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Fall 2018 Statistics GR5703 section 001
STAT INFERENCE & MODELING

Call Number 78031
Day & Time
Location
MW 5:40pm-6:55pm
627 Seeley W. Mudd Building
Points 3
Approvals Required None
Instructors Marco Avella
Jitong Qi
Type LECTURE
Course Description Prerequisites: (STAT GR5701) working knowledge of calculus and linear algebra (vectors and matrices), STAT GR5701 or equivalent, and familiarity with a programming language (e.g., R, Python) for statistical data analysis. In this course, we will systematically cover fundamentals of statistical inference and modeling, with special attention to models and methods that address practical data issues. The course will be focused on inference and modeling approaches such as the EM algorithm, MCMC methods and Bayesian modeling, linear regression models, generalized linear regression models, nonparametric regressions, and statistical computing. In addition, the course will provide introduction to statistical methods and modeling that addresses various practical issues such as design of experiments, analysis of time-dependent data,  missing values, etc. Throughpout the course, real-data examples will be used in lecture discussion and homework problems. This course lays the statistical foundation for inference and modeling using data, preparing the MS in Data Science students, for other courses in machine learning, data mining and visualization.
Web Site Vergil
Department Statistics
Enrollment 32 students (150 max) as of 11:54PM Monday, October 15, 2018
Subject Statistics
Number GR5703
Section 001
Division Graduate School of Arts and Sciences
Open To Engineering and Applied Science: Graduate
Campus Morningside
Note DSI MS students only
Section key 20183STAT5703G001

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SIS update 10/15/18 23:54    web update 10/16/18 15:04