Skip to content

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

industryyearnum_unicornsaverage_valuation_billions
Fintech20211382.75
Internet software & services20211192.15
E-commerce & direct-to-consumer2021472.47
Internet software & services2020204.35
E-commerce & direct-to-consumer2020164.00
Fintech2020154.33
Fintech2019206.80
Internet software & services2019134.23
E-commerce & direct-to-consumer2019122.58
Spinner
DataFrameas
df
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