School project
UX/UI
Data Visualization
Presentation
RESEARCH
The process began with researching and identifying a dataset suitable for the project. A dataset containing 40 data points and 14 variables was selected, providing information on property frequencies, improvement costs, rents, and prices.
DATA CLEANING & PREPARATION
After reviewing the dataset, it was streamlined to focus on four key variables: frequency, improvement costs, rents, and prices. To improve clarity, frequency values were scaled (multiplied by 10,000) to make them more comparable with currency-based variables on charts.
INITIAL VISUALIZATION
The first visualization was created using Excel. However, this version violated Tufte’s principles due to issues like excessive decoration, unnecessary 3D shapes, and overlapping bars that obscured data. It suffered from "chart junk" and lacked clarity and integrity.
IMPROVEMENT & REFINEMENT
A second version of the visualization addressed these flaws by simplifying the design:
Switched from 3D to 2D bar charts.
Reduced decoration and used a monochromatic color palette.
Arranged bars horizontally to prevent overlapping.
These changes made the graph clearer and easier to interpret. Insights from this version showed that spreading investments across properties with higher frequencies could yield better results than focusing on expensive, low-frequency properties.
ADVANCED VISUALIZATION IN TABLEAU
A final version was developed in Tableau, leveraging advanced features to improve data representation:
Used different units of measurement for each variable, eliminating the need to scale values manually.
Represented all property categories, including railroads, which were previously excluded due to their lack of house-related data.
Introduced glyphs to visually differentiate variables, such as a green dollar icon for rents (indicating income) and dice icons for frequency.
ANALYSIS & INSIGHTS
The final visualization highlighted important strategic insights, such as balancing investment across properties with high frequency and moderate costs to maximize profitability. It also demonstrated the importance of clear, accessible data representation in decision-making.
