STOR Courses

Here is information about STOR class enrollment for fall 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: 2025-04-29 12:57:54.127140

Class Number	Class	Meeting Time	Instructor	Room	Unreserved Enrollment	Reserved Enrollment	Total Enrollment	Wait List
3715	STOR 113 - 001 Decision Models for Business and Economics	TuTh 5:00PM - 6:15PM	To be Announced	Gardner Hall-Rm 0105	37/40	1/10	38/50	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.
5971	STOR 113 - 002 Decision Models for Business and Economics	MoWeFr 8:00AM - 8:50AM	To be Announced	Hanes Hall-Rm 0120	21/30	0/10	21/40	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.
12350	STOR 115 - 001 Reasoning with Data: Navigating a Quantitative World	MoWeFr 2:30PM - 3:20PM	Mark McCombs	Phillips Hall-Rm 0215	10/52	Seats filled	10/52	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.
8859	STOR 120 - 001 Foundations of Statistics and Data Science	MoWeFr 12:20PM - 1:10PM	Jeff McLean	Hanes Hall-Rm 0120	Seats filled	39/60	39/60	15/999
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.
9230	STOR 120 - 002 Foundations of Statistics and Data Science	TuTh 8:00AM - 9:15AM	VLADAS PIPIRAS	Hanes Hall-Rm 0120	18/20	10/40	28/60	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.
9568	STOR 120 - 003 Foundations of Statistics and Data Science	TuTh 9:30AM - 10:45AM	VLADAS PIPIRAS	Hanes Hall-Rm 0120	Seats filled	18/40	38/60	2/999
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.
8863	STOR 120 - 400 Foundations of Statistics and Data Science	Mo 8:00AM - 8:50AM	To be Announced	Hanes Hall-Rm 0107	Seats filled	Seats filled	10/10	1/999
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.
9906	STOR 120 - 401 Foundations of Statistics and Data Science	We 8:00AM - 8:50AM	To be Announced	Hanes Hall-Rm 0107	Seats filled	Seats filled	10/10	2/999
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.
8860	STOR 120 - 402 Foundations of Statistics and Data Science	Mo 9:05AM - 9:55AM	To be Announced	Hanes Hall-Rm 0107	Seats filled	Seats filled	10/10	5/999
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.
8861	STOR 120 - 403 Foundations of Statistics and Data Science	We 9:05AM - 9:55AM	To be Announced	Hanes Hall-Rm 0107	Seats filled	Seats filled	10/10	7/999
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.
9231	STOR 120 - 404 Foundations of Statistics and Data Science	Mo 12:20PM - 1:10PM	To be Announced	Hanes Hall-Rm 0107	Seats filled	Seats filled	10/10	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.
9907	STOR 120 - 405 Foundations of Statistics and Data Science	We 12:20PM - 1:10PM	To be Announced	Hanes Hall-Rm 0107	8/24	Seats filled	8/24	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.
9232	STOR 120 - 406 Foundations of Statistics and Data Science	Mo 1:25PM - 2:15PM	To be Announced	Hanes Hall-Rm 0107	8/10	Seats filled	8/10	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.
9233	STOR 120 - 407 Foundations of Statistics and Data Science	We 1:25PM - 2:15PM	To be Announced	Hanes Hall-Rm 0107	2/20	Seats filled	2/20	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.
9569	STOR 120 - 408 Foundations of Statistics and Data Science	Tu 8:00AM - 8:50AM	To be Announced	Hanes Hall-Rm 0107	9/10	Seats filled	9/10	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.
11815	STOR 120 - 409 Foundations of Statistics and Data Science	Th 8:00AM - 8:50AM	To be Announced	Hanes Hall-Rm 0107	Seats filled	Seats filled	10/10	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.
11816	STOR 120 - 410 Foundations of Statistics and Data Science	Tu 5:00PM - 5:50PM	To be Announced	Dey Hall-Rm 0304	9/10	Seats filled	9/10	1/999
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.
11817	STOR 120 - 411 Foundations of Statistics and Data Science	Th 11:00AM - 11:50AM	To be Announced	Hanes Hall-Rm 0107	Seats filled	Seats filled	10/10	1/999
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.
3717	STOR 155 - 001 Introduction to Data Models and Inference	TuTh 11:00AM - 12:15PM	Sayan Banerjee	Hanes Hall-Rm 0120	Seats filled	4/5	69/70	47/999
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.
3718	STOR 155 - 002 Introduction to Data Models and Inference	TuTh 3:30PM - 4:45PM	Mario Giacomazzo	Hanes Hall-Rm 0120	64/65	1/5	65/70	2/999
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.
3719	STOR 155 - 003 Introduction to Data Models and Inference	TuTh 5:00PM - 6:15PM	Mario Giacomazzo	Hanes Hall-Rm 0120	39/60	1/5	40/65	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.
9236	STOR 155 - 004 Introduction to Data Models and Inference	MoWeFr 10:10AM - 11:00AM	To be Announced	Gardner Hall-Rm 0105	Seats filled	0/5	60/65	17/999
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.
3720	STOR 155 - 005 Introduction to Data Models and Inference	MoWeFr 11:15AM - 12:05PM	To be Announced	Gardner Hall-Rm 0105	Seats filled	0/5	60/65	18/999
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.
7637	STOR 155 - 006 Introduction to Data Models and Inference	MoWeFr 9:05AM - 9:55AM	To be Announced	Gardner Hall-Rm 0105	Seats filled	0/5	60/65	11/999
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.
9235	STOR 155 - 007 Introduction to Data Models and Inference	MoWeFr 8:00AM - 8:50AM	To be Announced	Gardner Hall-Rm 0105	12/60	0/5	12/65	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.
10733	STOR 155 - 008 Introduction to Data Models and Inference	MoWeFr 5:45PM - 6:35PM	To be Announced	Hanes Hall-Rm 0120	6/60	1/5	7/65	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.
9234	STOR 155 - 009 Introduction to Data Models and Inference	TuTh 8:00AM - 9:15AM	To be Announced	Gardner Hall-Rm 0105	37/60	0/5	37/65	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.
7334	STOR 155 - 010 Introduction to Data Models and Inference	TuTh 3:30PM - 4:45PM	To be Announced	Gardner Hall-Rm 0105	59/60	1/5	60/65	1/999
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.
11096	STOR 235 - 001 Mathematics for Data Science	MoWeFr 9:05AM - 9:55AM	Katie Newhall	Howell Hall-Rm 0115	14/60	Seats filled	14/60	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.
11097	STOR 235 - 600 Mathematics for Data Science	Th 8:00AM - 8:50AM	Leyi Zhang	Phillips Hall-Rm 0228	0/15	Seats filled	0/15	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.
11098	STOR 235 - 601 Mathematics for Data Science	Th 9:30AM - 10:20AM	Nerion Zekaj	Phillips Hall-Rm 0228	4/15	Seats filled	4/15	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.
11099	STOR 235 - 602 Mathematics for Data Science	Th 12:30PM - 1:20PM	Austin Blitstein	Phillips Hall-Rm 0228	4/15	Seats filled	4/15	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.
11100	STOR 235 - 603 Mathematics for Data Science	Th 2:00PM - 2:50PM	Frane Sazunic Ljubetic	Phillips Hall-Rm 0228	6/15	Seats filled	6/15	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.
15406	STOR 290 - 001 Special Topics	MoWe 4:40PM - 5:55PM	Mario Giacomazzo	Gardner Hall-Rm 0307	0/40	Seats filled	0/40	0/999
Description: Examines selected topics from statistics and operations research. Course description is available from the department office. 3 units.
11414	STOR 291 - 001 Undergraduate Learning Assistantship	Mo 9:00AM - 10:00AM	Jeff McLean	Stone Center-Rm 0200	1/5	Seats filled	1/5	0/999
Description: Prerequisite, STOR 120 grade of A- or higher. Experience includes preparations, demonstrations, assistance, and attendance at weekly meetings and lab sections. This course will enable you to deepen your understanding of topics in statistics and data science by learning the material with an eye toward explaining it to other less-experienced students, and; develop pedagogical skills, such as developing a rapport with learners, engaging in clear oral and written communication, and taking the perspective of less experienced students. 1 units.
3722	STOR 305 - 001 Introduction to Decision Analytics	MoWeFr 12:20PM - 1:10PM	WILLIAM LASSITER	Gardner Hall-Rm 0105	Seats filled	2/15	67/80	26/999
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.
11112	STOR 315 - 001 Discrete Mathematics for Data Science	MoWeFr 2:30PM - 3:20PM	Mariana Olvera-Cravioto	Gardner Hall-Rm 0105	2/15	5/85	7/100	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.
11124	STOR 315 - 600 Discrete Mathematics for Data Science	Tu 8:00AM - 8:50AM	To be Announced	Hanes Hall-Rm 0130	6/25	Seats filled	6/25	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.
11819	STOR 315 - 601 Discrete Mathematics for Data Science	Th 8:00AM - 8:50AM	To be Announced	Hanes Hall-Rm 0130	1/25	Seats filled	1/25	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.
7683	STOR 320 - 001 Methods and Models of Data Science	MoWeFr 10:10AM - 11:00AM	Mo Liu	Hanes Hall-Rm 0120	Seats filled	60/70	90/100	50/999
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.
8103	STOR 320 - 002 Methods and Models of Data Science	MoWeFr 1:25PM - 2:15PM	Mo Liu	Hanes Hall-Rm 0120	Seats filled	62/70	92/100	45/999
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.
13859	STOR 320 - 003 Methods and Models of Data Science	MoWeFr 5:45PM - 6:35PM	To be Announced	Gardner Hall-Rm 0105	14/30	56/70	70/100	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.
8802	STOR 320 - 400 Methods and Models of Data Science	Mo 2:30PM - 3:20PM	To be Announced	Hanes Hall-Rm 0107	Seats filled	Seats filled	30/30	6/999
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.
8803	STOR 320 - 401 Methods and Models of Data Science	We 2:30PM - 3:20PM	To be Announced	Hanes Hall-Rm 0107	Seats filled	Seats filled	30/30	15/999
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.
8804	STOR 320 - 402 Methods and Models of Data Science	Mo 5:45PM - 6:35PM	To be Announced	Hanes Hall-Rm 0107	19/30	Seats filled	19/30	22/999
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.
9908	STOR 320 - 403 Methods and Models of Data Science	We 5:45PM - 6:35PM	To be Announced	Hanes Hall-Rm 0107	11/30	Seats filled	11/30	7/999
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.
9261	STOR 320 - 404 Methods and Models of Data Science	Tu 12:30PM - 1:20PM	To be Announced	Hanes Hall-Rm 0107	29/30	Seats filled	29/30	10/999
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.
9909	STOR 320 - 405 Methods and Models of Data Science	Th 12:30PM - 1:20PM	To be Announced	Hanes Hall-Rm 0107	22/30	Seats filled	22/30	9/999
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.
8805	STOR 320 - 406 Methods and Models of Data Science	Tu 2:00PM - 2:50PM	To be Announced	Hanes Hall-Rm 0107	29/30	Seats filled	29/30	16/999
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.
9159	STOR 320 - 407 Methods and Models of Data Science	Th 2:00PM - 2:50PM	To be Announced	Hanes Hall-Rm 0107	12/30	Seats filled	12/30	10/999
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.
13863	STOR 320 - 408 Methods and Models of Data Science	Tu 5:00PM - 5:50PM	To be Announced	Hanes Hall-Rm 0107	21/30	Seats filled	21/30	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.
13862	STOR 320 - 409 Methods and Models of Data Science	Th 5:00PM - 5:50PM	To be Announced	Hanes Hall-Rm 0107	9/30	Seats filled	9/30	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.
13861	STOR 320 - 410 Methods and Models of Data Science	Tu 3:30PM - 4:20PM	To be Announced	Davie Hall-Rm 0101	19/25	Seats filled	19/25	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.
13860	STOR 320 - 411 Methods and Models of Data Science	Th 3:30PM - 4:20PM	To be Announced	Dey Hall-Rm 0204	21/25	Seats filled	21/25	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.
15183	STOR 323 - 001 Ethics of Machine Learning in Society	MoWeFr 12:20PM - 1:10PM	To be Announced	Hanes Hall-Rm 0130	Seats filled	3/46	3/46	1/999
Description: Prerequisite, STOR 120 or STOR 320. Modern Statistics and Machine Learning introduces several normative issues. Concerns surrounding algorithmic bias, data privacy, and methodological transparency arise from our methods, but our methods are not capable of fully ameliorating them. A more complete answer must be sought in moral philosophy. This course is positioned at the intersection of these disciplines. It will combine rigorous analysis of modern data sets alongside open-ended philosophical discussion. Students leaving this class will be able to defend when their methods are not only technically sound but morally just. 3 units.
12761	STOR 390 - 001 Special Topics in Statistics and Operations Research	MoWeFr 8:00AM - 8:50AM	Oluremi Abayomi	Hanes Hall-Rm 0125	18/45	Seats filled	18/45	0/999
Description: Examines selected topics from statistics and operations research. Course description is available from the department office. 3 units.
7973	STOR 415 - 001 Introduction to Optimization	TuTh 9:30AM - 10:45AM	Quoc Tran-Dinh	Gardner Hall-Rm 0105	Seats filled	45/56	89/100	35/999
Description: Prerequisites, MATH 347 and STOR 315, 215, COMP 283 or MATH 381. Linear, integer, nonlinear, and dynamic programming, classical optimization problems, network theory. 3 units.
13864	STOR 415H - 012 Introduction to Optimization	MoWeFr 10:10AM - 11:00AM	GABOR PATAKI	Hanes Hall-Rm 0107	Seats filled	7/18	7/18	9/999
Description: Prerequisites, MATH 347 and STOR 315, 215, COMP 283 or MATH 381. Linear, integer, nonlinear, and dynamic programming, classical optimization problems, network theory. 3 units.
3723	STOR 435 - 001 Introduction to Probability	MoWeFr 9:05AM - 9:55AM	Shankar Bhamidi	Hanes Hall-Rm 0120	Seats filled	Seats filled	80/80	50/999
Description: Prerequisites, MATH/STOR 235 or MATH 233; and STOR 215 or STOR 315 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.
8293	STOR 435 - 002 Introduction to Probability	TuTh 2:00PM - 3:15PM	Sayan Banerjee	Hanes Hall-Rm 0120	Seats filled	Seats filled	80/80	24/999
Description: Prerequisites, MATH/STOR 235 or MATH 233; and STOR 215 or STOR 315 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.
9911	STOR 435 - 003 Introduction to Probability	TuTh 11:00AM - 12:15PM	Mariana Olvera-Cravioto	Gardner Hall-Rm 0105	Seats filled	26/30	66/70	5/999
Description: Prerequisites, MATH/STOR 235 or MATH 233; and STOR 215 or STOR 315 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.
13865	STOR 435H - 005 Introduction to Probability	MoWeFr 11:15AM - 12:05PM	Shankar Bhamidi	Hanes Hall-Rm 0107	Seats filled	Seats filled	18/18	13/999
Description: Prerequisites, MATH/STOR 235 or MATH 233; and STOR 215 or STOR 315 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.
3724	STOR 445 - 001 Stochastic Modeling	MoWeFr 11:15AM - 12:05PM	Guanting Chen	Hanes Hall-Rm 0120	27/28	Seats filled	99/100	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.
9912	STOR 445 - 002 Stochastic Modeling	MoWeFr 2:30PM - 3:20PM	Nilay Argon	Hanes Hall-Rm 0120	60/76	Seats filled	84/100	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.
3725	STOR 455 - 001 Methods of Data Analysis	TuTh 12:30PM - 1:45PM	CHUANSHU JI	Hanes Hall-Rm 0120	12/30	39/60	51/90	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.
7972	STOR 455 - 002 Methods of Data Analysis	MoWeFr 1:25PM - 2:15PM	To be Announced	Gardner Hall-Rm 0105	Seats filled	Seats filled	90/90	26/999
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.
11821	STOR 472 - 001 Short Term Actuarial Models	MoWeFr 9:05AM - 9:55AM	Oluremi Abayomi	Hanes Hall-Rm 0125	2/21	11/19	13/40	0/999
Description: Prerequisite, STOR 435, or 535. Short term probability models for potential losses and their applications to both traditional insurance systems and conventional business decisions. Introduction to stochastic process models of solvency requirements. 3 units.
8862	STOR 475 - 001 Healthcare Risk Analytics	Tu 3:30PM - 6:30PM	Frederick Kelly	Hanes Hall-Rm 0125	Seats filled	24/27	27/30	0/999
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.
8260	STOR 496 - 013 Undergraduate Reading and Research in Statistics and Operations Research	Mo 2:00PM - 3:00PM	J. S. (Steve) Marron	New East-Rm 0305	0/2	Seats filled	0/2
Description: Permission of the director of undergraduate studies. This course is intended mainly for students working on honors projects. May be repeated for credit. 1 - 3 units.
13872	STOR 535 - 001 Probability for Data Science	MoWeFr 11:15AM - 12:05PM	Nicolas Fraiman	Hanes Hall-Rm 0130	Seats filled	Seats filled	25/25	24/999
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.
13873	STOR 538 - 001 Sports Analytics	TuTh 12:30PM - 1:45PM	Mario Giacomazzo	Gardner Hall-Rm 0105	Seats filled	38/40	78/80	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.
15198	STOR 543 - 001 Dynamic Decision Analytics	MoWeFr 10:10AM - 11:00AM	WILLIAM LASSITER	Hanes Hall-Rm 0125	Seats filled	19/20	26/27	1/999
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. Previously offered as STOR 515. 3 units.
3726	STOR 555 - 001 Mathematical Statistics	TuTh 12:30PM - 1:45PM	ANDREW NOBEL	Hanes Hall-Rm 0125	Seats filled	13/14	19/20	4/999
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.
9263	STOR 557 - 001 Advanced Methods of Data Analysis	TuTh 11:00AM - 12:15PM	RICHARD SMITH	Hanes Hall-Rm 0125	Seats filled	Seats filled	20/20	6/999
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.
8294	STOR 565 - 001 Machine Learning	TuTh 2:00PM - 3:15PM	Chudi Zhong	Gardner Hall-Rm 0105	Seats filled	64/70	74/80	4/999
Description: Prerequisites, STOR 215 or 315 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.
11822	STOR 566 - 001 Introduction to Deep Learning	TuTh 8:00AM - 9:15AM	Yao Li	Hanes Hall-Rm 0125	Seats filled	Seats filled	20/20	12/999
Description: Prerequisites, STOR 415 or 512; and COMP 110 or 116. Deep neural networks (DNNs) have been widely used for tackling numerous machine learning problems that were once believed to be challenging. With their remarkable ability of fitting training data, DNNs have achieved revolutionary successes in many fields such as computer vision, natural language progressing, and robotics. This is an introduction course to deep learning. 3 units.
3727	STOR 612 - 001 Foundations of Optimization	MoWe 1:25PM - 2:40PM	Michael O'Neill	Hanes Hall-Rm 0125	Seats filled	Seats filled	20/20	6/999
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.
3768	STOR 634 - 001 Probability I	TuTh 9:30AM - 10:45AM	Benjamin Seeger	Hanes Hall-Rm 0130	6/10	9/10	15/20	0/999
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.
3728	STOR 641 - 001 Stochastic Modeling I	TuTh 2:00PM - 3:15PM	SERHAN ZIYA	Hanes Hall-Rm 0130	Seats filled	Seats filled	20/20	2/999
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.
3769	STOR 654 - 001 Statistical Theory I	TuTh 3:30PM - 4:45PM	Patrick Lopatto	Hanes Hall-Rm 0130	9/20	Seats filled	9/20	0/999
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.
3770	STOR 664 - 001 Applied Statistics I	MoWe 11:15AM - 12:30PM	Dan Kessler	Hanes Hall-Rm 0125	17/20	Seats filled	17/20	0/999
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.
11823	STOR 674 - 001 Statistical and Computational Tools for Reproducible Data Science	TuTh 12:30PM - 1:45PM	Zhengwu Zhang	Hanes Hall-Rm 0130	4/20	Seats filled	4/20	0/999
Description: Prerequisite, STOR 320 or 664. The purpose of this course is to provide a strong foundation in computational skills needed for reproducible research in data science and statistics. Topics will include computational tools and programming skills to facilitate reproducibility, as well as procedures and methods for reproducible conclusions. 3 units.
15397	STOR 697 - 001 Capstone	MoWe 3:30PM - 4:50PM	Glenn Sabin	Hanes Hall-Rm 0107	6/30	Seats filled	6/30	0/999
Description: Prerequisite, Students are expected to have completed at least 75% of the core requirements for the STAN major. This course is designed to give Statistics & Analytics (STAN) majors an opportunity to integrate and apply the knowledge and skills acquired throughout the STAN degree. At the beginning of the semester, the instructor will present to the class a broad description of several problems originating from external industry partners, and covering a wide range of statistics, modeling, optimization, and data science topics. Students will work on these problems throughout the remainder of the semester. 3 units.
6671	STOR 701 - 001 Statistics and Operations Research Colloquium	Mo 3:30PM - 4:45PM	Guanting Chen	Hanes Hall-Rm 0120	1/90	Seats filled	11/100	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.
11824	STOR 712 - 001 Optimization for Machine Learning and Data Science	MoWe 9:05AM - 10:20AM	Michael O'Neill	Hanes Hall-Rm 0130	6/40	Seats filled	6/40	0/999
Description: Prerequisite, STOR 612 or equivalent. This course will provide a detailed and deep treatment for commonly used methods in continuous optimization, with applications in machine learning, statistics, data science, operations research, among others. 3 units.
10785	STOR 743 - 001 Reinforcement Learning and Markov Decision Processes	MoWe 1:25PM - 2:40PM	Guanting Chen	Hanes Hall-Rm 0130	8/40	Seats filled	8/40	0/999
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.
7003	STOR 765 - 001 Statistical Consulting	TuTh 3:30PM - 4:45PM	J. S. (Steve) Marron	Hanes Hall-Rm 0107	5/34	Seats filled	5/34	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.
13882	STOR 767 - 001 Advanced Statistical Machine Learning	TuTh 2:00PM - 3:15PM	ANDREW NOBEL	Hanes Hall-Rm 0125	14/40	Seats filled	14/40	0/999
Description: Prerequisites, STOR 654, 655, 664, 665 and permission of the instructor. This is a graduate course on statistical machine learning. 3 units.
13883	STOR 881 - 001 Object Oriented Data Analysis	TuTh 11:00AM - 12:15PM	J. S. (Steve) Marron	Hanes Hall-Rm 0130	10/40	Seats filled	10/40	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.
7974	STOR 890 - 001 Special Problems	TuTh 9:30AM - 10:45AM	Amarjit Budhiraja	Hanes Hall-Rm 0107	9/34	Seats filled	9/34	0/999
Description: Permission of the instructor. 1 - 3 units.
8744	STOR 892 - 001 Special Topics in Operations Research and Systems Analysis	TuTh 9:30AM - 10:45AM	Jan Hannig	Hanes Hall-Rm 0125	8/40	Seats filled	8/40	0/999
Description: Permission of the instructor. 1 - 3 units.