Here is information about STOR class enrollment for **summer I 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 also available for: COMP, AMST, COMM, MATH, STOR

Data last updated: 2024-05-24 13:23:35.499380

Class Number | Class | Meeting Time | Instructor | Room | Unreserved Enrollment | Reserved Enrollment | Total Enrollment | Wait List |
---|---|---|---|---|---|---|---|---|

1323 | STOR 113 - 001 Decision Models for Business and Economics | MoTuWeThFr 11:30AM - 1:00PM | Dilay Ozkan | Hanes Hall-Rm 0107 | 11/30 | Seats filled | 11/30 | 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. | ||||||||

1325 | STOR 120 - 001 Foundations of Statistics and Data Science | MoTuTh 9:45AM - 11:45AM | Jeff McLean | Dey Hall-Rm 0307 | 9/30 | Seats filled | 9/30 | 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. | ||||||||

1326 | STOR 120 - 002 Foundations of Statistics and Data Science | MoTuTh 1:15PM - 3:15PM | Jeff McLean | Hanes Hall-Rm 0120 | 11/30 | Seats filled | 11/30 | 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. | ||||||||

1336 | STOR 120 - 400 Foundations of Statistics and Data Science | WeFr 9:45AM - 10:40AM | Ishmael Benjamin Torres Aguilar | Hanes Hall-Rm 0107 | 4/5 | Seats filled | 4/5 | 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. | ||||||||

1335 | STOR 120 - 401 Foundations of Statistics and Data Science | WeFr 9:45AM - 10:40AM | Can Er | Dey Hall-Rm 0208 | 5/7 | Seats filled | 5/7 | 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. | ||||||||

1334 | STOR 120 - 402 Foundations of Statistics and Data Science | WeFr 1:15PM - 2:10PM | Ishmael Benjamin Torres Aguilar | Hanes Hall-Rm 0107 | 5/6 | Seats filled | 5/6 | 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. | ||||||||

1333 | STOR 120 - 403 Foundations of Statistics and Data Science | WeFr 1:15PM - 2:10PM | Can Er | Dey Hall-Rm 0405 | 6/13 | Seats filled | 6/13 | 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. | ||||||||

1327 | STOR 155 - 001 Introduction to Data Models and Inference | MoTuWeThFr 9:45AM - 11:15AM | Oluremi Abayomi | Hanes Hall-Rm 0120 | Seats filled | Seats filled | 25/25 | 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. | ||||||||

1328 | STOR 155 - 002 Introduction to Data Models and Inference | MoTuWeThFr 11:30AM - 1:00PM | Panagiotis Andreou | Hanes Hall-Rm 0130 | 10/30 | Seats filled | 10/30 | 0/999 |

Description: Prerequisite, MATH 110. Data analysis; correlation and regression; sampling and experimental design; basic probability (random variables, expected values, normal and binomial distributions); hypothesis testing and confidence intervals for means, proportions, and regression parameters; use of spreadsheet software. 3 units. | ||||||||

1329 | STOR 155 - 003 Introduction to Data Models and Inference | MoTuWeThFr 1:15PM - 2:45PM | WILLIAM LASSITER | Hanes Hall-Rm 0130 | 5/30 | Seats filled | 5/30 | 0/999 |

Description: Prerequisite, MATH 110. Data analysis; correlation and regression; sampling and experimental design; basic probability (random variables, expected values, normal and binomial distributions); hypothesis testing and confidence intervals for means, proportions, and regression parameters; use of spreadsheet software. 3 units. | ||||||||

2167 | STOR 320 - 001 Introduction to Data Science | MoTuWeTh 12:45PM - 2:45PM | Oluremi Abayomi | Dey Hall-Rm 0206 | 22/45 | Seats filled | 22/45 | 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. | ||||||||

2168 | STOR 320 - 400 Introduction to Data Science | Fr 12:45PM - 2:45PM | Dilshad Imon | Hanes Hall-Rm 0112 | 10/15 | Seats filled | 10/15 | 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. | ||||||||

2169 | STOR 320 - 401 Introduction to Data Science | Fr 12:45PM - 2:45PM | Coleman Ferrell | Dey Hall-Rm 0302 | 12/15 | Seats filled | 12/15 | 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. | ||||||||

1331 | STOR 435 - 001 Introduction to Probability | MoTuWeThFr 9:45AM - 11:15AM | CHUANSHU JI | Hanes Hall-Rm 0125 | 11/50 | Seats filled | 11/50 | 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. | ||||||||

1332 | STOR 435 - 002 Introduction to Probability | MoTuWeThFr 11:30AM - 1:00PM | CHUANSHU JI | Hanes Hall-Rm 0125 | 12/50 | Seats filled | 12/50 | 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. |