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.

First I need to find out what the top 3 industries are.

DataFrameas

df1

variable

SELECT 
    i.industry,
    COUNT(i.company_id) AS count_new_unicorns
FROM industries i
INNER JOIN dates as d 
USING(company_id)
WHERE EXTRACT(YEAR FROM d.date_joined) BETWEEN 2019 AND 2021
GROUP BY i.industry
ORDER BY count_new_unicorns DESC
LIMIT 3;

Now that I have an idea for what to look for in my results I can start writing the querty to identify the three best-performing industries based on the number of new unicorns created over the last three years (2019, 2020, and 2021) combined.

DataFrameas

df2

variable

WITH top_industries AS ( 
SELECT 
 i.industry,
 COUNT(i.company_id) AS count_new_unicorns
FROM industries i
INNER JOIN dates as d 
USING(company_id)
WHERE EXTRACT(YEAR FROM d.date_joined) BETWEEN 2019 AND 2021
GROUP BY i.industry
ORDER BY count_new_unicorns DESC
LIMIT 3
)
, yearly_rankings AS
( SELECT i.industry, EXTRACT(YEAR FROM d.date_joined) AS year, COUNT(company_id) AS num_unicorns, 
 AVG(f.valuation)  AS valuation_avg
FROM industries AS i
INNER JOIN dates AS d
USING (company_id)
INNER JOIN funding AS f
USING (company_id)
WHERE EXTRACT(YEAR FROM d.date_joined) BETWEEN 2019 AND 2021
GROUP BY i.industry, EXTRACT(YEAR FROM d.date_joined)
)

SELECT industry, year, num_unicorns, ROUND(AVG(valuation_avg / 1000000000),2) AS average_valuation_billions
FROM yearly_rankings 
INNER JOIN top_industries 
USING (industry)
GROUP BY yearly_rankings.industry, yearly_rankings.year, yearly_rankings.num_unicorns
ORDER BY industry ,year desc