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.

Obtain a list of unicorns
Count the number of unicorns
Group the number of unicorns by industry
Selelct the top three performing industries based on the max number of unicorns.

DataFrameas

df

variable

WITH top_industries AS (
SELECT   COUNT(*),
            industry
  FROM    industries
  JOIN    companies USING(company_id)
  JOIN    dates USING(company_id)
  JOIN    funding USING(company_id)
 WHERE    EXTRACT(year FROM date_joined) IN ('2019', '2020', '2021')
GROUP BY  industry
ORDER BY  COUNT DESC
LIMIT     3
),
yearly_rankings AS (
SELECT    COUNT(*) AS new_unicorns, 
          industry,
          EXTRACT(year FROM date_joined) AS year,
          AVG(valuation) AS average_valuation
          --  funding
  FROM    industries
INNER JOIN    dates USING(company_id)
INNER JOIN    funding USING(company_id)
GROUP BY  industry, year
)

SELECT   industry, 
         year,
         new_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, new_unicorns, year, average_valuation
ORDER BY industry, year DESC         
;