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

WITH top_industries AS 
	(SELECT industry, COUNT(*) AS total_unicorns
	FROM dates AS d
	JOIN industries AS i
	ON d.company_id = i.company_id
	WHERE  EXTRACT(YEAR FROM date_joined) BETWEEN 2019 AND 2021
	GROUP BY industry
	ORDER BY COUNT(*) DESC
	LIMIT 3)
SELECT industry, EXTRACT(YEAR FROM date_joined), COUNT(*) AS num_unicorns,
ROUND(AVG(valuation)/1000000000,2) AS average_valuation_billions
FROM dates AS d
JOIN industries AS i
ON d.company_id = i.company_id
JOIN top_industries
USING(industry)
JOIN funding AS f
ON d.company_id = f.company_id
WHERE EXTRACT(YEAR FROM date_joined) BETWEEN 2019 AND 2021
GROUP BY industry, EXTRACT(YEAR FROM date_joined), total_unicorns
ORDER BY industry, EXTRACT(YEAR FROM date_joined) DESC

DataFrameas

df

variable

WITH top_industries AS 
(   SELECT industry, COUNT(*) AS count
	FROM dates AS d
	JOIN industries AS i
	ON d.company_id = i.company_id
	WHERE  EXTRACT(YEAR FROM date_joined) BETWEEN 2019 AND 2021
	GROUP BY industry
	ORDER BY count DESC
	LIMIT 3
),
yearly_rankings AS
(
	SELECT COUNT(*) AS num_unicorns,
		i.industry,
		EXTRACT(YEAR FROM date_joined) AS year,
		AVG(valuation) AS average_valuation
	FROM funding AS f
	JOIN dates AS d
		ON f.company_id = d.company_id
	JOIN industries AS i
	 	ON i.company_id = d.company_id
	GROUP by industry, year
)
SELECT industry, year, num_unicorns, ROUND(AVG(average_valuation / 1000000000), 2) AS average_valuation_billions

FROM yearly_rankings
WHERE year BETWEEN 2019 AND 2021
	AND industry in (SELECT industry FROM top_industries)
GROUP BY industry, num_unicorns, year, average_valuation
ORDER BY industry, year DESC