R

Workflow As a data analyst, it is absolutely essential that you keep detailed documentation of your analysis process. This means saving all of the code that you use for a project somewhere convenient so that you or anyone else can quickly and easily exactly reproduce your original results.

This post will teach you the basics of working with times series data in R as well as how to build simple forecasting models and evaluate their performance.

Exploratory data analysis (EDA) is an important part of the data science process. EDA is helpful for identifying patterns in data, examining the relationship between variables, and ultimately generating testable hypotheses and producing visualizations is one of the most effective ways to accomplish these tasks.

This workshop was designed for second-year MPP students at UCR. It serves as an introduction to data science as its own discipline and is aimed at teaching the students the fundamentals of conducting a data analysis project with R and Python.

In this post, we’ll walk through the Bayesian workflow for data analysis using the R package brms. To get started, we need to install Stan and brms. This involves a couple steps.

This post will walk you through how to use some of the most common generalized linear model and explain what problems they help solve and why. Before going through this code, I suggest that you take some time to review the presentation slides that I put together here.