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Project: Analyzing Unicorn Companies

Did you know that the average return from investing in stocks is 10% per year (not accounting for inflation)? 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

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.

The output

Your query should return a table in the following format:

industryyearnum_unicornsaverage_valuation_billions
industry12021------
industry22020------
industry32019------
industry12021------
industry22020------
industry32019------
industry12021------
industry22020------
industry32019------

Where industry1, industry2, and industry3 are the three top-performing industries.

Spinner
DataFrameavailable as
df
variable
with cte1 AS (
SELECT
	industry,
	COUNT(*) AS num_new_unicorns
FROM
	industries 
JOIN dates 
	USING(company_id)
WHERE EXTRACT(YEAR FROM date_joined) in (2019, 2020, 2021)
GROUP BY 
	industry
ORDER BY 
	num_new_unicorns DESC
LIMIT 3
),

cte2 AS (
SELECT 
	industries.company_id,
	industries.industry,
	EXTRACT(YEAR FROM date_joined) AS year_joined,
	valuation
FROM
	industries 
JOIN dates ON industries.company_id = dates.company_id
JOIN funding ON industries.company_id = funding.company_id
WHERE industry IN (SELECT industry FROM cte1)
)

SELECT 
	industry, 
	year_joined AS year,
	COUNT(*) AS num_unicorns, 
	ROUND(AVG(valuation/1000000000), 2) AS average_valuation_billions
FROM cte2
WHERE year_joined IN (2019, 2020, 2021)
GROUP BY 
	industry, year_joined 
ORDER BY 
	year DESC, num_unicorns DESC;