Exploratory Data Analysis in R: Seattle House Prices Case Study

Unlocking the stories hidden within real estate data requires a solid grasp of exploratory data analysis (EDA). This text-based course guides you through the process of analyzing real-world housing data using R, turning raw numbers into actionable market insights. You will transition from a beginner to a confident data analyst capable of importing, cleaning, and visualizing datasets. By working through a detailed case study of Seattle house prices, you will learn how to identify trends, handle missing values, and communicate data-driven findings clearly. What you'll learn: Understand foundational data science concepts and the core workflows of exploratory data analysis; Clean and prepare messy housing datasets using modern tidyverse packages like dplyr and tidyr; Create informative data visualizations with ggplot2 to reveal price distributions and geographic trends; Apply statistical summaries to describe key variables and relationships within the data; Practice modern data transformation techniques to handle outliers and missing values effectively. The course begins with essential terminology and setup, guiding you through data import and initial exploration. You will then progress step-by-step through data cleaning, visualization, and summarizing patterns to draw meaningful conclusions. This course is designed for absolute beginners in data science and R programming, with no prior coding or statistical experience required. Start your data analysis journey today and learn to uncover insights from real-world datasets.