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Trends in Startups

This guide walks you step by step through analyzing startup data using SQL. By the end, you’ll understand how to explore a dataset, use aggregate functions, group data, and extract meaningful business insights.

What You’ll Be Working With

You are given a SQLite database containing a table called startups. This table represents a portfolio of well-known startups and includes information such as valuation, funding stage, location, and number of employees.

Your goal is to ask useful questions of the data and answer them using SQL.

Step 1: Explore the Data Structure

Before doing any analysis, you need to understand what the table looks like.

SELECT *
FROM startups;

This query displays all rows and columns, helping you understand what data is available.

Step 2: Count the Number of Startups

SELECT COUNT(*)
FROM startups;

This query counts the total number of startups in the table.

Step 3: Calculate the Total Market Value

SELECT SUM(valuation)
FROM startups;

This calculates the combined valuation of all startups in the dataset.

Step 4: Find the Largest Funding Round

SELECT MAX(raised)
FROM startups;

This returns the highest funding amount raised by any startup.

Step 5: Filter by Startup Stage

SELECT MAX(raised)
FROM startups
WHERE stage = 'Seed';

This limits the analysis to Seed-stage startups only.

Step 6: Identify the Oldest Startup

SELECT MIN(founded)
FROM startups;

This returns the earliest founding year in the dataset.

Step 7: Calculate the Average Valuation

SELECT AVG(valuation)
FROM startups;

This provides a baseline average valuation across all startups.

Step 8: Compare Valuation by Category

SELECT category, AVG(valuation)
FROM startups
GROUP BY category;

This groups startups by category and calculates the average valuation for each.

Step 9: Round Results for Readability

SELECT category, ROUND(AVG(valuation), 2)
FROM startups
GROUP BY category;

This rounds average valuations to two decimal places.

Step 10: Rank Categories by Valuation

SELECT category, ROUND(AVG(valuation), 2)
FROM startups
GROUP BY 1
ORDER BY 2 DESC;

This sorts categories from highest to lowest average valuation.

Step 11: Identify Competitive Markets

SELECT category, COUNT(*)
FROM startups
GROUP BY category;

This shows how many startups exist in each category.

Step 12: Filter Competitive Categories

SELECT category, COUNT(*)
FROM startups
GROUP BY category
HAVING COUNT(*) > 3
ORDER BY 2 DESC;

This keeps only categories with more than three startups.

Step 13: Analyze Startup Size by Location

SELECT location, AVG(employees)
FROM startups
GROUP BY location;

This calculates average startup size by location.

Step 14: Focus on Large Startup Hubs

SELECT location, AVG(employees)
FROM startups
GROUP BY location
HAVING AVG(employees) > 500;

This highlights locations with large average team sizes.

Key Takeaways

  • Always explore the data first
  • Use aggregate functions to summarize information
  • Use GROUP BY to compare categories or locations
  • Use HAVING to filter aggregated results
  • Order results to highlight trends

This workflow applies to almost any SQL-based data analysis task.