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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
DataFrameas
df
variable
WITH top_performing_industries AS (
	SELECT 
		sub.industry,
		sub.year
	FROM (
			SELECT
				i.industry,
				EXTRACT(YEAR FROM d.date_joined) AS year,
				COUNT(DISTINCT c.company) AS num_unicorns,
				RANK() OVER(ORDER BY COUNT(c.company_id) DESC) AS rank_industry
				FROM companies AS c 
					LEFT JOIN industries AS i 
						ON c.company_id = i.company_id
					LEFT JOIN dates AS d
						ON d.company_id = c.company_id
		WHERE EXTRACT(YEAR FROM d.date_joined) IN ('2019', '2020', '2021')
		GROUP BY i.industry, year
	) AS sub
	WHERE rank_industry < 4
), 
yearly_ranking_data AS(
	SELECT 
		i.industry, 
		EXTRACT(YEAR FROM d.date_joined) AS year, 
		COUNT(DISTINCT c.company) AS num_unicorns, 
		ROUND(AVG(f.valuation), 2) AS average_valuation_billions
	FROM companies AS c
		LEFT JOIN industries AS i
			ON c.company_id = i.company_id
		LEFT JOIN dates AS d 
			ON i.company_id = d.company_id
		LEFT JOIN funding AS f
			ON c.company_id = f.company_id
	WHERE EXTRACT(YEAR FROM d.date_joined) IN ('2019', '2020', '2021')
	GROUP BY i.industry, year
)
SELECT t.industry, y.year, y.num_unicorns, ROUND(average_valuation_billions/1000000000, 2) AS average_valuation_billions
FROM top_performing_industries AS t
	LEFT JOIN yearly_ranking_data y
		ON t.industry = y.industry
ORDER BY y.year DESC, y.num_unicorns DESC
Spinner
DataFrameas
df1
variable
WITH top_industries AS
(
    SELECT i.industry, 
        COUNT(i.*)
    FROM industries AS i
    INNER JOIN dates AS d
        ON i.company_id = d.company_id
    WHERE EXTRACT(year FROM d.date_joined) 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
        ON i.company_id = d.company_id
    INNER JOIN funding AS f
        ON d.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 yearly_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
Spinner
DataFrameas
df2
variable
    SELECT i.industry,
		EXTRACT(year FROM d.date_joined) AS year,
        COUNT(i.*)
    FROM industries AS i
    INNER JOIN dates AS d
        ON i.company_id = d.company_id
    WHERE EXTRACT(year FROM d.date_joined) in ('2019', '2020', '2021')
    GROUP BY industry, year
    ORDER BY year DESC, COUNT(i.*) DESC
Spinner
DataFrameas
df3
variable
SELECT i.industry, EXTRACT(YEAR FROM date_joined) AS year, COUNT(i.company_id)
FROM dates d
	JOIN industries i ON d.company_id = i.company_id
WHERE date_joined IS NOT NULL 
	AND EXTRACT(YEAR FROM date_joined) IN ('2019')
GROUP BY i.industry, year
ORDER BY COUNT DESC