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

df

variable

SELECT * FROM companies

DataFrameas

df1

variable

SELECT * FROM industries

DataFrameas

df2

variable

SELECT * from funding

DataFrameas

df3

variable

SELECT * FROM dates

DataFrameas

df4

variable

SELECT
FROM companies
LEFT JOIN dates
USING(company_id)
LEFT JOIN industries
USING(company_id)

DataFrameas

df5

variable

SELECT industry, year_founded AS year, COUNT(*) AS num_unicorns
FROM companies
LEFT JOIN public.dates
USING(company_id)
LEFT JOIN public.industries
USING(company_id)
WHERE year_founded IN (2019, 2020, 2021)
GROUP BY industry, year
ORDER BY year DESC, num_unicorns DESC;

DataFrameas

df6

variable

SELECT industry, COUNT(*) AS num_unicorns
FROM companies
LEFT JOIN public.industries
USING(company_id)
LEFT JOIN public.dates
USING(company_id)
WHERE year_founded IN (2019, 2020, 2021)
GROUP BY industry
ORDER BY num_unicorns DESC
LIMIT 3;

DataFrameas

df8

variable

WITH top_industries AS 
(
		SELECT -- select the top 3 industry by its num_unicorns
			i.industry, COUNT(i.*)
		FROM public.industries AS i
		LEFT JOIN public.dates AS d
		USING(company_id)
		WHERE year_founded IN (2019, 2020, 2021)
		GROUP BY industry
		ORDER BY COUNT(*) DESC
		LIMIT 3
), 

yearly_rankings AS
( 
	SELECT COUNT(i.*) AS num_unicorns, 
		i.industry, 
		EXTRACT(year FROM d.date_joined) AS year,
		AVG(f.valuation) AS average_valuation
	FROM industries AS i 
	INNER JOIN dates AS d 
		USING(company_id)
	INNER JOIN funding AS f 
		USING(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 IN (2019, 2020, 2021) AND
	industry IN (SELECT industry FROM top_industries) -- CTE Common Table Expressions
GROUP BY industry, num_unicorns, year
ORDER BY 
	industry, 
	year DESC;