Data Analytics and Visualization
RE 519, Runstad Department of Real Estate, University of Washington, Autumn 2025
Monday & Wednesday, 2:30 - 3:50pm, Mechanical Engineering Building 245
Haoyu Yue (Pre-doctoral Instructor), [email protected], Schedule Office Hours
Real estate decision-making requires the assessment of interdisciplinary datasets, which include socioeconomic, financial, and environmental data. Determining evolving patterns, analyzing and visualizing them, is critical in holistically assessing an area and a real estate decision to be made. This course aims to provide you with an opportunity to improve your coding ability and demonstrate that using R is more replicable and efficient than Excel.
We will work in groups to solve tricky data analytics & visualization problems. Of course, we will encounter a lot of new commands and new ways of thinking about how data is organized. However, we will most importantly learn how to understand the process of data analysis and how to best inform our audience and honestly describe the underlying data. The course is developed based on materials from Dr. Feiyang Sun at UC San Diego, Siman Ning, and Christian Phillips.
September 22, 2025
Welcome to RE 519 Data Analytics and Visualization in Autumn 2025. We are looking forward to meeting you in person on our first day of class (Wednesday, September 24, 2025). The course website will be the main place for all course information and materials. We will use Canvas as a place for submitting assignments and for grading purposes. Ed Discussion will be used for announcements, discussion, and technical questions.
Before the first class on Wednesday, September 24, 2025, please:
- Finish the pre-class survey.
- Look through the readings for this class on the course website.
- Look through the Lab 1 Part A and try to install R and RStudio in advance. No worries if you encounter any trouble, we will install them in the first class.
- Bring your laptop, no matter whether Windows, Mac, or Linux, to each class!
Schedules
Go to the recent section. This schedule is subject to change, and please check back regularly for updates. All readings and materials can be directly accessed via the links below, although some may require a UW NetID login. Some readings and links about R/coding are on each lab session page.
I - Introduction to Data Science and R
- Sep 24
- Overview and the Values of Data Science
- Slides / Pre-class Survey
- OptionalData Science and its Relationship to Big Data and Data-driven Decision Making. Foster Provost and Tom Fawcett. 2013.
OptionalGetting ahead of the Market: How Big Data is Transforming Real Estate. McKinsey & Company. 2018.- Sep 29
- Data in Real Estate
- LAB 1-B Basic of R/RStudio and Markdown
- REQUIREDChapter 1-2, Real Estate Analysis in the Information Age. Kimberly Winson-Geideman, et al. 2018.
OptionalChapter 1-3, Seeing Theory - A Visual Introduction to Probability and Statistics. Daniel Kunin.
OptionalChapter 5: Market, Place, Interface, All Data Are Local: Thinking Critically in a Data-Driven Society. Yanni Alexander Loukissas. 2019.
OptionalCensus Data for People and Housing. Yanni Christopher C. Brown. 2020. - LAB 1-B Basic of R/RStudio and Markdown
- Oct 01
- Data Science Workflows and Components
- LAB 1-C Dataframes and Accessing Census Data
- REQUIREDChapter 3-4, Real Estate Analysis in the Information Age. Kimberly Winson-Geideman, et al. 2018.
- LAB 1-C Dataframes and Accessing Census Data
- Oct 06
- Data Science for Real Estate at Macro- and Micro-level
- LAB 1 DUE 11:59 PM
- Oct 08
- Policy and Data-Driven Decision
- LAB 2-B Summarizing and Joining Dataframes
- RequiredFinding Common Ground: Best Practices for State Policies Supporting Transit-Oriented Development. Mason A. Virant, et al. 2024.
OptionalUrban Big Data: City Management and Real Estate Markets. Richard Barkham, et al. 2022.- Team Formation Due
- Project Team Formation Instruction
- LAB 2-B Summarizing and Joining Dataframes
II - Data Visualization
- Oct 13
- Data Visual Design
- LAB 3-A Tidy Data and GitHub
- LAB 2 DUE 11:59 PM
OptionalData Viz Project by ferdio.
OptionalDesign and Redesign in Data Visualization. Martin Wattenberg and Fernanda Viégas. 2015.
OptionalVisual and Statistical Thinking: Displays of Evidence for Making Decisions. Edward Tufte. 1997.
OptionalPreface & Chapter 1, Data Visualization. Kieran Healy. 2019. - LAB 3-A Tidy Data and GitHub
- Oct 15
- Exploratory Data Analysis (EDA)
- LAB 3-B Basic EDA in R
- OptionalExploratory Data Analysis. United States Environmental Protection Agency.
- LAB 3-B Basic EDA in R
- Oct 20
- Visualization Perception
- LAB 4 Visualization using R and ggplot2
- LAB 3 DUE 11:59 PM
- LAB 4 Visualization using R and ggplot2
- Oct 22
- No Class
- Instructor leave due to ACSP Annual Conference 2025
- Oct 27
- Geospatial Data Science
- LAB 5-A Geospatial Data and Visualization in R
- LAB 4 DUE 11:59 PM
- LAB 5-A Geospatial Data and Visualization in R
- Oct 29
- Interactive Visualization
- LAB 5-B shiny: Interative Visualization using R
- Nov 03
- Introduction to Tableau
- LAB 6-A Tableau - I
- LAB 5 DUE 11:59 PM
- LAB 6-A Tableau - I
- Nov 05
- Dashboard in Tableau
- LAB 6-B Tableau - II
III - Data Modeling
- Nov 10
- Correlation and Linear Regression
- LAB 7-A
- LAB 6 DUE 11:59 PM
OptionalChapter 4-6, Seeing Theory - A Visual Introduction to Probability and Statistics. Daniel Kunin.
OptionalStatistical Modeling: The Two Cultures. Leo Breiman. 2001. - LAB 7-A
- Nov 12
- Logistic and Multiple Linear Regression
- LAB 7-BCoding
- Project Proposal Due 11:59 PM
- Project Proposal Instruction
- LAB 7-BCoding
- Nov 17
- Spatiotemporal Data Analysis
- LAB 8-A
- LAB 7 DUE 11:59 PM
- LAB 8-A
- Nov 19
- Intro to Supervised and Unsupervised Learning
- LAB 8-B
- Nov 24
- Guest Speaker (TBD)
- LAB 8 DUE 11:59 PM
- Nov 26
- Project + Coding Clinic (Optional Online Session)
- Zoom
- Dec 01
- Artificial Intelligence and Data Science Ethnics
RequiredA Brief Overview of AI governance for Responsible Machine Learning Systems. Navdeep Gill et al. 2022.
OptionalAI for Social Good. Nature Communications. Nenad Tomašev et al. 2020.
OptionalAtlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Kate Crawford. 2022.
OptionalA Golden Decade of Deep Learning: Computing Systems & Applications. Jeffrey Dean (UW Alumni, Google). 2022.
OptionalWhat is a Neural Network. 3Blue1Brown. 2017.
IV - Data Analytics and Visualization Projects
- Dec 03
- Project Presentation
- Project Presentation Instruction
- Dec 12
- Final Project Submission
- Project Submission Instruction
- Final Project DUE 11:59 PM
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Built on Just the Class developed by Kevin Lin at Allen School