|Certificates: Applied Data Science|
The course provides a broad overview of machine learning algorithms and focuses on their application in data mining. Building on a basic background of regression analysis, and following a "learning-by-doing" approach, students are introduced to data mining tools and techniques that are used to identify patterns and relationships in large and complex data. While the emphasis is on intuition and application rather than theoretical results, through different case studies, students are introduced to the fundamentals of the different methods, and learn how to conceptualize a problem, analyze it using appropriate tools, and communicate their results.
||Gen Ed Area Dept:
NSM QAC, SBS QAC|
|Course Format: Lecture / Discussion||Grading Mode: Graded|
||Prerequisites: QAC211 OR [PHYS221 or QAC221 or CIS231] OR ECON300 OR [GOVT367 or QAC302] OR MATH231 OR MATH232
||Fulfills a Major Requirement for: (CADS)(DATA-MN)
||Past Enrollment Probability: Not Available
|SECTION 01 In-person only|
|Special Attributes: CQC|
|Major Readings: Wesleyan RJ Julia Bookstore
L. Torgo, DATA MINING USING R: LEARNING WITH CASE STUDIES, 2010,CRC Press and Journal articles
|Examinations and Assignments: |
This course will have several homework assignments designed as small projects every two weeks. A dataset will be assigned for each project. Students are required to use skills they learn in the previous two weeks to mining the dataset. There will be one final project for which students are responsible to choose their data set. We will point them to databases and provide datasets for them to choose from according to their own interests. Part of the course work (e.g. final project) will require students to work in small teams.
|Additional Requirements and/or Comments: |
An introductory statistics/data analysis background is a prerequisite for the course and that is why QAC201, or 211, or 221 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 in R is a significant part of the course work.
|Instructor(s): Ouyang,Ning Times: ..T.R.. 02:40PM-04:00PM; Location: PAC100; |
|Total Enrollment Limit: 19||SR major: 0||JR major: 0|| || |
|Seats Available: 8||GRAD: 1||SR non-major: 7||JR non-major: 7||SO: 4||FR: 0|
|Drop/Add Enrollment Requests|
|Total Submitted Requests: 0||1st Ranked: 0||2nd Ranked: 0||3rd Ranked: 0||4th Ranked: 0||Unranked: 0|