Syllabus

2019

Implement unsupervised and supervised learning methods for text data.

Introduce methods for text data, structuring text data in R, and conducting exploratory analysis.

Review methods for storing spatial data, define simple features data frames, and construct vector maps in ggplot2.

Identify components of geospatial visualizations and implement raster maps using ggplot2.

Practice scraping content from web pages using rvest.

Define an application program interface, write functions to query APIs, and practice tidying JSON objects.

Introduce tree-based classification and demonstrate cross-validation.

Review the goals of statistical learning, introduce methods for linear regression, and practice working with model objects in R.

All things related to R Markdown, plus a review of R scripts and Git troubleshooting.

Methods for implementing a tidy, reproducible workflow.

Define computer bugs, discuss defensive programming tactics, and practice troubleshooting scripts.

Define a vector, review iterative operations, and practice using loops and map functions.

Review the pipe, define a function, and practice writing and debugging functions.

Introduce relational data structures, and practice working with factor columns.

Data frames, importing data files, and tidying data.

Define exploratory data analysis and practice exploring data with visualization methods.

Computational problem-solving, verbs for data manipulation, and practice transforming data frames using dplyr.

Introduction to data visualizations, the grammar of graphics, and ggplot2.

Overview of programming, applications to social science, reproducible research, and course logistics.