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!

**Click here to show class descriptions**. Click here to hide them.

Data also available for: COMP, AAAD, AMST, ANTH, APPL, ASTR, BCB, BIOL, BIOS, BMME, BUSI, CHEM, CLAR, CMPL, COMM, DATA, DRAM, ECON, EDUC, ENEC, ENGL, ENVR, EPID, EXSS, GEOG, HBEH, HIST, INLS, LING, MATH, MEJO, PHIL, PHYS, PLAN, PLCY, POLI, PSYC, ROML, SOCI, STOR, WGST

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. |