any questions/comments/issues. I am happy to add any departments that are missing from these listings, just reach out to ask!

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Data last updated: 2024-03-04 09:50:19.465514

Class Number	Class	Meeting Time	Instructor	Room	Unreserved Enrollment	Reserved Enrollment	Wait List
1769	STOR 113 - 001 Decision Models for Business and Economics	MoWeFr 10:10AM - 11:00AM	Quoc Tran-Dinh	Hanes Hall-Rm 0120	Seats filled (100 total)	Seats filled	0/999
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.
10212	STOR 113 - 002 Decision Models for Business and Economics	TuTh 9:30AM - 10:45AM	Grigory Terlov	Hanes Hall-Rm 0130	Seats filled (40 total)	Seats filled	0/999
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.
11689	STOR 115 - 001 Reasoning with Data: Navigating a Quantitative World	MoWeFr 10:10AM - 11:00AM	Linda Green	Phillips Hall-Rm 0215	Seats filled (22 total)	Seats filled	0/999
Description: Students will use mathematical and statistical methods to address societal problems, make personal decisions, and reason critically about the world. Authentic contexts may include voting, health and risk, digital humanities, finance, and human behavior. This course does not count as credit towards the psychology or neuroscience majors. 3 units.
16015	STOR 115 - 002 Reasoning with Data: Navigating a Quantitative World	TuTh 12:30PM - 1:45PM	Mark McCombs	Wilson Hall-Rm 0107	Seats filled (15 total)	Seats filled
Description: Students will use mathematical and statistical methods to address societal problems, make personal decisions, and reason critically about the world. Authentic contexts may include voting, health and risk, digital humanities, finance, and human behavior. This course does not count as credit towards the psychology or neuroscience majors. 3 units.
9296	STOR 120 - 001 Foundations of Statistics and Data Science	MoWeFr 8:00AM - 8:50AM	Oluremi Abayomi	Hanes Hall-Rm 0120	Seats filled (100 total)	Seats filled	0/999
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.
9297	STOR 120 - 002 Foundations of Statistics and Data Science	MoWeFr 9:05AM - 9:55AM	Oluremi Abayomi	Hanes Hall-Rm 0120	Seats filled (100 total)	Seats filled	0/999
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.
11746	STOR 120 - 003 Foundations of Statistics and Data Science	MoWeFr 11:15AM - 12:05PM	Jeff McLean	Hanes Hall-Rm 0120	42/43 (100 total)	Seats filled	0/999
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.
9303	STOR 120 - 400 Foundations of Statistics and Data Science	Mo 9:00AM - 9:50AM	Aidan Burchard	Hanes Hall-Rm 0107	Seats filled (20 total)	Seats filled	0/999
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.
10214	STOR 120 - 401 Foundations of Statistics and Data Science	Tu 9:30AM - 10:20AM	Trung Nghia Nguyen	Hanes Hall-Rm 0107	Seats filled (20 total)	Seats filled	0/999
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.
9302	STOR 120 - 402 Foundations of Statistics and Data Science	We 1:00PM - 1:50PM	Daniel Meskill	Hanes Hall-Rm 0107	Seats filled (20 total)	Seats filled	0/999
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.
9301	STOR 120 - 403 Foundations of Statistics and Data Science	Th 5:00PM - 5:50PM	Trung Nghia Nguyen	Hanes Hall-Rm 0107	Seats filled (20 total)	Seats filled	0/999
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.
9300	STOR 120 - 404 Foundations of Statistics and Data Science	Tu 11:00AM - 11:50AM	Trung Nghia Nguyen	Hanes Hall-Rm 0107	Seats filled (20 total)	Seats filled	0/999
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.
10213	STOR 120 - 405 Foundations of Statistics and Data Science	We 11:00AM - 11:50AM	Daniel Meskill	Hanes Hall-Rm 0107	Seats filled (20 total)	Seats filled	0/999
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.
9299	STOR 120 - 406 Foundations of Statistics and Data Science	Th 12:30PM - 1:20PM	Daniel Meskill	Hanes Hall-Rm 0107	Seats filled (20 total)	Seats filled	0/999
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.
9298	STOR 120 - 407 Foundations of Statistics and Data Science	Fr 3:00PM - 3:50PM	Ishmael Benjamin Torres Aguilar	Hanes Hall-Rm 0107	Seats filled (20 total)	Seats filled	0/999
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.
11747	STOR 120 - 408 Foundations of Statistics and Data Science	Th 9:30AM - 10:20AM	Ishmael Benjamin Torres Aguilar	Hanes Hall-Rm 0107	Seats filled (20 total)	Seats filled	0/999
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.
11748	STOR 120 - 409 Foundations of Statistics and Data Science	Fr 1:00PM - 1:50PM	Aidan Burchard	Hanes Hall-Rm 0107	Seats filled (20 total)	Seats filled	0/999
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.
11749	STOR 120 - 410 Foundations of Statistics and Data Science	Mo 1:00PM - 1:50PM	Ishmael Benjamin Torres Aguilar	Hanes Hall-Rm 0107	Seats filled (20 total)	Seats filled	0/999
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.
11750	STOR 120 - 411 Foundations of Statistics and Data Science	Tu 5:00PM - 5:50PM	Aidan Burchard	Hanes Hall-Rm 0107	Seats filled (20 total)	Seats filled	0/999
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.
1770	STOR 151 - 001 Introduction to Data Analysis	TuTh 2:00PM - 3:15PM	RICHARD SMITH	Gardner Hall-Rm 0105	Seats filled (100 total)	Seats filled	0/999
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.
1771	STOR 155 - 001 Introduction to Data Models and Inference	MoWeFr 11:15AM - 12:05PM	Oluremi Abayomi	Gardner Hall-Rm 0105	Seats filled (100 total)	Seats filled	0/999
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.
3382	STOR 155 - 002 Introduction to Data Models and Inference	TuTh 3:30PM - 4:45PM	CHUANSHU JI	Gardner Hall-Rm 0105	Seats filled (80 total)	Seats filled	0/999
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.
1773	STOR 155 - 003 Introduction to Data Models and Inference	TuTh 11:00AM - 12:15PM	Sayan Banerjee	Hanes Hall-Rm 0120	Seats filled (100 total)	Seats filled	0/999
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.
1774	STOR 155 - 004 Introduction to Data Models and Inference	MoWeFr 8:00AM - 8:50AM	Sumit Kumar Kar	Gardner Hall-Rm 0105	Seats filled (100 total)	Seats filled	0/999
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.
1775	STOR 155 - 005 Introduction to Data Models and Inference	MoWeFr 8:00AM - 8:50AM	Dilay Ozkan	Carolina Hall-Rm 0220	Seats filled (50 total)	Seats filled	0/999
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.
5330	STOR 155 - 006 Introduction to Data Models and Inference	MoWeFr 9:05AM - 9:55AM	Shaleni Kovach	Hanes Hall-Rm 0125	Seats filled (40 total)	Seats filled	0/999
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.
13198	STOR 155 - 007 Introduction to Data Models and Inference	TuTh 8:00AM - 9:15AM	Fuwei Yu	Carolina Hall-Rm 0220	Seats filled (50 total)	Seats filled	0/999
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.
1778	STOR 155 - 008 Introduction to Data Models and Inference	MoWeFr 11:15AM - 12:05PM	Adrian Allen	Peabody Hall-Rm 1040	Seats filled (100 total)	Seats filled	0/999
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.
13197	STOR 155 - 009 Introduction to Data Models and Inference	MoWeFr 12:20PM - 1:10PM	Yuhao Zhou	Hanes Hall-Rm 0130	Seats filled (40 total)	Seats filled	0/999
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.
13196	STOR 155 - 010 Introduction to Data Models and Inference	MoWeFr 8:00AM - 8:50AM	Izzet Egemen Elver	Hanes Hall-Rm 0125	Seats filled (40 total)	Seats filled	0/999
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.
13199	STOR 235 - 001 Mathematics for Data Science	MoWeFr 10:10AM - 11:00AM	Shankar Bhamidi	Gardner Hall-Rm 0105	26/52 (60 total)	Seats filled	0/999
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.
13210	STOR 235 - 600 Mathematics for Data Science	Mo 5:00PM - 5:50PM	Tianzhu Liu	Hanes Hall-Rm 0130	10/15 (15 total)	Seats filled	0/999
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.
13211	STOR 235 - 601 Mathematics for Data Science	We 5:00PM - 5:50PM	Tianzhu Liu	Hanes Hall-Rm 0130	4/15 (15 total)	Seats filled	0/999
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.
13212	STOR 235 - 602 Mathematics for Data Science	Th 12:30PM - 1:20PM	Geonhyeok Jeong	Hanes Hall-Rm 0130	12/15 (15 total)	Seats filled	0/999
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.
13213	STOR 235 - 603 Mathematics for Data Science	Fr 1:30PM - 2:20PM	Geonhyeok Jeong	Hanes Hall-Rm 0130	8/15 (15 total)	Seats filled	0/999
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.
5631	STOR 305 - 001 Introduction to Decision Analytics	TuTh 5:00PM - 6:15PM	Ali Mohammad Nezhad	Hanes Hall-Rm 0120	Seats filled (90 total)	Seats filled	0/999
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.
13200	STOR 315 - 001 Discrete Mathematics for Data Science	TuTh 12:30PM - 1:45PM	Mariana Olvera-Cravioto	Hanes Hall-Rm 0120	14/65 (100 total)	Seats filled	0/999
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.
13207	STOR 315 - 601 Discrete Mathematics for Data Science	Fr 9:00AM - 9:50AM	Nikolaos Dimou	Hanes Hall-Rm 0130	20/40 (40 total)	Seats filled	0/999
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.
13205	STOR 315 - 603 Discrete Mathematics for Data Science	Th 3:30PM - 4:20PM	Nikolaos Dimou	Hanes Hall-Rm 0130	29/40 (40 total)	Seats filled	0/999
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.
5339	STOR 320 - 001 Introduction to Data Science	TuTh 8:00AM - 9:15AM	Mario Giacomazzo	Hanes Hall-Rm 0120	Seats filled (90 total)	Seats filled	0/999
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.
5628	STOR 320 - 002 Introduction to Data Science	TuTh 9:30AM - 10:45AM	Mario Giacomazzo	Hanes Hall-Rm 0120	Seats filled (90 total)	Seats filled	0/999
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.
9292	STOR 320 - 400 Introduction to Data Science	We 9:00AM - 9:50AM	Anna Myakushina	Hanes Hall-Rm 0107	Seats filled (20 total)	Seats filled	0/999
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.
9291	STOR 320 - 401 Introduction to Data Science	Mo 11:00AM - 11:50AM	Coleman Ferrell	Hanes Hall-Rm 0107	Seats filled (20 total)	Seats filled	0/999
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.
10215	STOR 320 - 402 Introduction to Data Science	Tu 2:00PM - 2:50PM	Anna Myakushina	Hanes Hall-Rm 0107	Seats filled (20 total)	Seats filled	0/999
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.
9290	STOR 320 - 403 Introduction to Data Science	We 5:00PM - 5:50PM	Coleman Ferrell	Hanes Hall-Rm 0107	Seats filled (20 total)	Seats filled	0/999
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.
9295	STOR 320 - 404 Introduction to Data Science	Fr 9:00AM - 9:50AM	Coleman Ferrell	Hanes Hall-Rm 0107	Seats filled (20 total)	Seats filled	0/999
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.
9294	STOR 320 - 405 Introduction to Data Science	Th 11:00AM - 11:50AM	Andrew Nguyen	Hanes Hall-Rm 0107	Seats filled (20 total)	Seats filled	0/999
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.
9293	STOR 320 - 406 Introduction to Data Science	Fr 2:00PM - 2:50PM	Andrew Nguyen	Hanes Hall-Rm 0107	Seats filled (20 total)	Seats filled	0/999
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.
10216	STOR 320 - 407 Introduction to Data Science	Mo 5:00PM - 5:50PM	Anna Myakushina	Hanes Hall-Rm 0107	Seats filled (20 total)	Seats filled	0/999
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.
12398	STOR 390 - 001 Special Topics in Statistics and Operations Research	MoWeFr 8:00AM - 8:50AM	Andrew Ackerman	Hanes Hall-Rm 0130	Seats filled (25 total)	Seats filled	0/999
Description: Examines selected topics from statistics and operations research. Course description is available from the department office. 3 units.
1777	STOR 415 - 001 Introduction to Optimization	MoWeFr 2:30PM - 3:20PM	Michael O'Neill	Gardner Hall-Rm 0105	Seats filled (100 total)	Seats filled	0/999
Description: Prerequisites, MATH 347 and STOR 315. Linear, integer, nonlinear, and dynamic programming, classical optimization problems, network theory. 3 units.
1785	STOR 435 - 001 Introduction to Probability	TuTh 11:00AM - 12:15PM	CHUANSHU JI	Gardner Hall-Rm 0105	25/26 (60 total)	Seats filled	0/999
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.
1786	STOR 435 - 002 Introduction to Probability	MoWeFr 12:20PM - 1:10PM	Xiangying Huang	Gardner Hall-Rm 0105	Seats filled (80 total)	Seats filled	0/999
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.
11751	STOR 435 - 003 Introduction to Probability	TuTh 5:00PM - 6:15PM	Peter Rudzis	Gardner Hall-Rm 0105	Seats filled (75 total)	Seats filled	0/999
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.
4535	STOR 445 - 001 Stochastic Modeling	TuTh 8:00AM - 9:15AM	Guanting Chen	Gardner Hall-Rm 0105	Seats filled (100 total)	Seats filled	0/999
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.
4536	STOR 455 - 001 Methods of Data Analysis	MoWeFr 1:25PM - 2:15PM	Jeff McLean	Gardner Hall-Rm 0105	Seats filled (100 total)	Seats filled	0/999
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.
5331	STOR 455 - 002 Methods of Data Analysis	MoWeFr 2:30PM - 3:20PM	Jeff McLean	Hanes Hall-Rm 0120	Seats filled (100 total)	Seats filled	0/999
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.
13201	STOR 471 - 001 Long-Term Actuarial Models	MoWe 5:45PM - 7:00PM	CHARLES DUNN	Hanes Hall-Rm 0125	6/30 (40 total)	Seats filled	0/999
Description: Prerequisite, STOR 435, or 535. 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. 3 units.
15178	STOR 512 - 001 Optimization for Machine Learning and Neural Networks	MoWeFr 12:20PM - 1:10PM	Quoc Tran-Dinh	Hanes Hall-Rm 0120	21/100 (100 total)	Seats filled
Description: Prerequisites, STOR 415 or STOR 612; or MATH 233 and MATH 347, or MATH 235, and COMP 110 or COMP 116; or permission of instructor. This is an upper-level course focusing on optimization aspects of common and practical problems and topics in statistical learning, machine learning, neural networks, and modern AI. It covers several topics such as optimization perspective of linear regression, nonlinear regression, matrix factorization, stochastic gradient descent, regularization techniques, neural networks, deep learning techniques, and minimax models. 3 units.
10287	STOR 515 - 001 Dynamic Decision Analytics	MoWeFr 1:25PM - 2:15PM	WILLIAM LASSITER	Hanes Hall-Rm 0120	36/100 (100 total)	Seats filled	0/999
Description: Prerequisites, STOR 435 or 535, and MATH 347. An introduction to algorithms and modeling techniques that use knowledge gained from prior experience to make intelligent decisions in real time. Topics include Markov decision processes, dynamic programming, multiplicative weights update, exploration vs. exploitation, multi-armed bandits, and two player games. 3 units.
11752	STOR 538 - 001 Sports Analytics	TuTh 12:30PM - 1:45PM	Mario Giacomazzo	Gardner Hall-Rm 0105	Seats filled (100 total)	Seats filled	0/999
Description: Prerequisite, STOR 320 or STOR 455. This course will survey the history of sports analytics across multiple areas and challenge students in team-based projects to practice sports analytics. Students will learn how applied statistics and mathematics help decision makers gain competitive advantages for on-field performance and off-field business decisions. 3 units.
9289	STOR 556 - 001 Time Series Data Analysis	TuTh 3:30PM - 4:45PM	KAI ZHANG	Hanes Hall-Rm 0120	22/25 (70 total)	Seats filled	0/999
Description: Prerequisites, STOR 435 or 535, and STOR 455. This course covers the fundamental theory and methods for time series data, as well as related statistical software and real-world data applications. Topics include the autocorrelation function, estimation and elimination of trend and seasonality, estimation and forecasting procedures in ARMA models and nonstationary time series models. 3 units.
5058	STOR 565 - 001 Machine Learning	TuTh 2:00PM - 3:15PM	Zhengwu Zhang	Hanes Hall-Rm 0120	Seats filled (80 total)	Seats filled	0/999
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.
10288	STOR 572 - 001 Simulation for Analytics	MoWeFr 10:10AM - 11:00AM	WILLIAM LASSITER	Hanes Hall-Rm 0125	Seats filled (35 total)	Seats filled	0/999
Description: Prerequisites, STOR 120 or 155, and STOR 435 or 535. This upper-level-undergraduate and beginning-graduate-level course introduces the concepts of modeling, programming, and statistical analysis as they arise in stochastic computer simulations. Topics include modeling static and discrete-event simulations of stochastic systems, random number generation, random variate generation, simulation programming, and statistical analysis of simulation input and output. 3 units.
1780	STOR 614 - 001 Advanced Optimization	MoWe 1:25PM - 2:40PM	GABOR PATAKI	Hanes Hall-Rm 0130	10/34 (34 total)	Seats filled	0/999
Description: Prerequisite, STOR 612 or equivalent (or permission of instructor). STOR 614 consists of three major parts: Integer programming, conic programming, and nonlinear optimization. Topics: modeling, theory and algorithms for integer programming; second-order cone and semidefinite programming; theory and algorithms for constrained optimization; dynamic programming; networks. 3 units.
1781	STOR 635 - 001 Probability II	TuTh 2:00PM - 3:15PM	Sayan Banerjee	Hanes Hall-Rm 0130	11/34 (34 total)	Seats filled	0/999
Description: Prerequisite, STOR 634; permission of the instructor for students lacking the prerequisite. Foundations of probability. Basic classical theorems. Modes of probabilistic convergence. Central limit problem. Generating functions, characteristic functions. Conditional probability and expectation. 3 units.
1782	STOR 642 - 001 Stochastic Modeling II	TuTh 9:30AM - 10:45AM	VIDYADHAR KULKARNI	Hanes Hall-Rm 0125	18/40 (40 total)	Seats filled	0/999
Description: Prerequisite, STOR 641. This 3-credit course is the second graduate-level course on stochastic modeling that expands upon the material taught in STOR 641. The course covers the following topics: renewal and regenerative processes, queueing models, and Markov decision processes. 3 units.
1783	STOR 655 - 001 Statistical Theory II	MoWe 11:15AM - 12:30PM	ANDREW NOBEL, Kyung Rok Kim	Hanes Hall-Rm 0125	9/34 (34 total)	Seats filled	0/999
Description: Prerequisite, STOR 654. Point estimation. Hypothesis testing and confidence sets. Contingency tables, nonparametric goodness-of-fit. Linear model optimality theory: BLUE, MVU, MLE. Multivariate tests. Introduction to decision theory and Bayesian inference. 3 units.
1784	STOR 665 - 001 Applied Statistics II	TuTh 11:00AM - 12:15PM	KAI ZHANG	Hanes Hall-Rm 0130	7/40 (40 total)	Seats filled	0/999
Description: Prerequisite, STOR 664; permission of the instructor for students lacking the prerequisite. ANOVA (including nested and crossed models, multiple comparisons). GLM basics: exponential families, link functions, likelihood, quasi-likelihood, conditional likelihood. Numerical analysis: numerical linear algebra, optimization; GLM diagnostics. Simulation: transformation, rejection, Gibbs sampler. 3 units.
3448	STOR 701 - 001 Statistics and Operations Research Colloquium	Mo 3:30PM - 5:00PM	Zhengwu Zhang, Quoc Tran-Dinh	Hanes Hall-Rm 0120	47/100 (100 total)	Seats filled	0/999
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.
16267	STOR 701 - 002 Statistics and Operations Research Colloquium	Fr 3:00PM - 4:00PM	Zhengwu Zhang	Hanes Hall-Rm 0125	6/40 (40 total)	Seats filled
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.
4206	STOR 765 - 001 Statistical Consulting	TuTh 3:30PM - 4:45PM	James Marron	Hanes Hall-Rm 0107	6/25 (25 total)	Seats filled	0/999
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.
13208	STOR 881 - 001 Object Oriented Data Analysis	TuTh 12:30PM - 1:45PM	James Marron	Dey Hall-Rm 0307	18/40 (40 total)	Seats filled	0/999
Description: Object Oriented Data Analysis (OODA) is the statistical analysis of populations of complex objects. Examples include data sets where the data points could be curves, images, shapes, movies, or tree structured objects. 1 - 3 units.
13202	STOR 891 - 001 Special Problems	MoWe 9:05AM - 10:20AM	VLADAS PIPIRAS	Hanes Hall-Rm 0130	20/34 (34 total)	Seats filled	0/999
Description: Permission of the instructor. 1 - 3 units.
13203	STOR 892 - 001 Special Topics in Operations Research and Systems Analysis	MoWe 11:15AM - 12:30PM	Nilay Argon	Greenlaw Hall-Rm 0301	9/34 (34 total)	Seats filled	0/999
Description: Permission of the instructor. 1 - 3 units.