Project - Analyzing Unicorn Companies

DataFrameas

df

variable

SELECT * FROM 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

df4

variable

SELECT * 
FROM funding
LIMIT 5;

DataFrameas

df5

variable

SELECT * FROM industries
LIMIT 5;

DataFrameas

df6

variable

SELECT * FROM companies
LIMIT 5;

DataFrameas

df2

variable

SELECT 
i.industry, 
EXTRACT(YEAR FROM d.date_joined) AS year,
COUNT(c.company_id) AS num_unicorns, 
ROUND(AVG(f.valuation)/1000000000, 2) AS average_valuation_billions
FROM companies as c

INNER JOIN industries AS i USING(company_id)
	INNER JOIN dates AS d USING(company_id)
	INNER JOIN funding AS f USING(company_id)
WHERE EXTRACT(YEAR FROM d.date_joined) IN (2019,2020,2021)
GROUP BY i.industry, year

DataFrameas

df3

variable

SELECT 
i.industry, 
EXTRACT(YEAR FROM d.date_joined) AS year,
COUNT(c.company_id) AS num_unicorns, 
ROUND(AVG(f.valuation)/1000000000, 2) AS average_valuation_billions
FROM companies as c

INNER JOIN industries AS i USING(company_id)
	INNER JOIN dates AS d USING(company_id)
	INNER JOIN funding AS f USING(company_id)
WHERE EXTRACT(YEAR FROM d.date_joined) IN (2019,2020,2021)
AND i.industry IN
			(SELECT i.industry
			 FROM companies AS c
			INNER JOIN industries i ON c.company_id = i.company_id
			INNER JOIN dates d ON c.company_id = d.company_id
			WHERE EXTRACT(YEAR FROM d.date_joined) BETWEEN 2019 AND 2021
			GROUP BY i.industry, 
			EXTRACT(YEAR FROM d.date_joined)
			ORDER BY COUNT(c.company_id) DESC
			LIMIT 3)
GROUP BY 
	i.industry, EXTRACT(YEAR FROM d.date_joined);

DataFrameas

df1

variable

SELECT 
i.industry, 
EXTRACT(YEAR FROM d.date_joined) AS year,
COUNT(c.company_id) AS num_unicorns, 
ROUND(AVG(f.valuation)/1000000000, 2) AS average_valuation_billions
FROM companies as c

INNER JOIN industries AS i USING(company_id)
	INNER JOIN dates AS d USING(company_id)
	INNER JOIN funding AS f USING(company_id)
WHERE EXTRACT(YEAR FROM d.date_joined) IN (2019,2020,2021)

AND i.industry IN
			(SELECT i.industry
			 FROM companies AS c
			INNER JOIN industries i ON c.company_id = i.company_id
			INNER JOIN dates d ON c.company_id = d.company_id
			WHERE EXTRACT(YEAR FROM d.date_joined) BETWEEN 2019 AND 2021
			GROUP BY i.industry, 
			EXTRACT(YEAR FROM d.date_joined)
			ORDER BY COUNT(c.company_id) DESC
			LIMIT 3)
GROUP BY 
	i.industry,
	EXTRACT(YEAR FROM d.date_joined)
ORDER BY
	i.industry,
	year DESC;