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NOTE: Course information changes frequently, including Methods of Instruction. Please revisit these pages periodically for the most recent and up-to-date course information.

Spring 2022 Statistics GU4243 section 002
Climate Pred Challenges

Call Number 17318
Day & Time
T 4:10pm-6:40pm
903 School of Social Work
Points 3
Grading Mode Standard
Approvals Required None
Instructors Tian Zheng - e-mail, homepage
Galen A McKinley
Method of Instruction In-Person
Course Description Prerequisites: Pre-requisite for this course includes working knowledge in Statistics and Probability, data mining, statistical modeling and machine learning. Prior programming experience in R or Python is required. This course will incorporate knowledge and skills covered in a statistical curriculum with topics and projects in data science. Programming will be covered using existing tools in R. Computing best practices will be taught using test-driven development, version control, and collaboration. Students finish the class with a portfolio of projects, and deeper understanding of several core statistical/machine-learning algorithms. Short project cycles throughout the semester provide students extensive hands-on experience with various data-driven applications.
Web Site Vergil
Department Statistics
Enrollment 3 students (4 max) as of 5:34PM Tuesday, July 5, 2022
Subject Statistics
Number GU4243
Section 002
Division Interfaculty
Campus Morningside
Note Undergraduate students only. Instructor approval required.
Section key 20221STAT4243W002

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SIS update 07/05/22 17:34    web update 07/05/22 20:58