STATS 415 - Data Mining and Statistical Learning
Section: 001
Term: FA 2018
Subject: Statistics (STATS)
Department: LSA Statistics
Requirements & Distribution:
Advisory Prerequisites:
MATH 215 and 217, and one of STATS 401, 406, 412 or 426.
This course counts toward the 60 credits of math/science required for a Bachelor of Science degree.
May not be repeated for credit.

This course covers the principles of data mining, exploratory analysis and visualization of complex data sets, and predictive modeling, Topics include: a) techniques and algorithms for exploratory data analysis and for discovering associations, patterns, changes, and anomalies in large data sets; and b) modern methods for multivariate analysis and statistical learning in regression, classification, and clustering. The presentation balances statistical concepts (such as model bias and over-fitting data, and interpreting results) and computational issues (including algorithmic complexity and strategies for efficient implementation). Students are exposed to algorithms, computations, and hands-on data analysis in weekly discussion sessions.

Course Requirements:

Evaluation will be based on weekly problem sets, one midterm exam, and a final project. The final project will be an individual project involving either data analysis using the methods covered in the course, or a simulation-based or analytical investigation of the properties of one of the methods covered in the course. Students will be expected to write a statement of their findings of approximately 3 pages in length, as well as providing clean and documented versions of their computer code,

Intended Audience:

Course can be used as an elective to satisfy the requirements of the statistics concentration, the applied statistics minor, and the statistics minor.

Class Format:

3 hours of lecture and 1 hour GSI-led discussion.

STATS 415 - Data Mining and Statistical Learning
Schedule Listing
001 (LEC)
TuTh 2:30PM - 4:00PM
002 (DIS)
Tu 8:30AM - 10:00AM
003 (DIS)
Tu 10:00AM - 11:30AM
004 (DIS)
Tu 11:30AM - 1:00PM
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