R for Data Science |
Basic operations, data wrangling, model building and graphing |
Databases using R |
Work with databases in R |
Bayesian Data Analysis |
Basics of Bayesian statistics and demos in R |
R Cookbook |
Comprehensive R review covering most basic operations, general stats, graphics, time series analysis and markdown |
Data science for economists |
Another comprehensive R review covering version control, web scrapping, spatial analysis, and other tools like Docker, Google Compute Engine, SQL and Spark |
Applied Causal Analysis (with R) |
Introduces concepts such as ATT, ATE, SUTVA and tools for causal analysis (DiD, matching, RDD) |
Statistical Rethinking 2 with Stan and R |
Replicates models in Richard McElreath's Statistical Rethinking (2nd ed.) book using Stan, R, rstan, tidybayes, and ggplot2 |
Finmetrics |
Quantitative analysis of financial data |
Computational Economics |
Introduction to computational approaches for solving economic models |
Tidy Portfoliomanagement in R |
Quantitative analysis of financial data and portfolio management |
Econometrics II |
Advanced undergrad econometrics with focus on empirical research covering topics such as causal inference, panel, nonlinear methods and time series |
Data Science: Theories, Models, Algorithms, and Analytics |
Machine learning in R covering mathematical and statistical operations, text analytics, networks, discriminant analysis, clustering, neural networks, finance models |
Happy Git and GitHub for the useR |
Working with Git, GitHub in the shell and RStudio |
STAT 545 |
Intro to data wrangling and visualization, also deals with making packages, web scrapping and Shiny |
Geocomputation with R |
Geographic data analysis, visualization and modeling |
R Markdown: The Definitive Guide |
Comprehensive guide to R Markdown (document format) |
Mastering Spark with R |
Apache Spark with R in large scale data science |
Forecasting: Principles and Practice |
Concepts of and introduction to forecasting methods |
Advanced R |
Advanced concepts in R useful for understanding why R works the way it does |
Text Mining with R |
Analyzing text-heavy and unstructured data |
Fundamentals of Data Visualization |
Data visualization |
Computing for the Social Sciences |
Covers a wide range of topics including text analysis, Shiny, Markdown, webscrapping, geospatial visualization, exploratory data analysis and Spark |
Interactive web-based data visualization with R, plotly, and shiny |
Teaches practical skills for creating interactive and dynamic web graphics for data analysis from R |
Congressional Data in R |
Overview of Congessional datasets and R packages for joining/merging, cleaning, visualization, and modeling |