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 
 i.industry,
 EXTRACT(year FROM d.date_joined) AS year,
 COUNT(*) AS num_unicorns,
 ROUND(AVG(f.valuation)/1000000000,2) AS average_valuation_billions
 FROM industries AS i
 INNER JOIN dates AS d ON i.company_id = d.company_id
 INNER JOIN funding AS f ON i.company_id = f.company_id
 WHERE EXTRACT(year FROM d.date_joined) BETWEEN 2019 AND 2021
 AND i.industry IN (
	 SELECT top_industries.industry FROM(
 SELECT i.industry,
COUNT(CASE WHEN EXTRACT(year FROM d.date_joined) = 2019 THEN 2019
 WHEN EXTRACT(year FROM d.date_joined) = 2020 THEN 2020
 WHEN EXTRACT(year FROM d.date_joined) = 2021 THEN 2021
 END) AS num_per_ind
 FROM industries as i
 INNER join dates as d ON i.company_id = d.company_id
 GROUP BY industry
 ORDER BY num_per_ind DESC
 LIMIT 3
 ) AS top_industries)
 GROUP BY industry, year
 ORDER BY industry, 2 DESC
 LIMIT 9

DataFrameas

df1

variable

SELECT
    i.industry,
    EXTRACT(YEAR FROM d.date_joined) AS year,
    COUNT(*) AS num_unicorns,
    ROUND(AVG(f.valuation) / 1000000000) AS average_valuation_billions
FROM
    industries AS i
JOIN
    dates AS d
ON
    i.company_id = d.company_id
JOIN
    funding AS f
ON
    i.company_id = f.company_id
WHERE
    EXTRACT(YEAR FROM d.date_joined) BETWEEN 2019 AND 2021
GROUP BY
    i.industry, EXTRACT(YEAR FROM d.date_joined)
HAVING
    COUNT(*) >= 1
ORDER BY
    num_unicorns DESC