3 credits
Spring 2026 Lecture Upper Division
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.
Learning Outcomes1Given 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.
2Implement an algorithm using C and/or R code, understand, modify, and if needed perform basic debugging of the existing code.
3Understand and implement the computational techniques of numerical optimization, matrix manipulation, sampling, logistic regression, and EM algorithm.
Prerequisites
Restrictions
made by Purdue students.