STOR Courses

Here is information about STOR class enrollment for fall 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: 2024-08-19 14:22:56.940304

Class Number	Class	Meeting Time	Instructor	Room	Unreserved Enrollment	Reserved Enrollment	Total Enrollment	Wait List
3970	STOR 113 - 001 Decision Models for Business and Economics	MoWeFr 10:10AM - 11:00AM	GABOR PATAKI	Hanes Hall-Rm 0120	62/63	Seats filled	106/107	2/999
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.
6418	STOR 113 - 002 Decision Models for Business and Economics	MoWeFr 11:15AM - 12:05PM	GABOR PATAKI	Hanes Hall-Rm 0120	Seats filled	Seats filled	107/107	9/999
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.
14555	STOR 115 - 001 Reasoning with Data: Navigating a Quantitative World	MoWeFr 2:30PM - 3:20PM	Hank Flury	Greenlaw Hall-Rm 0101	50/52	Seats filled	50/52	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.
9559	STOR 120 - 001 Foundations of Statistics and Data Science	MoWeFr 8:00AM - 8:50AM	Oluremi Abayomi	Hanes Hall-Rm 0120	64/74	Seats filled	97/107	6/999
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.
9972	STOR 120 - 002 Foundations of Statistics and Data Science	MoWeFr 9:05AM - 9:55AM	Oluremi Abayomi	Hanes Hall-Rm 0120	Seats filled	Seats filled	107/107	22/999
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.
13661	STOR 120 - 004 Foundations of Statistics and Data Science	MoWeFr 12:20PM - 1:10PM	Jeff McLean	Hanes Hall-Rm 0120	Seats filled	Seats filled	107/107	17/999
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.
10359	STOR 120 - 03F Foundations of Statistics and Data Science	MoWeFr 11:15AM - 12:05PM	Nicolas Fraiman	Hanes Hall-Rm 0107	Seats filled	Seats filled	34/34
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.
9564	STOR 120 - 400 Foundations of Statistics and Data Science	Tu 9:30AM - 10:20AM	Aidan Burchard	Hanes Hall-Rm 0107	25/26	Seats filled	25/26	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.
10769	STOR 120 - 401 Foundations of Statistics and Data Science	Tu 11:00AM - 11:50AM	Aidan Burchard	Hanes Hall-Rm 0107	24/26	Seats filled	24/26	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.
9560	STOR 120 - 402 Foundations of Statistics and Data Science	Mo 2:30PM - 3:20PM	Aidan Burchard	Hanes Hall-Rm 0107	Seats filled	Seats filled	26/26	3/999
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.
9561	STOR 120 - 403 Foundations of Statistics and Data Science	Mo 5:00PM - 5:50PM	Ishmael Benjamin Torres Aguilar	Hanes Hall-Rm 0107	22/26	Seats filled	22/26	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.
9973	STOR 120 - 404 Foundations of Statistics and Data Science	We 5:00PM - 5:50PM	Ishmael Benjamin Torres Aguilar	Hanes Hall-Rm 0107	Seats filled	Seats filled	26/26	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.
10770	STOR 120 - 405 Foundations of Statistics and Data Science	We 2:30PM - 3:20PM	Ishmael Benjamin Torres Aguilar	Hanes Hall-Rm 0107	Seats filled	Seats filled	34/34	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.
9974	STOR 120 - 406 Foundations of Statistics and Data Science	Fr 1:00PM - 1:50PM	Can Er	Hanes Hall-Rm 0107	25/26	Seats filled	25/26	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.
9975	STOR 120 - 407 Foundations of Statistics and Data Science	Fr 11:00AM - 11:50AM	Can Er	Carolina Hall-Rm 0104	Seats filled	Seats filled	30/30	10/999
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.
10360	STOR 120 - 408 Foundations of Statistics and Data Science	Th 9:30AM - 10:20AM	Can Er	Hanes Hall-Rm 0107	Seats filled	Seats filled	26/26	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.
13664	STOR 120 - 409 Foundations of Statistics and Data Science	Th 5:00PM - 5:50PM	To be Announced	Hanes Hall-Rm 0107	Seats filled	Seats filled	26/26	3/999
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.
13665	STOR 120 - 410 Foundations of Statistics and Data Science	Fr 9:00AM - 9:50AM	To be Announced	Hanes Hall-Rm 0107	Seats filled	Seats filled	26/26	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.
13666	STOR 120 - 411 Foundations of Statistics and Data Science	We 2:00PM - 2:50PM	To be Announced	Fetzer Hall-Rm 0104	29/30	Seats filled	29/30	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.
13667	STOR 120 - 412 Foundations of Statistics and Data Science	Fr 1:25PM - 2:15PM	Aidan Burchard	Murphey Hall-Rm 0112	Seats filled	Seats filled	26/26	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.
3971	STOR 151 - 001 Introduction to Data Analysis	TuTh 12:30PM - 1:45PM	Eva Loeser	Hanes Hall-Rm 0120	Seats filled	Seats filled	107/107	16/999
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.
3972	STOR 155 - 001 Introduction to Data Models and Inference	TuTh 11:00AM - 12:15PM	Sayan Banerjee	Hanes Hall-Rm 0120	Seats filled	Seats filled	107/107	79/999
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.
3973	STOR 155 - 002 Introduction to Data Models and Inference	TuTh 8:00AM - 9:15AM	CHUANSHU JI	Gardner Hall-Rm 0105	76/83	Seats filled	103/110	4/999
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.
3974	STOR 155 - 003 Introduction to Data Models and Inference	MoWeFr 12:20PM - 1:10PM	WILLIAM LASSITER	Gardner Hall-Rm 0105	Seats filled	Seats filled	110/110	81/999
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.
9978	STOR 155 - 004 Introduction to Data Models and Inference	MoWeFr 4:40PM - 5:30PM	Joseph Lavond	Gardner Hall-Rm 0105	Seats filled	Seats filled	105/105	25/999
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.
3975	STOR 155 - 005 Introduction to Data Models and Inference	MoWeFr 5:45PM - 6:35PM	Peter Rudzis	Gardner Hall-Rm 0105	79/90	Seats filled	99/110	3/999
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.
8189	STOR 155 - 006 Introduction to Data Models and Inference	TuTh 8:00AM - 9:15AM	Minji Kim	Hanes Hall-Rm 0125	Seats filled	Seats filled	47/47	16/999
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.
9977	STOR 155 - 007 Introduction to Data Models and Inference	TuTh 5:00PM - 6:15PM	Dilshad Imon	Hanes Hall-Rm 0130	Seats filled	Seats filled	46/46	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.
11840	STOR 155 - 008 Introduction to Data Models and Inference	MoWeFr 8:00AM - 8:50AM	Sumit Kumar Kar	Gardner Hall-Rm 0105	43/45	Seats filled	53/55	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.
9976	STOR 155 - 009 Introduction to Data Models and Inference	MoWeFr 8:00AM - 8:50AM	Sumit Kumar Kar	Gardner Hall-Rm 0105	35/40	Seats filled	50/55	6/999
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.
3976	STOR 215 - 001 Foundations of Decision Sciences	TuTh 9:30AM - 10:45AM	VIDYADHAR KULKARNI	Gardner Hall-Rm 0105	54/70	Seats filled	67/83	0/999
Description: Prerequisite, MATH 110. Introduction to basic concepts and techniques of discrete mathematics with applications to business and social and physical sciences. Topics include logic, sets, functions, combinatorics, discrete probability, graphs, and networks. 3 units.
12570	STOR 235 - 001 Mathematics for Data Science	MoWeFr 9:05AM - 9:55AM	JINGFANG HUANG	Howell Hall-Rm 0115	31/60	Seats filled	31/60
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.
12571	STOR 235 - 600 Mathematics for Data Science	Th 8:00AM - 8:50AM	Junyan Liu	Phillips Hall-Rm 0385	2/15	Seats filled	2/15
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.
12572	STOR 235 - 601 Mathematics for Data Science	Th 9:30AM - 10:20AM	Nerion Zekaj	Phillips Hall-Rm 0228	9/15	Seats filled	9/15
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.
12573	STOR 235 - 602 Mathematics for Data Science	Th 12:30PM - 1:20PM	To be Announced	Phillips Hall-Rm 0228	13/15	Seats filled	13/15
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.
12574	STOR 235 - 603 Mathematics for Data Science	Th 2:00PM - 2:50PM	Frane Sazunic Ljubetic	Phillips Hall-Rm 0228	7/15	Seats filled	7/15
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.
3977	STOR 305 - 001 Introduction to Decision Analytics	MoWeFr 10:10AM - 11:00AM	WILLIAM LASSITER	Gardner Hall-Rm 0105	Seats filled	Seats filled	110/110	8/999
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.
9568	STOR 305 - 002 Introduction to Decision Analytics	MoWeFr 9:05AM - 9:55AM	WILLIAM LASSITER	Gardner Hall-Rm 0105	96/110	Seats filled	96/110	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.
12601	STOR 315 - 001 Discrete Mathematics for Data Science	TuTh 5:00PM - 6:15PM	Ali Mohammad Nezhad	Gardner Hall-Rm 0105	42/69	Seats filled	73/100	1/999
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.
12626	STOR 315 - 600 Discrete Mathematics for Data Science	Th 3:30PM - 4:20PM	Andrew Nguyen	Hanes Hall-Rm 0130	Seats filled	Seats filled	25/25	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.
13668	STOR 315 - 601 Discrete Mathematics for Data Science	Th 11:00AM - 11:50AM	Andrew Nguyen	Hanes Hall-Rm 0130	Seats filled	Seats filled	25/25	1/999
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.
12627	STOR 315 - 602 Discrete Mathematics for Data Science	Fr 9:00AM - 9:50AM	Kenny Zhang	Hanes Hall-Rm 0130	10/25	Seats filled	10/25	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.
13669	STOR 315 - 603 Discrete Mathematics for Data Science	Fr 1:00PM - 1:50PM	Kenny Zhang	Dey Hall-Rm 0404	13/25	Seats filled	13/25	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.
8239	STOR 320 - 001 Introduction to Data Science	TuTh 8:00AM - 9:15AM	Yao Li	Hanes Hall-Rm 0120	Seats filled	Seats filled	107/107	31/999
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.
8691	STOR 320 - 002 Introduction to Data Science	MoWeFr 5:45PM - 6:35PM	Mo Liu	Hanes Hall-Rm 0120	Seats filled	Seats filled	107/107	19/999
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.
9490	STOR 320 - 400 Introduction to Data Science	We 3:30PM - 4:20PM	Morgan Smith	Hanes Hall-Rm 0107	Seats filled	Seats filled	30/30	13/999
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.
9491	STOR 320 - 401 Introduction to Data Science	We 5:00PM - 5:50PM	Morgan Smith	Carolina Hall-Rm 0104	29/30	Seats filled	29/30	4/999
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.
9492	STOR 320 - 402 Introduction to Data Science	Fr 10:10AM - 11:00AM	Yuhao Zhou	Hanes Hall-Rm 0107	Seats filled	Seats filled	30/30	4/999
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.
10771	STOR 320 - 403 Introduction to Data Science	Fr 3:30PM - 4:20PM	Yuhao Zhou	Dey Hall-Rm 0203	18/20	Seats filled	18/20	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.
10004	STOR 320 - 404 Introduction to Data Science	Tu 2:00PM - 2:50PM	Anna Myakushina	Hanes Hall-Rm 0107	Seats filled	Seats filled	29/29	3/999
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.
10772	STOR 320 - 405 Introduction to Data Science	Tu 5:00PM - 5:50PM	Anna Myakushina	Hanes Hall-Rm 0107	Seats filled	Seats filled	29/29	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.
9493	STOR 320 - 406 Introduction to Data Science	We 9:00AM - 9:50AM	Morgan Smith	Dey Hall-Rm 0313	Seats filled	Seats filled	24/24	4/999
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.
9890	STOR 320 - 407 Introduction to Data Science	We 1:00PM - 1:50PM	Morgan Smith	Hanes Hall-Rm 0107	25/28	Seats filled	25/28	5/999
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.
15082	STOR 390 - 001 Special Topics in Statistics and Operations Research	TuTh 8:00AM - 9:15AM	Andrew Ackerman	Hanes Hall-Rm 0130	32/45	Seats filled	32/45	0/999
Description: Examines selected topics from statistics and operations research. Course description is available from the department office. 3 units.
8553	STOR 415 - 001 Introduction to Optimization	TuTh 11:00AM - 12:15PM	Quoc Tran-Dinh	Gardner Hall-Rm 0105	29/65	Seats filled	64/100	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.
10773	STOR 415 - 002 Introduction to Optimization	MoWeFr 1:25PM - 2:15PM	Michael O'Neill	Gardner Hall-Rm 0105	Seats filled	Seats filled	105/105	4/999
Description: Prerequisites, MATH 347 and STOR 315, 215 or MATH 381. Linear, integer, nonlinear, and dynamic programming, classical optimization problems, network theory. 3 units.
3978	STOR 435 - 001 Introduction to Probability	MoWeFr 11:15AM - 12:05PM	Mariana Olvera-Cravioto	Gardner Hall-Rm 0105	Seats filled	Seats filled	90/90	11/999
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.
8904	STOR 435 - 002 Introduction to Probability	MoWeFr 2:30PM - 3:20PM	Xiangying Huang	Hanes Hall-Rm 0120	19/29	Seats filled	80/90	13/999
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.
10774	STOR 435 - 003 Introduction to Probability	TuTh 5:00PM - 6:15PM	Benjamin Seeger	Hanes Hall-Rm 0120	45/55	Seats filled	75/85	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.
3979	STOR 445 - 001 Stochastic Modeling	TuTh 2:00PM - 3:15PM	SERHAN ZIYA	Hanes Hall-Rm 0120	21/23	Seats filled	98/100	2/999
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.
10775	STOR 445 - 002 Stochastic Modeling	MoWeFr 2:30PM - 3:20PM	Guanting Chen	Gardner Hall-Rm 0105	19/71	Seats filled	48/100	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.
3980	STOR 455 - 001 Methods of Data Analysis	TuTh 12:30PM - 1:45PM	Souvik Ray	Gardner Hall-Rm 0105	Seats filled	Seats filled	110/110	12/999
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.
8552	STOR 455 - 002 Methods of Data Analysis	TuTh 2:00PM - 3:15PM	Kendall Thomas	Gardner Hall-Rm 0105	Seats filled	Seats filled	105/105	8/999
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.
13670	STOR 472 - 001 Short Term Actuarial Models	MoWeFr 1:25PM - 2:15PM	Oluremi Abayomi	Venable Hall-Rm G311	7/17	Seats filled	30/40	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.
9562	STOR 475 - 001 Healthcare Risk Analytics	Tu 3:30PM - 6:30PM	Frederick Kelly	Hanes Hall-Rm 0125	6/7	Seats filled	29/30	1/999
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.
10005	STOR 520 - 001 Statistical Computing for Data Science	TuTh 9:30AM - 10:45AM	Chudi Zhong	Hanes Hall-Rm 0125	20/25	Seats filled	21/26	0/999
Description: Prerequisites, STOR 435 or 535, and STOR 455. This course provides hands-on experience working with data sets provided in class and downloaded from certain public websites. Lectures cover basic topics such as R programming, visualization, data wrangling and cleaning, exploratory data analysis, web scraping, data merging, predictive modeling, and elements of machine learning. Programming analyses in more advanced areas of data science. Students may not receive credit for both STOR 320 and STOR 520. 4 units.
11838	STOR 520 - 401 Statistical Computing for Data Science	Mo 5:45PM - 6:35PM	Coleman Ferrell	Phillips Hall-Rm 0301	10/15	Seats filled	10/15	0/999
Description: Prerequisites, STOR 435 or 535, and STOR 455. This course provides hands-on experience working with data sets provided in class and downloaded from certain public websites. Lectures cover basic topics such as R programming, visualization, data wrangling and cleaning, exploratory data analysis, web scraping, data merging, predictive modeling, and elements of machine learning. Programming analyses in more advanced areas of data science. Students may not receive credit for both STOR 320 and STOR 520. 0 units.
10778	STOR 520 - 403 Statistical Computing for Data Science	Tu 12:30PM - 1:20PM	Coleman Ferrell	Hanes Hall-Rm 0107	11/15	Seats filled	11/15	0/999
Description: Prerequisites, STOR 435 or 535, and STOR 455. This course provides hands-on experience working with data sets provided in class and downloaded from certain public websites. Lectures cover basic topics such as R programming, visualization, data wrangling and cleaning, exploratory data analysis, web scraping, data merging, predictive modeling, and elements of machine learning. Programming analyses in more advanced areas of data science. Students may not receive credit for both STOR 320 and STOR 520. 0 units.
3981	STOR 555 - 001 Mathematical Statistics	TuTh 3:30PM - 4:45PM	ANDREW NOBEL	Gardner Hall-Rm 0105	23/59	Seats filled	24/60	0/999
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.
10007	STOR 557 - 001 Advanced Methods of Data Analysis	TuTh 9:30AM - 10:45AM	RICHARD SMITH	Hanes Hall-Rm 0120	32/68	Seats filled	34/70	0/999
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.
8905	STOR 565 - 001 Machine Learning	MoWeFr 1:25PM - 2:15PM	YUFENG LIU	Hanes Hall-Rm 0120	18/22	Seats filled	76/80	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.
13671	STOR 566 - 001 Introduction to Deep Learning	TuTh 3:30PM - 4:45PM	Yao Li	Hanes Hall-Rm 0120	29/47	Seats filled	47/65	0/999
Description: Prerequisites, STOR 435 or 535; 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.
14966	STOR 590 - 001 Special Topics in Statistics and Operations Research	TuTh 2:00PM - 3:15PM	Sayan Banerjee	Hanes Hall-Rm 0125	11/30	Seats filled	11/30	0/999
Description: Examines selected topics from statistics and operations research. Course description is available from the department office. 3 units.
3982	STOR 612 - 001 Foundations of Optimization	TuTh 2:00PM - 3:15PM	Quoc Tran-Dinh	Hanes Hall-Rm 0130	Seats filled	Seats filled	20/20	8/999
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.
4027	STOR 634 - 001 Probability I	MoWe 1:25PM - 2:40PM	Shankar Bhamidi	Hanes Hall-Rm 0130	Seats filled	Seats filled	20/20	2/999
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.
3983	STOR 641 - 001 Stochastic Modeling I	MoWe 9:05AM - 10:20AM	Guanting Chen	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.
4028	STOR 654 - 001 Statistical Theory I	TuTh 9:30AM - 10:45AM	Patrick Lopatto	Hanes Hall-Rm 0130	22/23	Seats filled	22/23	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.
4029	STOR 664 - 001 Applied Statistics I	MoWe 11:15AM - 12:30PM	Daniel Kessler	Hanes Hall-Rm 0125	25/26	Seats filled	25/26	1/999
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.
13672	STOR 674 - 001 Statistical and Computational Tools for Reproducible Data Science	TuTh 11:00AM - 12:15PM	Zhengwu Zhang	Hanes Hall-Rm 0125	11/15	Seats filled	11/15	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.
7162	STOR 701 - 001 Statistics and Operations Research Colloquium	Mo 3:30PM - 4:45PM	To be Announced	Hanes Hall-Rm 0120	40/90	Seats filled	50/100	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.
13673	STOR 712 - 001 Optimization for Machine Learning and Data Science	MoWe 9:05AM - 10:20AM	Michael O'Neill	Hanes Hall-Rm 0125	18/34	Seats filled	18/34	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.
7514	STOR 765 - 001 Statistical Consulting	TuTh 3:30PM - 4:45PM	Zhengwu Zhang	Hanes Hall-Rm 0107	10/34	Seats filled	10/34	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.
8554	STOR 890 - 001 Special Problems	TuTh 12:30PM - 1:45PM	VIDYADHAR KULKARNI	Hanes Hall-Rm 0130	13/34	Seats filled	13/34	0/999
Description: Permission of the instructor. 1 - 3 units.
10012	STOR 891 - 001 Special Problems	TuTh 12:30PM - 1:45PM	KAI ZHANG	Hanes Hall-Rm 0125	10/30	Seats filled	10/30	0/999
Description: Permission of the instructor. 1 - 3 units.
9422	STOR 892 - 001 Special Topics in Operations Research and Systems Analysis	MoWe 11:15AM - 12:30PM	Shankar Bhamidi	Hanes Hall-Rm 0130	15/30	Seats filled	15/30	0/999
Description: Permission of the instructor. 1 - 3 units.
11457	STOR 893 - 001 Special Topics	TuTh 5:00PM - 6:15PM	ANDREW NOBEL	TBA	1/20	Seats filled	1/20
Description: Advance topics in current research in statistics and operations research. 1 - 3 units.