Focuses on data wrangling and visualisation with the tidyverse, functional programming, simulation, and optimization. Discussions around reproducibility, data sharing, data privacy. Data wrangling, exploratory data analysis, predictive modeling, data visualization, and effective communication of results.
R STUDIO CLASS HOW TO
Combines techniques from statistics, math, computer science, and social sciences, to learn how to use data to understand natural phenomena, explore patterns, model outcomes, and make predictions. Year 1 undergraduate course for students with no background in stats, computing, or data science. Introduction to Data Science, by Mine Çetinkaya-Rundel. The toolset is based on rstanarm and brms packages. The topics covered are Bayesian vs Frequentist, Linear Models, General Linear Models and Hierarchical Models. It is aimed for graduate students (mainly from social/humane sciences) that have basic background in statistics and no background at all in computing. A course in portuguese offered at Universidade Nove de Julho - UNINOVE in São Paulo, Brazil. īayesian Statistics - Estatística Bayesiana, by ( ). In the third part, we will focus on Bayesian data analysis as an alternative framework for answering statistical questions. We will learn how to compare models using simulation methods such as bootstrapping and cross-validation. In the second part, we will cover topics in frequentist statistics (such as multiple regression, logistic regression, and mixed effects models) using the general linear model as an organizing framework.
The course is organized into three parts: In the first part, we will learn how to visualize, wrangle, and simulate data in R. It is a practical course in which learning statistical concepts and building models in R go hand in hand. This course offers an introduction to advanced topics in statistics with the focus of understanding data in the behavioral and social sciences. Psych 252: Statistical Methods, by Tobias Gerstenberg. For fun, there are also sections on creating generative art in R. The programming side to the class covers data structures in R, flow control and programming with functions.
R STUDIO CLASS CODE
Practical scientific topics covered include data visualisation, data wrangling, document preparation with R markdown, sharing code with GitHub and project management. An introductory class offered at the University of New South Wales, aimed at students with little to no background in statistics or computing. 2020ĭata Science in R: A Robust Toolkit For Psychological Research, by Danielle Navarro. These are semester/quarter long courses taught fully, or for the most part, in an in person setting. In person courses University courses teaching R If you have suggestions for courses to add, please submit a pull request or add an issue. This is a user curated list and is bound to be non-comprehensive at all times.
The goal is to create a repository where one can discover courses and learning materials for learning and teaching R. Inspired by the Using Julia in the classroom page and suggestion by post on RStudio Community as well as the Learn the tidyverse page.
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