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Fall 2022 Statistics GR5703 section 001

Call Number 13858
Day & Time
TR 6:10pm-7:25pm
903 School of Social Work
Points 3
Grading Mode Standard
Approvals Required None
Instructor Dobrin Marchev
Method of Instruction In-Person
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 25 students (50 max) as of 5:33PM Monday, September 26, 2022
Subject Statistics
Number GR5703
Section 001
Division Interfaculty
Open To Engineering:Graduate
Campus Morningside
Note DSI students only
Section key 20223STAT5703W001

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SIS update 09/26/22 17:33    web update 09/26/22 21:16