STOR Courses

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
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.