QAC 323
Fall 2016
| Section:
01
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| Crosslisting:
CIS 323 |
| Certificates: Applied Data Science |
This course introduces the applied principles of Bayesian statistical analysis. The Bayesian paradigm is particularly appealing in research where prior research and historical data are available on parameters of interest. This course will teach students appropriate techniques for analyzing data of this nature as well as broaden computational skills in R. The course will lay the foundation for Bayesian data analysis that students can use to further develop skills in decision making. |
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| Credit: .5 |
Gen Ed Area Dept:
NSM QAC, SBS QAC |
| Course Format: Laboratory Course | Grading Mode: Graded |
| Level: UGRD |
Prerequisites: MATH132 OR ECON300 OR [GOVT367 or QAC302] |
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Fulfills a Requirement for: (CADS)(DATA-MN)(PSYC) |
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Past Enrollment Probability: 90% or above |
| SECTION 01 - 2nd Quarter | | Special Attributes: CQC |
Major Readings: Wesleyan RJ Julia Bookstore
Albert, Jim, BAYESIAN COMPUTATION WITH R, Springer, 2009 (accessible online through the Wesleyan library).
Kruschke, John K., DOING BAYESIAN DATA ANALYSIS, Academic Press, 2011.
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Examinations and Assignments:
Several homework assignments and a take-home final exam. Part of the grade will depend on class preparation and participation. |
Additional Requirements and/or Comments:
An introductory statistics/probability background is a prerequisite for the course and that is why MATH 132 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 requirement through other course work. |
| Instructor(s): Nazzaro,Valerie L. Times: .....F. 01:20PM-04:10PM; Location: ALLB204; |
| Total Enrollment Limit: 16 | | SR major: 0 | JR major: 0 | | |
| Seats Available: 10 | GRAD: 1 | SR non-major: 6 | JR non-major: 6 | SO: 3 | FR: 0 |
| Drop/Add Enrollment Requests | | | | | |
| Total Submitted Requests: 0 | 1st Ranked: 0 | 2nd Ranked: 0 | 3rd Ranked: 0 | 4th Ranked: 0 | Unranked: 0 |
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