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

SELECT 
	industries.industry,
    EXTRACT('year' from dates.date_joined) as year,
    COUNT(dates.company_id) as num_unicorns,
    ROUND(AVG(funding.valuation)/1e9,2) as average_valuation_billions
FROM
	dates
	INNER JOIN funding ON dates.company_id = funding.company_id
	INNER JOIN industries ON dates.company_id = industries.company_id

WHERE 
	EXTRACT('year' from dates.date_joined) IN (2021,2020,2019)

GROUP BY 
	industry,
    year

ORDER BY 
	industry,
    year DESC

DataFrameas

df

variable

WITH top_industries as (SELECT
    industries.industry,
    EXTRACT('year' from dates.date_joined) as year,
    COUNT(dates.company_id) as num_unicorns
FROM 
    industries
INNER JOIN dates ON industries.company_id = dates.company_id
WHERE EXTRACT('year' from dates.date_joined) in ('2019','2020','2021')
GROUP BY 
industry,
ORDER BY count DESC
LIMIT 3
),

yearly_rankings as (
SELECT 
    COUNT(industries.company_id) as num_unicorns,
    EXTRACT('year' from dates.date_joined) as year,
    ROUND(AVG(funding.valuation)/1e9,2) as average_valuation_billions
FROM 
    industries
    INNER join dates on industries.company_id = dates.company_id
    INNER join funding on industries.company_id = funding.company_id
    
GROUP BY 
    year,
    industries.industry
)

SELECT 
	top_industries.industry as industry,
    top_industries.year as year,
    yearly_rankings.num_unicorns as num_unicorns,
    yearly_rankings.average_valuation_billions as average_valuation_billions
FROM 
	top_industries,yearly_rankings
WHERE top_industries.year in (2019,2020,2021)
ORDER BY industry,top_industries.year DESC

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, average_valuation
ORDER BY industry, year DESC