Schedule

Course Schedule
subject to change
Week
	Topic
	Tools
		
	Preliminaries
			
1
	Course Introduction
	Scripts; R and RStudio
		
2
	A Guide to Your Computer
	Markup Languages; Quarto
		
3
	File Management & Version Control
	Filesystems; git; GitHub
		
4
	A Field Guide to Data
	Data Formats; surveys, readr; tidyr
		
5
	Structural Data Manipulation
	dplyr; srvyr
		
6
	Data Visualization I
	Grammar of Graphics; ggplot2
		
7
	Data Visualization II
	ggplot2
		
8
	Core Exam (Thursday, October 15)
			
9
	Workflow & Data Retrieval
	tidycensus; APIs; database/SQL overview
		
10
	Social Networks & Network Data
	iGraph; statnet
		
11
	Cartography
	tidyverse mapping; color scales; projection
		
12
	Geographic Data
	sf; tigris; mapgl; osm
		
13
	Grab Bag: Statistics, Text, & Working with AI
	Loops, t.test(), lm(), glm()
		
14
	Project Presentations
			
F
	Finals Week (Project Due)

—————
Advanced Topics (If we get to them)
Week
	Topic
	Tools
		
	Text Data & Data Scraping
			
	Statistical Models
			
	Missing Data
			
	Web Apps & Visualization
	quarto, shiny
		
	Programming with AI
	Claude Code
		
	Local LLMs

Logistical Notes
All content may be found linked from the course content pages. Readings and content are posted for the whole semester, so students may look ahead if they wish. Typically, each week has two class days: the first, a lecture, and the second, a workshop. Each workshop day typically has some computing tasks, reference reading or other material, and a data assignment.
What to expect
Each week of class is organized into a learning module. Each learning module covers a topic relevant to data wrangling, management, and visualization, and goes in three phases: lecture, workshop, and assignment.
1.	Lectures: Each module’s material is first presented through lectures given Tuesday. Occasionally, there will be an at-home workshop to complete as well. You should start every week with these. Each week has companion readings and reference materials for those wishing to dive deeper.
2.	Workshops: Each module’s material is then reinforced through a workshop in our Thursday class session.
3.	Assignment: Each module concludes with an applied assignment (typically, a problem set, but occasionally something else - a practicum or project). The module’s assignment is due the week after the module in class, and we will go over solutions together the day it is due.
Workshops (Live Class Sessions)
During workshop sessions, we cover material in the following order:
1.	Go over solutions and material for the assignment due that week. Sometimes, this will primarily involve the professor covering solutions, but other times, it will involve students presenting their work on that week, as well. Be prepared to ask questions and talk about your assignments.
2.	Conduct hands-on coding sessions for material in the current week’s module.
3.	Allow for time to begin the assignment for the current week’s module, ask the professor questions, etc.
Where to find materials and how to navigate the course
Each learning module can be accessed here, on the class website. We’ll use Blackboard for initial assignment submission before transferring to GitHub a few assignments in. All course materials may be found here, with the exception of some that contain proprietary or copyrighted material that must be kept behind a login.