Best City for Golf in the US, Data Analytics Project

Data Analyst · 2025 · 1 Week · 1 person · 2 min read

Built an end-to-end data pipeline and ranking system to identify the best U.S. cities for golf, combining course data, climate indicators, and geographic analysis.

Overview

This project analyzes and ranks U.S. cities based on golf accessibility, quality, and playability. Using Teeradar course data and state-level golfability metadata, I developed a data pipeline to collect, clean, aggregate, and visualize golf-related metrics. The analysis highlights top-performing golf cities and explores how climate and location influence rankings.

Problem

Golf enthusiasts and industry stakeholders lack a standardized, data-driven way to compare cities based on golf quality, availability, and year-round playability. Existing rankings are often subjective, outdated, or limited in scope.

Constraints

  • Reliance on third-party APIs and rate limits
  • Incomplete availability of pricing and tee-time data
  • Inconsistent city and course naming conventions
  • Limited access to private course metadata

Approach

I designed an automated data pipeline to fetch raw course data from the Teeradar API, consolidate it into structured formats, and aggregate metrics at the city level. I normalized and weighted key indicators to produce composite scores, then explored trends and rankings using interactive visualizations in Jupyter notebooks.

Key Decisions

Use Teeradar as the primary data source

Reasoning:

Teeradar provided comprehensive nationwide coverage with structured course metadata and ratings.

Aggregate results at the city level

Reasoning:

City-level analysis balances geographic relevance with data availability and supports meaningful comparisons.

Apply MinMax normalization for scoring

Reasoning:

Normalization allowed fair comparison across metrics with different scales.

Include state-level golfability as an optional feature

Reasoning:

This enabled sensitivity analysis to evaluate how year-round playability impacts rankings.

Tech Stack

  • Python
  • Pandas
  • NumPy
  • Jupyter Notebook
  • Matplotlib
  • Seaborn
  • SQLite
  • Parquet

Result & Impact

  • 500+
    Cities Analyzed
  • 15,000+
    Golf Courses Processed
  • 3 (Parquet, NDJSON, SQLite)
    Data Formats Generated
  • 100+ cities
    Top Rankings Evaluated

The project identified Orlando, FL and Scottsdale, AZ as leading golf cities, validated expected regional trends, and revealed underrepresented areas. It demonstrates how structured data pipelines can support location-based lifestyle decisions.

Learnings

  • Designing scalable data pipelines for API-driven datasets
  • Handling geospatial and city-level aggregation challenges
  • Building composite scoring systems and evaluating their sensitivity
  • Communicating analytical findings through visual storytelling
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