Project - Analyzing Unicorn 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

df

variable

-- with top_performers as
-- 	(select industry, count(*)
-- 	from public.industries
-- 	left JOIN dates on public.industries.company_id = 	  	 public.dates.company_id
--      where year_founded > 2018
-- 	 group by industry
-- 	 order by count(*) desc
-- 	 limit 3)

-- -- select * from top_performers
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_profit 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
)

select industries.industry, year_founded as "year", count(industries.*) as num_unicorns, round(avg(valuation/100000000),2) as average_valuation_billions
from industries
left JOIN dates on public.industries.company_id = public.dates.company_id
inner JOIN funding on industries.company_id = funding.company_id
WHERE year_founded in ('2019', '2020', '2021')
    AND industry in (SELECT industry
                    FROM top_industries)
group by industries.industry, year_founded
order by industry,year_founded desc



-- 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, average_valuation
-- ORDER BY industry, year DESC