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 valuation FROM funding

DataFrameas

df

variable

WITH unicorns AS (
SELECT
	i.industry
	,DATE_PART('Year', d.date_joined) AS year
	,COUNT(*)AS num_unicorns
	,ROUND(AVG(f.valuation),2) AS average_valuation_billions
FROM industries AS i
JOIN dates AS d
	ON d.company_id = i.company_id
JOIN funding AS f
	ON f.company_id = i.company_id
GROUP BY DATE_PART('Year', d.date_joined), i.industry
HAVING DATE_PART('Year', d.date_joined) IN (2019,2020,2021)
)
SELECT 	
	industry
	,year
	,num_unicorns
	,RANK() OVER(PARTITION BY industry ORDER BY average_valuation_billions) AS 	average_valuation_billions
	FROM unicorns;

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
	i.industry
    ,COUNT(d.*) AS num_unicorns
	,DATE_PART('Year', d.date_joined) AS year
	,AVG(f.valuation) AS average_valuation
FROM industries AS i
JOIN dates AS d
        ON i.company_id = d.company_id
JOIN funding AS f
	ON d.company_id = f.company_id
WHERE EXTRACT(year FROM d.date_joined) in ('2019', '2020', '2021')
GROUP BY i.industry,DATE_PART('Year', d.date_joined)
)

SELECT industry,
    year,
    num_unicorns,
    ROUND(AVG(average_valuation / 1000000000), 2) AS average_valuation_billions
FROM yearly_rankings
WHERE  industry in (SELECT industry
                    FROM top_industries)
GROUP BY industry, num_unicorns, year, average_valuation
ORDER BY industry, year DESC

DataFrameas

df

variable

SELECT
	i.industry
    ,COUNT(d.*)
	,DATE_PART('Year', d.date_joined) AS year
	,AVG(f.valuation) AS average_valuation_billions
FROM industries AS i
JOIN dates AS d
        ON i.company_id = d.company_id
JOIN funding AS f
	ON d.company_id = f.company_id
WHERE EXTRACT(year FROM d.date_joined) in ('2019', '2020', '2021')
GROUP BY i.industry,DATE_PART('Year', d.date_joined);