We're still actively developing this site. If you encounter any issues, please report them! - Report an issue

R FOR STATISTICS III

STAT 305
Course Description

Provides an understanding of the commonly used statistical language R. Learn to combine R with high performance computing tools to do scientific computing.

Prerequisties
Satisfies

This course does not satisfy any prerequisites.

Credits

1

Offered

Fall, Spring

Grade Point Average
3.52

-2.86% from Historical

Completion Rate
100%

2.45% from Historical

A Rate
64.29%

-17.73% from Historical

Class Size
28

-16.17% from Historical

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

Similar Courses

R FOR STATISTICS I

STAT 303

An understanding of the commonly used statistical language R. Topics will include using R to manipulate data and perform exploratory data analysis.

R FOR STATISTICS II

STAT 304

Provides an understanding of the commonly used statistical language R. Topics will include writing conditional expressions, loops, and functions; manipulating data matrices and arrays; extracting data from text; and making high level visualizations of data.

DATA SCIENCE MODELING II

STAT 340

Teaches how to explore, model, and analyze data using R. Topics include basic probability models; the central limit theorem; Monte Carlo simulation; one- and two-sample hypothesis testing; Bayesian inference; linear and logistic regression; ANOVA; the bootstrap; random forests and cross-validation. Features the analysis of real-world data sets and the communication of findings in a clear and reproducible manner within a project setting.

DATA SCIENCE MODELING I

STAT 240

Introduces reproducible data management, modeling, analysis, and statistical inference through a practical, hands-on case studies approach. Topics include the use of an integrated statistical computing environment, data wrangling, the R programming language, data graphics and visualization, random variables and concepts of probability including the binomial and normal distributions, data modeling, statistical inference in one- and two- sample settings for proportions and means, simple linear regression, and report generation using R Markdown with applications to a wide variety of data to address open-ended questions.

DATA SCIENCE WITH R

STAT 433

Perform Data Science as an iterative (back and forth) process of four different types of activities (data collection, data wrangling, data analysis, communication). Traverse through the five requisite stances (scientist, coder, mathematician, methodologist, skeptic). Develop and hone a broad set of computational tools in R (but not the broadest) and a broad set of statistical/machine learning tools (but not the broadest). Focus on doing these with agility to make the coding "transparent" and serve the large goals of the project.