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.

It's important to first find out the Top 3 industries based on the number of unicorn. I first did this by counting the number of companies in the dates table for the 3 years we are asked to find (2019,2020,2021)

DataFrameas

df

variable

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

It shows that Fintech, Internet software & services and E-commerce & direct-to-consumer are the top 3 industries based on the number of unicorns created in 2019-2021

The two approaches I used to get the desired result are shown below.

DataFrameas

df

variable

WITH model_dataset AS
(SELECT company_id, 
company, 
industry, 
EXTRACT(year FROM date_joined) AS year,
valuation
FROM dates
JOIN industries USING (company_id)
JOIN funding USING (company_id)
JOIN companies USING (company_id)
 WHERE EXTRACT(year FROM date_joined) IN (2019,2020,2021)
ORDER BY company_id
),
top_industries AS 
(SELECT industry, COUNT(company) num_unicorns
FROM dates
INNER JOIN industries USING (company_id)
INNER JOIN companies USING (company_id)
WHERE EXTRACT(year FROM date_joined) IN (2019,2020,2021)
GROUP BY industry
ORDER BY num_unicorns DESC
LIMIT 3)
 SELECT industry, 
 		year, 
 		COUNT(company_id) AS num_unicorns,
 		ROUND(AVG(valuation)/1000000000, 2) AS average_valuation_billions
    FROM model_dataset
    WHERE industry IN (SELECT industry FROM top_industries)
    GROUP BY industry, year
    ORDER BY industry, year DESC

DataFrameas

df

variable

WITH top_industries AS
(SELECT industry, 
 COUNT(company) num_unicorns
FROM dates
INNER JOIN industries USING (company_id)
INNER JOIN companies 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 industry,
 EXTRACT(year FROM date_joined) AS year,
 COUNT(company_id) AS num_unicorns,
 AVG(valuation) AS average_valuation
 FROM dates
 JOIN industries USING (company_id)
 JOIN funding USING (company_id)
 GROUP BY year, industry
)
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)
    AND year IN (2019, 2020,2021)
    GROUP BY industry, year, num_unicorns
    ORDER BY industry, year DESC