STOR Courses

Here is information about STOR class enrollment for spring 2025. 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: 2025-01-23 11:38:38.984558

Class Number	Class	Meeting Time	Instructor	Room	Unreserved Enrollment	Reserved Enrollment	Total Enrollment	Wait List
1630	STOR 113 - 001 Decision Models for Business and Economics	TuTh 11:00AM - 12:15PM	SERHAN ZIYA	Hanes Hall-Rm 0120	Seats filled	Seats filled	90/90	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.
9384	STOR 113 - 002 Decision Models for Business and Economics	MoWeFr 12:20PM - 1:10PM	Peter Rudzis	Hanes Hall-Rm 0120	Seats filled	Seats filled	50/50	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.
10450	STOR 115 - 001 Reasoning with Data: Navigating a Quantitative World	MoWeFr 10:10AM - 11:00AM	Joseph Compton	Phillips Hall-Rm 0215	16/22	Seats filled	16/22	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.
8616	STOR 120 - 001 Foundations of Statistics and Data Science	MoWeFr 10:10AM - 11:00AM	Jeff McLean	Gardner Hall-Rm 0105	Seats filled	Seats filled	60/60	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.
8617	STOR 120 - 002 Foundations of Statistics and Data Science	MoWeFr 11:15AM - 12:05PM	Jeff McLean	Gardner Hall-Rm 0105	Seats filled	Seats filled	70/70	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.
10498	STOR 120 - 003 Foundations of Statistics and Data Science	MoWeFr 11:15AM - 12:05PM	Oluremi Abayomi	Hanes Hall-Rm 0120	Seats filled	Seats filled	55/55	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.
8623	STOR 120 - 400 Foundations of Statistics and Data Science	Mo 8:00AM - 8:50AM	Aidan Burchard	Hanes Hall-Rm 0107	3/25	Seats filled	3/25	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.
9386	STOR 120 - 401 Foundations of Statistics and Data Science	Mo 9:05AM - 9:55AM	Ciaran Mccollum	Hanes Hall-Rm 0107	20/25	Seats filled	20/25	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.
8622	STOR 120 - 402 Foundations of Statistics and Data Science	Tu 9:30AM - 10:20AM	Aidan Burchard	Hanes Hall-Rm 0107	24/25	Seats filled	24/25	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.
8621	STOR 120 - 403 Foundations of Statistics and Data Science	We 12:00PM - 12:50PM	Aidan Burchard	Hanes Hall-Rm 0107	20/25	Seats filled	20/25	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.
8620	STOR 120 - 404 Foundations of Statistics and Data Science	Tu 5:00PM - 5:50PM	Ciaran Mccollum	Hanes Hall-Rm 0107	20/25	Seats filled	20/25	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.
9385	STOR 120 - 405 Foundations of Statistics and Data Science	We 8:00AM - 8:50AM	Ciaran Mccollum	Hanes Hall-Rm 0107	9/25	Seats filled	9/25	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.
8619	STOR 120 - 406 Foundations of Statistics and Data Science	We 9:05AM - 9:55AM	Ciaran Mccollum	Hanes Hall-Rm 0107	21/25	Seats filled	21/25	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.
8618	STOR 120 - 407 Foundations of Statistics and Data Science	Th 9:30AM - 10:20AM	Aidan Burchard	Hanes Hall-Rm 0107	23/25	Seats filled	23/25	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.
10499	STOR 120 - 408 Foundations of Statistics and Data Science	Th 11:00AM - 11:50AM	Yikai Wang	Hanes Hall-Rm 0107	20/25	Seats filled	20/25	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.
10500	STOR 120 - 409 Foundations of Statistics and Data Science	Th 5:00PM - 5:50PM	Yikai Wang	Hanes Hall-Rm 0107	16/25	Seats filled	16/25	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.
10501	STOR 120 - 410 Foundations of Statistics and Data Science	Fr 9:05AM - 9:55AM	Yikai Wang	Hanes Hall-Rm 0107	6/25	Seats filled	6/25	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.
10502	STOR 120 - 411 Foundations of Statistics and Data Science	Fr 10:10AM - 11:10AM	Yikai Wang	Hanes Hall-Rm 0107	19/25	Seats filled	19/25	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.
1631	STOR 151 - 001 Introduction to Data Analysis	TuTh 5:00PM - 6:15PM	Eva Loeser	Hanes Hall-Rm 0120	Seats filled	Seats filled	95/95	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.
1632	STOR 155 - 001 Introduction to Data Models and Inference	TuTh 2:00PM - 3:15PM	CHUANSHU JI	Hanes Hall-Rm 0120	89/90	Seats filled	89/90	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.
1634	STOR 155 - 003 Introduction to Data Models and Inference	MoWeFr 2:30PM - 3:20PM	Adrian Allen	Hanes Hall-Rm 0120	Seats filled	Seats filled	100/100	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.
1635	STOR 155 - 004 Introduction to Data Models and Inference	TuTh 8:00AM - 9:15AM	Andrew Ackerman	Hanes Hall-Rm 0120	Seats filled	Seats filled	100/100	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.
1636	STOR 155 - 005 Introduction to Data Models and Inference	MoWeFr 4:40PM - 5:30PM	Emma Mitchell	Gardner Hall-Rm 0105	Seats filled	Seats filled	100/100	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.
4999	STOR 155 - 006 Introduction to Data Models and Inference	MoWeFr 8:00AM - 8:50AM	Panagiotis Andreou	Gardner Hall-Rm 0105	Seats filled	Seats filled	90/90	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.
11350	STOR 155 - 007 Introduction to Data Models and Inference	TuTh 8:00AM - 9:15AM	Fuwei Yu	Hanes Hall-Rm 0125	Seats filled	Seats filled	40/40	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.
1638	STOR 155 - 008 Introduction to Data Models and Inference	MoWeFr 11:15AM - 12:05PM	Tianzhu Liu	Hanes Hall-Rm 0125	Seats filled	Seats filled	40/40	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.
11349	STOR 155 - 009 Introduction to Data Models and Inference	MoWeFr 12:20PM - 1:10PM	William Mccance	Hanes Hall-Rm 0125	Seats filled	Seats filled	40/40	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.
11348	STOR 155 - 010 Introduction to Data Models and Inference	MoWeFr 8:00AM - 8:50AM	Can Er	Hanes Hall-Rm 0130	Seats filled	Seats filled	30/30	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.
17526	STOR 155 - 011 Introduction to Data Models and Inference	MoWeFr 10:10AM - 11:00AM	WILLIAM LASSITER	Howell Hall-Rm 0115	Seats filled	Seats filled	80/80	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.
11351	STOR 235 - 001 Mathematics for Data Science	TuTh 5:00PM - 6:15PM	Patrick Lopatto	Gardner Hall-Rm 0105	Seats filled	Seats filled	40/40	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.
11360	STOR 235 - 600 Mathematics for Data Science	Mo 2:30PM - 3:20PM	Adam Gokcan	Hanes Hall-Rm 0107	Seats filled	Seats filled	15/15	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.
11361	STOR 235 - 601 Mathematics for Data Science	We 1:25PM - 2:15PM	Lewis Dubrowski	Hanes Hall-Rm 0107	13/15	Seats filled	13/15	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.
11362	STOR 235 - 602 Mathematics for Data Science	We 5:45PM - 6:35PM	Adam Gokcan	Hanes Hall-Rm 0107	14/15	Seats filled	14/15	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.
11363	STOR 235 - 603 Mathematics for Data Science	Th 8:00AM - 8:50AM	Lewis Dubrowski	Hanes Hall-Rm 0107	2/15	Seats filled	2/15	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.
5277	STOR 305 - 001 Introduction to Decision Analytics	TuTh 3:30PM - 4:45PM	Guanting Chen	Gardner Hall-Rm 0105	Seats filled	Seats filled	70/70	0/999
Description: Prerequisite, STOR 120, 155, or MATH 152. Students cannot enroll in STOR 305 if they have already taken STOR 415. 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. 3 units.
11352	STOR 315 - 001 Discrete Mathematics for Data Science	MoWeFr 10:10AM - 11:00AM	Benjamin Seeger	Hanes Hall-Rm 0120	Seats filled	Seats filled	40/40	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.
13950	STOR 315 - 600 Discrete Mathematics for Data Science	Mo 12:20PM - 1:10PM	Brian White	Hanes Hall-Rm 0107	22/34	Seats filled	22/34	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.
13951	STOR 315 - 602 Discrete Mathematics for Data Science	We 2:30PM - 3:20PM	Brian White	Hanes Hall-Rm 0107	21/34	Seats filled	21/34	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.
5008	STOR 320 - 001 Introduction to Data Science	MoWeFr 12:20PM - 1:10PM	Mario Giacomazzo	Gardner Hall-Rm 0105	Seats filled	Seats filled	90/90	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.
5276	STOR 320 - 002 Introduction to Data Science	MoWeFr 1:25PM - 2:15PM	Mario Giacomazzo	Gardner Hall-Rm 0105	Seats filled	Seats filled	80/80	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.
13776	STOR 320 - 003 Introduction to Data Science	MoWeFr 2:30PM - 3:20PM	Kendall Thomas	Gardner Hall-Rm 0105	Seats filled	Seats filled	90/90	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.
8612	STOR 320 - 400 Introduction to Data Science	Mo 10:10AM - 11:00AM	Sumit Kumar Kar	Hanes Hall-Rm 0107	Seats filled	Seats filled	30/30	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.
8611	STOR 320 - 401 Introduction to Data Science	Fr 2:30PM - 3:20PM	Sumit Kumar Kar	Hanes Hall-Rm 0107	22/30	Seats filled	22/30	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.
9387	STOR 320 - 402 Introduction to Data Science	Tu 2:00PM - 2:50PM	Sumit Kumar Kar	New East-Rm 0301	17/18	Seats filled	17/18	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.
8610	STOR 320 - 403 Introduction to Data Science	We 11:00AM - 11:50AM	Sumit Kumar Kar	Hanes Hall-Rm 0107	24/30	Seats filled	24/30	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.
8615	STOR 320 - 404 Introduction to Data Science	We 4:40PM - 5:30PM	Coleman Ferrell	Hanes Hall-Rm 0107	26/30	Seats filled	26/30	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.
8614	STOR 320 - 405 Introduction to Data Science	Th 2:00PM - 2:50PM	Coleman Ferrell	Dey Hall-Rm 0306	23/25	Seats filled	23/25	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.
8613	STOR 320 - 406 Introduction to Data Science	Fr 8:00AM - 8:50AM	Coleman Ferrell	Hanes Hall-Rm 0107	14/30	Seats filled	14/30	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.
9388	STOR 320 - 407 Introduction to Data Science	Mo 4:40PM - 5:30PM	Anna Myakushina	Hanes Hall-Rm 0107	23/30	Seats filled	23/30	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.
13946	STOR 320 - 408 Introduction to Data Science	Tu 6:00PM - 6:50PM	Anna Myakushina	Hanes Hall-Rm 0107	10/30	Seats filled	10/30	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.
13947	STOR 320 - 409 Introduction to Data Science	We 3:35PM - 4:25PM	Coleman Ferrell	Hanes Hall-Rm 0107	Seats filled	Seats filled	34/34	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.
13948	STOR 320 - 410 Introduction to Data Science	Fr 1:25PM - 2:15PM	Anna Myakushina	Hanes Hall-Rm 0107	27/30	Seats filled	27/30	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.
13949	STOR 320 - 411 Introduction to Data Science	Mo 11:00AM - 11:50AM	Anna Myakushina	Hanes Hall-Rm 0107	26/30	Seats filled	26/30	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.
1637	STOR 415 - 001 Introduction to Optimization	TuTh 11:00AM - 12:15PM	GABOR PATAKI	Gardner Hall-Rm 0105	Seats filled	Seats filled	90/90	0/999
Description: Prerequisites, MATH 347 and STOR 315, 215 or MATH 381. Linear, integer, nonlinear, and dynamic programming, classical optimization problems, network theory. 3 units.
17523	STOR 415 - 002 Introduction to Optimization	MoWeFr 12:20PM - 1:10PM	WILLIAM LASSITER	Hanes Hall-Rm 0130	Seats filled	Seats filled	40/40	0/999
Description: Prerequisites, MATH 347 and STOR 315, 215 or MATH 381. Linear, integer, nonlinear, and dynamic programming, classical optimization problems, network theory. 3 units.
1644	STOR 435 - 001 Introduction to Probability	TuTh 3:30PM - 4:45PM	CHUANSHU JI	Hanes Hall-Rm 0120	32/85	Seats filled	35/88	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.
1645	STOR 435 - 002 Introduction to Probability	TuTh 2:00PM - 3:15PM	Sayan Banerjee	Gardner Hall-Rm 0105	85/86	Seats filled	88/89	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.
10503	STOR 435 - 003 Introduction to Probability	TuTh 12:30PM - 1:45PM	Xiangying Huang	Hanes Hall-Rm 0120	Seats filled	Seats filled	90/90	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.
4256	STOR 445 - 001 Stochastic Modeling	TuTh 9:30AM - 10:45AM	VIDYADHAR KULKARNI	Hanes Hall-Rm 0120	Seats filled	Seats filled	100/100	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.
4257	STOR 455 - 001 Methods of Data Analysis	MoWeFr 8:00AM - 8:50AM	Oluremi Abayomi	Hanes Hall-Rm 0120	Seats filled	Seats filled	65/65	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.
5000	STOR 455 - 002 Methods of Data Analysis	MoWeFr 9:05AM - 9:55AM	Souvik Ray	Hanes Hall-Rm 0120	Seats filled	Seats filled	100/100	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.
11353	STOR 471 - 001 Long-Term Actuarial Models	MoWeFr 9:05AM - 9:55AM	Oluremi Abayomi	Hanes Hall-Rm 0125	Seats filled	24/25	29/30	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.
15314	STOR 493 - 011 Internship in Statistics and Operations Research	Mo 8:00AM - 8:50AM	ANDREW NOBEL	Hanes Hall-Rm 0B56	7/15	Seats filled	7/15	0/999
Description: 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. 3 units.
12650	STOR 512 - 001 Optimization for Machine Learning and Neural Networks	TuTh 12:30PM - 1:45PM	Michael O'Neill	Fetzer Hall-Rm 0106	Seats filled	Seats filled	50/50	0/999
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.
13937	STOR 535 - 001 Probability for Data Science	TuTh 2:00PM - 3:15PM	Mo Liu	Hanes Hall-Rm 0125	Seats filled	Seats filled	45/45	0/999
Description: Prerequisites, MATH/STOR 235 or MATH 233; and STOR 215 or STOR 315 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.
10504	STOR 538 - 001 Sports Analytics	MoWeFr 9:05AM - 9:55AM	Mario Giacomazzo	Gardner Hall-Rm 0105	Seats filled	Seats filled	80/80	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.
8609	STOR 556 - 001 Time Series Data Analysis	TuTh 12:30PM - 1:45PM	KAI ZHANG	Gardner Hall-Rm 0105	Seats filled	Seats filled	70/70	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.
4743	STOR 565 - 001 Machine Learning	TuTh 9:30AM - 10:45AM	Yao Li	Gardner Hall-Rm 0105	Seats filled	Seats filled	90/90	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.
9427	STOR 572 - 001 Simulation for Analytics	MoWeFr 8:00AM - 8:50AM	WILLIAM LASSITER	Hanes Hall-Rm 0125	Seats filled	Seats filled	20/20	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.
13938	STOR 590 - 001 Special Topics in Statistics and Operations Research	TuTh 11:00AM - 12:15PM	Mariana Olvera-Cravioto	Hanes Hall-Rm 0125	Seats filled	Seats filled	12/12	0/999
Description: Examines selected topics from statistics and operations research. Course description is available from the department office. 3 units.
1639	STOR 614 - 001 Advanced Optimization	TuTh 2:00PM - 3:15PM	Ali Mohammad Nezhad	Hanes Hall-Rm 0130	1/9	8/25	9/34	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.
1640	STOR 635 - 001 Probability II	TuTh 9:30AM - 10:45AM	Nicolas Fraiman	Hanes Hall-Rm 0130	6/9	17/25	23/34	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.
1641	STOR 642 - 001 Stochastic Modeling II	TuTh 12:30PM - 1:45PM	SERHAN ZIYA	Hanes Hall-Rm 0107	5/10	10/24	15/34	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.
1642	STOR 655 - 001 Statistical Theory II	TuTh 11:00AM - 12:15PM	Jan Hannig	Hanes Hall-Rm 0130	0/4	21/30	21/34	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.
1643	STOR 665 - 001 Applied Statistics II	TuTh 3:30PM - 4:45PM	KAI ZHANG	Hanes Hall-Rm 0130	3/10	12/30	15/40	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.
13939	STOR 672 - 001 Simulation Modeling and Analysis	MoWe 9:05AM - 10:20AM	Nilay Argon	Hanes Hall-Rm 0130	0/15	9/25	9/40	0/999
Description: Prerequisites, STOR 555 and 641. Introduces students to modeling, programming, and statistical analysis applicable to computer simulations. Emphasizes statistical analysis of simulation output for decision-making. Focuses on discrete-event simulations and discusses other simulation methodologies such as Monte Carlo and agent-based simulations. Students model, program, and run simulations using specialized software. Familiarity with computer programming recommended. 3 units.
15316	STOR 691H - 007 Honors in Statistics and Analytics	Mo 8:00AM - 8:50AM	RICHARD SMITH	Hanes Hall-Rm 0303	1/2	Seats filled	1/2	0/999
Description: Permission of the department. Majors only. Individual reading, study, or project supervised by a faculty member. 3 units.
3216	STOR 701 - 001 Statistics and Operations Research Colloquium	Mo 3:30PM - 5:00PM	To be Announced	Hanes Hall-Rm 0120	28/100	Seats filled	28/100	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.
9161	STOR 702 - 001 Seminar in Teaching	Mo 7:00PM - 8:00PM	Oluremi Abayomi, WILLIAM LASSITER	Hanes Hall-Rm 0101	15/20	Seats filled	15/20	0/999
Description: This seminar course is intended to give Ph.D. students exposure to various issues and pedagogy in teaching statistics and operations research. The course also provides a forum for students to observe and learn from current teaching faculty. Students should register for one credit only. STOR Ph.D. students only. 1 units.
3943	STOR 765 - 001 Statistical Consulting	TuTh 3:30PM - 4:45PM	Zhengwu Zhang	Hanes Hall-Rm 0107	7/25	Seats filled	7/25	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.
13940	STOR 831 - 001 Advanced Probability	TuTh 2:00PM - 3:15PM	Xiangying Huang	Hanes Hall-Rm 0107	18/34	Seats filled	18/34	0/999
Description: Prerequisites, STOR 634 and 635. Advanced theoretic course, covering topics selected from weak convergence theory, central limit theorems, laws of large numbers, stable laws, infinitely divisible laws, random walks, martingales. 3 units.
16342	STOR 834 - 001 Extreme Value Theory	TuTh 12:30PM - 1:45PM	RICHARD SMITH	Greenlaw Hall-Rm 0305	10/36	Seats filled	10/36	0/999
Description: Prerequisites, STOR 635 and 654. This course covers both mathematical theory and statistical methodology concerned with extreme values in sequences of random variables. IID theory: the three types of extreme value distributions, statistical methods by block maxima and threshold exceedances. Extensions to dependent stochastic sequences: the extremal index and related concepts. Multivariate and spatial extremes, max-stable process. Applications in: engineering and strength of materials; finance and insurance; environment and climate. 3 units.
11354	STOR 891 - 001 Special Problems	Fr 3:00PM - 4:00PM	Zhengwu Zhang	Hanes Hall-Rm 0130	8/40	Seats filled	8/40	0/999
Description: Permission of the instructor. 1 - 3 units.
11054	STOR 893 - 001 Special Topics	MoWe 1:25PM - 2:40PM	ANDREW NOBEL	Hanes Hall-Rm 0130	13/30	Seats filled	13/30	0/999
Description: Advance topics in current research in statistics and operations research. 1 - 3 units.