Project - Analyzing Unicorn Companies

Did you know that the average return from investing in stocks is 10% per year! But who wants to be average?!

You have been asked to support an investment firm by analyzing trends in high-growth companies. They are interested in understanding which industries are producing the highest valuations and the rate at which new high-value companies are emerging. Providing them with this information gives them a competitive insight as to industry trends and how they should structure their portfolio looking forward.

You have been given access to their unicorns database, which contains the following tables:

dates

Column	Description
company_id	A unique ID for the company.
date_joined	The date that the company became a unicorn.
year_founded	The year that the company was founded.

funding

Column	Description
company_id	A unique ID for the company.
valuation	Company value in US dollars.
funding	The amount of funding raised in US dollars.
select_investors	A list of key investors in the company.

industries

Column	Description
company_id	A unique ID for the company.
industry	The industry that the company operates in.

companies

Column	Description
company_id	A unique ID for the company.
company	The name of the company.
city	The city where the company is headquartered.
country	The country where the company is headquartered.
continent	The continent where the company is headquartered.

DataFrameas

df1

variable

[77]

-- Identify top 3 industries based on new unicorns in last three years
SELECT industry, COUNT(*)
FROM industries
JOIN dates
USING(company_id)
WHERE EXTRACT(year FROM date_joined) IN (2019, 2020, 2021)
GROUP BY industry
ORDER BY COUNT(*) DESC
LIMIT 3

DataFrameas

df

variable

-- My way, which orders industries by total number of new unicorns, not name
WITH top3 AS 
	(SELECT industry,
			EXTRACT(year FROM date_joined) AS year,
			COUNT(*) AS num_unicorns,
			SUM(COUNT(*)) OVER(PARTITION BY industry),
	 		ROUND(AVG(valuation) / 1000000000, 2) AS average_valuation_billions
	  FROM industries
	  JOIN dates
	 USING(company_id)
	  JOIN funding
	 USING(company_id)
	 WHERE EXTRACT(year FROM date_joined) IN (2019, 2020, 2021)
			AND industry IN (
				SELECT industry
				  FROM industries
				  JOIN dates
				 USING(company_id)
				 WHERE EXTRACT(year FROM date_joined) IN (2019, 2020, 2021)
				 GROUP BY industry
				 ORDER BY COUNT(*) DESC
				 LIMIT 3)
	 GROUP BY industry, year
	 ORDER BY sum DESC, year DESC)

-- Extract desired columns
SELECT industry, year, num_unicorns, average_valuation_billions
  FROM top3;

DataFrameas

df2

variable

-- The way the brief requested
SELECT industry,
		EXTRACT(year FROM date_joined) AS year,
		COUNT(*) AS num_unicorns,
 		ROUND(AVG(valuation) / 1000000000, 2) AS average_valuation_billions
  FROM industries
  JOIN dates
 USING(company_id)
  JOIN funding
 USING(company_id)
 WHERE EXTRACT(year FROM date_joined) IN (2019, 2020, 2021)
		AND industry IN (
			SELECT industry
			  FROM industries
			  JOIN dates
			 USING(company_id)
			 WHERE EXTRACT(year FROM date_joined) IN (2019, 2020, 2021)
			 GROUP BY industry
			 ORDER BY COUNT(*) DESC
			 LIMIT 3)
 GROUP BY industry, year
 ORDER BY industry, year DESC;