Here is information about STOR class enrollment for summer II 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: 2024-06-13 14:07:04.467788
Class Number | Class | Meeting Time | Instructor | Room | Unreserved Enrollment | Reserved Enrollment | Total Enrollment | Wait List |
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2099 | STOR 113 - 001 Decision Models for Business and Economics | MoTuWeThFr 11:30AM - 1:00PM | Sumit Kumar Kar | Hanes Hall-Rm 0130 | 13/46 | Seats filled | 13/46 | 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. | ||||||||
2102 | STOR 115 - 001 Reasoning with Data: Navigating a Quantitative World | MoTuWeThFr 9:45AM - 11:15AM | Hank Flury | Davie Hall-Rm 0301 | 0/10 | Seats filled | 0/10 | 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. | ||||||||
2103 | STOR 120 - 001 Foundations of Statistics and Data Science | MoTuTh 9:45AM - 11:45AM | Andrew Ackerman | Hanes Hall-Rm 0107 | 16/34 | Seats filled | 16/34 | 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. 4 units. | ||||||||
2104 | STOR 120 - 400 Foundations of Statistics and Data Science | WeFr 9:45AM - 10:40AM | Akshay Sakanaveeti | Hanes Hall-Rm 0107 | 8/17 | Seats filled | 8/17 | 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. | ||||||||
2105 | STOR 120 - 401 Foundations of Statistics and Data Science | WeFr 9:45AM - 10:40AM | Kyung Rok Kim | Dey Hall-Rm 0203 | 8/17 | Seats filled | 8/17 | 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. | ||||||||
1288 | STOR 155 - 001 Introduction to Data Models and Inference | MoTuWeThFr 9:45AM - 11:15AM | Trung Nghia Nguyen | Hanes Hall-Rm 0120 | 13/30 | Seats filled | 13/30 | 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. | ||||||||
1289 | STOR 155 - 002 Introduction to Data Models and Inference | MoTuWeThFr 11:30AM - 1:00PM | Tianzhu Liu | Hanes Hall-Rm 0125 | 6/30 | Seats filled | 6/30 | 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. | ||||||||
1290 | STOR 155 - 003 Introduction to Data Models and Inference | MoTuWeThFr 1:15PM - 2:45PM | Sumit Kumar Kar | Hanes Hall-Rm 0125 | 18/30 | Seats filled | 18/30 | 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. | ||||||||
1291 | STOR 320 - 001 Introduction to Data Science | MoTuWeTh 12:45PM - 2:45PM | Kendall Thomas | Hanes Hall-Rm 0120 | 26/30 | Seats filled | 26/30 | 0/999 |
Description: Prerequisite, STOR 120 or 155. Development of basic skill set for data analysis from obtaining data to data carpentry, exploration, modeling, and communication. Topics covered include regression, clustering, classification, algorithmic thinking, and non-standard data objects (networks and text data). Students may not receive credit for both STOR 320 and STOR 520. 4 units. | ||||||||
1293 | STOR 320 - 400 Introduction to Data Science | Fr 12:45PM - 2:45PM | Dilshad Imon | Hanes Hall-Rm 0107 | 13/15 | Seats filled | 13/15 | 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. | ||||||||
1294 | STOR 320 - 401 Introduction to Data Science | Fr 12:45PM - 2:45PM | Coleman Ferrell | Dey Hall-Rm 0405 | 13/15 | Seats filled | 13/15 | 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. | ||||||||
1297 | STOR 455 - 001 Methods of Data Analysis | MoTuWeThFr 1:15PM - 2:45PM | Andrew Ackerman | Hanes Hall-Rm 0130 | 27/35 | Seats filled | 27/35 | 0/999 |
Description: Prerequisite, STOR 120, or 155. Review of basic inference; two-sample comparisons; correlation; introduction to matrices; simple and multiple regression (including significance tests, diagnostics, variable selection); analysis of variance; use of statistical software. 3 units. |