Here is information about STOR class enrollment for **summer II 2023**. Classes with no meeting time listed are not shown. Feel free to contact me with any questions/comments/issues.

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Data also available for: COMP, AMST, COMM, MATH, STOR

Data last updated: 2023-07-26 21:00:19.248864

Class Number | Class | Meeting Time | Instructor | Room | Unreserved Enrollment | Reserved Enrollment | Wait List |
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1503 | STOR 155 - 001 Introduction to Data Models and Inference | MoTuWeThFr 9:45AM - 11:15AM | Wan Zhang | Hanes Hall - Rm 0125 | 20/30 (30 total) | Seats filled | |

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. | |||||||

1504 | STOR 155 - 002 Introduction to Data Models and Inference | MoTuWeThFr 11:30AM - 1:00PM | Emma Mitchell | Hanes Hall - Rm 0120 | 26/30 (30 total) | Seats filled | |

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. | |||||||

1505 | STOR 155 - 003 Introduction to Data Models and Inference | MoTuWeThFr 1:15PM - 2:45PM | Emma Mitchell | Hanes Hall - Rm 0125 | 24/30 (30 total) | Seats filled | |

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. | |||||||

1507 | STOR 320 - 001 Introduction to Data Science | MoTuWeTh 3:00PM - 5:00PM | Mario Giacomazzo | Hanes Hall - Rm 0120 | 15/30 (30 total) | Seats filled | |

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. | |||||||

1508 | STOR 320 - 002 Introduction to Data Science | MoTuWeTh 12:45PM - 2:45PM | Mario Giacomazzo | Hanes Hall - Rm 0107 | 15/30 (30 total) | Seats filled | |

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. | |||||||

1509 | STOR 320 - 400 Introduction to Data Science | Fr 3:00PM - 5:00PM | Dilshad Imon | Hanes Hall - Rm 0107 | 10/15 (15 total) | Seats filled | |

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. | |||||||

1510 | STOR 320 - 401 Introduction to Data Science | Fr 3:00PM - 5:00PM | Hui Shen | Greenlaw - Rm 0302 | 5/15 (15 total) | Seats filled | |

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. | |||||||

1511 | STOR 320 - 402 Introduction to Data Science | Fr 12:45PM - 2:45PM | Dilshad Imon | Dey Hall - Rm 0205 | 7/15 (15 total) | Seats filled | |

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. | |||||||

1512 | STOR 320 - 403 Introduction to Data Science | Fr 12:45PM - 2:45PM | Hui Shen | Hanes Hall - Rm 0107 | 8/15 (15 total) | Seats filled | |

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. | |||||||

1513 | STOR 455 - 001 Methods of Data Analysis | MoTuWeThFr 1:15PM - 2:45PM | Andrew Ackerman | Gardner - Rm 0105 | 20/30 (30 total) | Seats filled | |

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. |