🔍 From Question to Insight

Social data science isn’t magic — it’s a clear, structured process that turns curiosity into clarity, and complexity into opportunity.

🧭 1. Frame the Right Question

Good research starts not with data — but with purpose.

  • What decision or understanding is needed?
  • Who will act on the findings?
  • What kind of outcome would be most useful?

🔎 Example:
A city might ask, “Where should we target small business grants for maximum community impact?”

🛠️ 2. Gather and Engineer the Data (ETL)

Even the best question means little without good data.

  • Extract: Identify and pull relevant datasets — public, internal, or scraped.
  • Transform: Clean up messiness — typos, missing values, strange categories.
  • Load: Restructure the data into formats ready for analysis.

🛠️ Example:
A nonprofit needs to combine local unemployment statistics with their internal program data.

🔬 3. Explore the Data (EDA)

Before asking for “insights,” you must listen to what the data says.

  • Visualize distributions, clusters, outliers
  • Test assumptions
  • Identify surprising patterns or gaps

🧠 Example:
While exploring, you discover that most home improvement contracts cluster near certain ZIP codes — suggesting marketing opportunities.

📈 4. Analyze and Model

Once the landscape is clear, analysis can answer.

  • Regression to find drivers of outcomes
  • Clustering to find natural groupings
  • Geospatial analysis for location-based insights
  • Text analysis for open-ended survey responses
  • Time series forecasting to plan for the future

📊 Example:
Predicting how many remote workers a city will attract post-2025 based on housing and broadband data.

🎯 5. Communicate the Story

Numbers alone don’t change minds — narratives do.

  • Build clear, simple charts
  • Tell stories in human language
  • Offer specific recommendations, not just “findings”

📝 Example:
A report for a city council showing not just data on remote work migration, but policy suggestions based on it.

🛠️ Bonus: Other Help You Might Need

You don’t need to have “perfect” data to start.
Sometimes the need is earlier:

  • Setting up reliable data pipelines
  • Warehousing public and organizational data
  • Writing SEO-friendly reports or blog content using real data
  • Helping shape questions even before research begins

🛠️ Where the Data Comes From — and How I Work With It

Turning public questions into insight requires reliable sources and the right tools.
Here’s a glimpse into the places I gather data — and the technologies I use to unlock its value:

📚 Some Data Sources I Use:

  • 🏛️ U.S. Census Bureau
  • 📊 Bureau of Labor Statistics (BLS)
  • 🌐 data.gov and other federal open data portals
  • 🏙️ State, city, and regional government datasets
  • 🏥 Nonprofit, educational, and research organization databases
  • 🕸️ Web scraping public information (where legally and ethically appropriate)

🧰 Some Tools I Work With:

  • 🐍 Python — data wrangling, analysis, and modeling
    (Libraries: NumPy, pandas, Seaborn, scikit-learn, PyTorch)
  • 📊 R — statistical analysis and advanced modeling
  • 📈 Power BI — interactive dashboards for non-technical audiences
  • 🧮 SQL — working directly with large datasets
  • 📋 Excel — rapid prototyping and communication
  • 🔗 APIs — connecting live to government, nonprofit, and civic datasets

Good data + Clear questions + The right tools = Smart insights that serve real communities.

💡 Not Sure What You Need?

That’s totally fine. Start by describing your goal or pain point — I’ll help you figure out what kind of data work can support it.

➡️ Reach out with a question →

📬 Ready to Start?

Even messy beginnings can lead to powerful, practical insights.
Contact me! — Let’s explore what you want to know.

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