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I 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.

unicorns database contains the following tables:

dates

ColumnDescription
company_idA unique ID for the company.
date_joinedThe date that the company became a unicorn.
year_foundedThe year that the company was founded.

funding

ColumnDescription
company_idA unique ID for the company.
valuationCompany value in US dollars.
fundingThe amount of funding raised in US dollars.
select_investorsA list of key investors in the company.

industries

ColumnDescription
company_idA unique ID for the company.
industryThe industry that the company operates in.

companies

ColumnDescription
company_idA unique ID for the company.
companyThe name of the company.
cityThe city where the company is headquartered.
countryThe country where the company is headquartered.
continentThe continent where the company is headquartered.
Spinner
DataFrameas
df
variable
-- My task is to first identify the three best-performing industries based on the number of new unicorns created in 2019, 2020, and 2021 combined.

SELECT
	industry,
	COUNT(*) AS num_unicorns
FROM industries i
JOIN dates d
	ON i.company_id = d.company_id
WHERE EXTRACT(year FROM date_joined) IN (2019, 2020, 2021)
GROUP BY industry
ORDER BY num_unicorns DESC
LIMIT 3;
Spinner
DataFrameas
df1
variable
-- Yearly rankings

SELECT 
	industry,
	EXTRACT(year FROM date_joined) AS year,
	COUNT(*) AS num_unicorns,
	AVG(valuation) AS average_valuation
FROM industries i
JOIN dates d
	ON i.company_id = d.company_id
JOIN funding f
	ON i.company_id = f.company_id
GROUP BY industry, year
ORDER BY year ASC, average_valuation DESC;
Spinner
DataFrameas
df
variable
-- Best performing industries with the number of unicorns within these industries, the year that they became a unicorn, and their average valuation (converted to billions of dollars)

WITH top_industries AS (
	SELECT
		industry,
		COUNT(*) AS num_unicorns
	FROM industries i
	JOIN dates d
		ON i.company_id = d.company_id
	WHERE EXTRACT(year FROM date_joined) IN (2019, 2020, 2021)
	GROUP BY industry
	ORDER BY num_unicorns DESC
	LIMIT 3 ),

rankings AS (
	SELECT 
		industry,
		EXTRACT(year FROM date_joined) AS year,
		COUNT(*) AS num_unicorns,
		AVG(valuation) AS average_valuation
	FROM industries i
	JOIN dates d
		ON i.company_id = d.company_id
	JOIN funding f
		ON i.company_id = f.company_id
	GROUP BY industry, year )

SELECT
	industry,
	year,
	num_unicorns,
	ROUND(AVG(average_valuation/1000000000),2) AS average_valuation_billions
FROM rankings
WHERE year in (2019, 2020, 2021)
	AND industry IN (SELECT industry FROM top_industries)
GROUP BY industry, num_unicorns, year
ORDER BY year DESC, num_unicorns DESC