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Fall 2020
Mar 07, 2021
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STAT 54500 - Introduction To Computational Statistics
Credit Hours: 3.00. This introductory course covers the fundamentals of computing for statistics and data analysis. It starts with a brief overview of programming using a general purpose compiled language (C) and a statistics-oriented interpreted language (R). The course proceeds to cover data structures and algorithms that are directly relevant to statistics and data analysis and concludes with a computing-oriented introduction to selected statistical methods. A significant part of the course involves programming and hands-on experimentation demonstrating the covered techniques, ration, and Markov chain Monte Carlo methods. Typically offered Fall.
3.000 Credit hours

Syllabus Available
Levels: Graduate, Professional, Undergraduate
Schedule Types: Distance Learning, Lecture
All Sections for this Course

Offered By: College of Science
Department: Statistics

Course Attributes:
Upper Division

May be offered at any of the following campuses:     
      West Lafayette

Learning Outcomes: 1. Given a straightforward data manipulation problem, decide on what elementary data structures to use, write down an algorithm for a solution as a pseudocode, and analyze its computational complexity. 2. Implement an algorithm using C and/or R code, understand, modify, and if needed perform basic debugging of the existing code. 3. Understand and implement the computational techniques of numerical optimization, matrix manipulation, sampling, logistic regression, and EM algorithm.

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