Data Analytics and Visualization
This is the archive website for Autumn 2025, please refer to this page for the latest sites.
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
Lab Sessions
Lab 1 - Basic of R and RStudio
Covers setup of R, RStudio, and GitHub, basic R and Markdown skills, and accessing Census data for simple data exploration and reproducible analysis.
Lab 2 - Data Wrangling for Dataframes
Focuses on data wrangling with tidyverse—modifying, summarizing, and joining dataframes, sampling for the Central Limit Theorem, and using GitHub for version control.
Lab 3 - Preparing Data for Visualization
Introduces tidy data and exploratory data analysis (EDA) with ggplot2, including data cleaning, handling missing values, and basic visualizations using the Zillow Home Value Index.
LAB 4 – Visualization Using R and ggplot2
Focuses on data visualization design principles and implementation using ggplot2, exploring aesthetic mappings, scales, and themes for expressive, effective, and ethical graphics.
LAB 5 – Tableau and Spatial Data in R
Combines Tableau and R for spatial data visualization. Introduces interactive dashboard design in Tableau and geospatial analysis in R using
sf, focusing on coordinate systems, spatial joins, and map-based analytics.
LAB 6 – Dashboard using Tableau and Miscellaneous
This lab serves as the starting point for the final project’s exploratory analysis and introduces interactive dashboard design with Tableau. The optional Section B provides a brief introduction to command line tools, virtual environments, and SQL, highlighting how they support data processing, reproducibility, and database querying in real-world analytics workflows.
LAB 7 – Linear Regression
Covers linear regression in both statistical and machine learning frameworks. Part A introduces classical regression using King County sales data, focusing on model specification, assumptions, diagnostics, interactions, nonlinear terms, model comparison, and prediction. Part B introduces regression in machine learning using
tidymodels, including data preprocessing, penalized regression (Ridge/Lasso), cross-validation, tuning, and test-set evaluation.
LAB 8 – Machine Learning
Introduces two classical machine learning methods: decision trees (supervised) and k-means clustering (unsupervised). Also, introduces neural-network intuition through 3Blue1Brown video.
LAB 9 – Building a Chatbot
Introduces practical workflows for building and deploying an AI chatbot using large language models, with hands-on experience in Flowise and website integration.
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