Exploratory Data Analysis and Pattern Discovery
QAC 305
Spring 2018
| Section:
01
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The course introduces the theory and practice of exploring, describing, summarizing and detecting patterns of interest in complex datasets. Various approaches including aggregation, clustering, data visualization, and latent variable modeling will be employed. This course will give students an opportunity to develop computational skills (primarily in SAS) and to learn how to discover and interpret relationships in unstructured observational data. The applications and examples for this course will be broad and relevant to many fields of study. |
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| Credit: 1 |
Gen Ed Area Dept:
NSM QAC, SBS QAC |
| Course Format: Lecture / Discussion | Grading Mode: Graded |
| Level: UGRD |
Prerequisites: QAC211 OR ECON300 OR GOVT367 |
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Fulfills a Requirement for: (CADS)(DATA-MN)(PSYC) |
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Past Enrollment Probability: 90% or above |
| SECTION 01 | | Special Attributes: CQC |
Major Readings: Wesleyan RJ Julia Bookstore
Kaufman, L. (2005). FINDING GROUPS IN DATA: AN INTRODUCTION TO CLUSTER ANALYSIS. Wiley: New Jersey.
Fabrigar, L. & Wegener, D. (2011). EXPLORATORY FACTOR ANALYSIS. Oxford: London.
Everit, B. (2010). APPLIED MULTIVARIATE DATA ANALYSIS. Wiley: London.
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Examinations and Assignments:
Several homework assignments and a semester-long course project, which will involve turning complex information into actionable insights using a dataset of their choice. A portion of the grade will be based on class participation and preparedness. |
Additional Requirements and/or Comments:
An introductory statistics/data analysis background is a prerequisite for the course and that is why QAC211, or ECON 300 or GOVT 367 are listed as formal prerequisites. Pre-req overrides will be approved by the Professor for students who satisfy this basic requirements through other course work. The course includes a strong lab component and programming with statistical computing tools is a significant part of the course work. |
| Instructor(s): Kabacoff,Robert Ira Times: .M.W... 10:50AM-12:10PM; Location: ALLB204; |
| Total Enrollment Limit: 19 | | SR major: 0 | JR major: 0 | | |
| Seats Available: 4 | GRAD: 1 | SR non-major: 7 | JR non-major: 7 | SO: 4 | FR: 0 |
| Drop/Add Enrollment Requests | | | | | |
| Total Submitted Requests: 1 | 1st Ranked: 0 | 2nd Ranked: 0 | 3rd Ranked: 0 | 4th Ranked: 0 | Unranked: 1 |
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