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 *
FROM industries;

DataFrameas

df

variable

SELECT industry, EXTRACT('year' FROM date_joined) AS year, COUNT(company_id) AS num_unicorns
FROM industries
INNER JOIN dates
USING(company_id)
WHERE EXTRACT('year' FROM date_joined) IN ('2019', '2020', '2021')
GROUP BY industry, year
ORDER BY num_unicorns DESC, industry DESC, year DESC;

DataFrameas

df

variable

SELECT EXTRACT('year' FROM date_joined) AS year, industry, COUNT(company_id) AS num_unicorns, AVG(valuation) AS avg_valuation
FROM industries
INNER JOIN funding
USING(company_id)
INNER JOIN dates
USING (company_id)
WHERE EXTRACT('year' FROM date_joined) IN ('2019', '2020', '2021')
GROUP BY year, industry;

DataFrameas

df

variable

WITH top_industries AS
(SELECT industry, COUNT(company_id) AS num_unicorns
FROM industries
INNER JOIN dates
USING(company_id)
WHERE EXTRACT('year' FROM date_joined) IN ('2019', '2020', '2021')
GROUP BY industry
ORDER BY num_unicorns DESC
LIMIT 3),

yearly_rankings AS
(SELECT EXTRACT('year' FROM date_joined) AS year, industry, COUNT(company_id) AS num_unicorns, AVG(valuation) AS avg_valuation
FROM industries
INNER JOIN funding
USING(company_id)
INNER JOIN dates
USING (company_id)
WHERE EXTRACT('year' FROM date_joined) IN ('2019', '2020', '2021')
GROUP BY year, industry)

SELECT industry, year, num_unicorns, ROUND(AVG(avg_valuation / 1000000000), 2) AS average_valuation_billions
FROM yearly_rankings
WHERE year IN (2019, 2020, 2021) AND industry IN 
    (SELECT industry
     FROM top_industries
     ORDER BY num_unicorns DESC)
GROUP BY industry, year, num_unicorns
ORDER BY industry, year DESC, num_unicorns DESC;

DataFrameas

df1

variable

SELECT industry, COUNT(company_id) AS num_unicorns
FROM industries
INNER JOIN dates
USING(company_id)
WHERE EXTRACT('year' FROM date_joined) IN ('2019', '2020', '2021')
GROUP BY industry
ORDER BY num_unicorns DESC
LIMIT 3;

DataFrameas

df2

variable

SELECT EXTRACT('year' FROM date_joined) AS year, industry, COUNT(company_id) AS num_unicorns, AVG(valuation) AS avg_valuation
FROM industries
INNER JOIN funding
USING(company_id)
INNER JOIN dates
USING (company_id)
WHERE EXTRACT('year' FROM date_joined) IN ('2019', '2020', '2021')
GROUP BY year, industry;