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 companies

DataFrameas

df

variable

-- Find the top industries by valuation
SELECT industries.industry, ROUND(AVG(funding.valuation)/1000000000, 2) as avg_valuation_billions
FROM funding
JOIN industries ON funding.company_id = industries.company_id
GROUP BY industries.industry
ORDER BY avg_valuation_billions DESC
LIMIT 5;

DataFrameas

df

variable

-- Find the top industries by valuation
SELECT industries.industry, ROUND(AVG(funding.valuation)/1000000000, 2) as avg_valuation_billions
FROM funding
JOIN industries ON funding.company_id = industries.company_id
GROUP BY industries.industry
ORDER BY avg_valuation_billions DESC
LIMIT 3;

DataFrameas

df

variable

-- Find the rate at which new high-value companies are emerging
WITH new_unicorns AS (
SELECT
DATE_TRUNC('year', dates.date_joined) AS year,
COUNT(*) as num_unicorns
FROM dates
JOIN funding ON dates.company_id = funding.company_id
WHERE funding.valuation >= 1000000000
GROUP BY year
ORDER BY year
)
SELECT year, num_unicorns, ROUND((num_unicorns/(SELECT SUM(num_unicorns) FROM new_unicorns))*100, 2) as percent_of_total
FROM new_unicorns;

To find the three best-performing industries based on the number of new unicorns created over the last three years (2019, 2020, and 2021) combined, and return the industry, the year, the number of companies in these industries that became unicorns each year in 2019, 2020, and 2021, along with the average valuation per industry per year, without using the WITH function, you can use a query like the following:

DataFrameas

df

variable

SELECT industry, COUNT(*) as num_unicorns
FROM industries
JOIN dates ON industries.company_id = dates.company_id
WHERE year_founded >= 2019
GROUP BY industry
ORDER BY num_unicorns DESC
LIMIT 3

DataFrameas

df

variable

SELECT 
    industry, 
    year_founded as year, 
    COUNT(DISTINCT industries.company_id) as num_companies,
    ROUND(AVG(valuation)/1000000000, 2) as avg_valuation_billions
FROM 
    industries 
JOIN 
    dates ON industries.company_id = dates.company_id
JOIN
    funding ON industries.company_id = funding.company_id
WHERE 
    year_founded >= 2019 AND
    industry IN (SELECT industry FROM (SELECT industry, COUNT(*) as num_unicorns
                                    FROM industries
                                    JOIN dates ON industries.company_id = dates.company_id
                                    WHERE year_founded >= 2019
                                    GROUP BY industry
                                    ORDER BY num_unicorns DESC
                                    LIMIT 3) as top_industries)
GROUP BY 
    industry, year
ORDER BY 
    industry DESC, year DESC;

DataFrameas

df

variable

WITH Top_3 AS (
SELECT
industry,
DATE_TRUNC('year',date_joined) as year,
COUNT(DISTINCT industries.company_id) as num_companies,
ROUND(AVG(valuation)/1000000000, 2) as avg_valuation_in_billions
FROM
industries
JOIN
dates ON industries.company_id = dates.company_id
JOIN
funding ON industries.company_id = funding.company_id
WHERE
date_joined >= '2019-01-01'::date AND
industry IN (SELECT industry FROM (SELECT industry, COUNT(*) as num_unicorns
FROM industries
JOIN dates ON industries.company_id = dates.company_id
WHERE date_joined >= '2019-01-01'::date
GROUP BY industry
ORDER BY num_unicorns DESC
LIMIT 3) as top_industries)
GROUP BY
industry, year
ORDER BY
industry DESC, year DESC
)
SELECT
CASE
WHEN industry = 'Internet software & services' THEN 'industry1'
WHEN industry = 'Fintech' THEN 'industry2'
WHEN industry = 'E-commerce & direct-to-consumer' THEN 'industry3'
ELSE industry
END as industry,
year,
num_companies,
avg_valuation_in_billions
FROM
Top_3;

DataFrameas

df

variable

SELECT
CASE
WHEN industry = 'Internet software & services' THEN 'industry1'
WHEN industry = 'Fintech' THEN 'industry2'
WHEN industry = 'E-commerce & direct-to-consumer' THEN 'industry3'
ELSE industry
END as industry, 
    year_founded as year, 
    COUNT(DISTINCT industries.company_id) as num_unicorns,
    ROUND(AVG(valuation), 2) as avg_valuation_billions
FROM 
    industries 
JOIN 
    dates ON industries.company_id = dates.company_id
JOIN
    funding ON industries.company_id = funding.company_id
WHERE 
    year_founded >= 2019 AND
    industry IN (SELECT industry FROM (SELECT industry, COUNT(*) as num_unicorns
                                    FROM industries
                                    JOIN dates ON industries.company_id = dates.company_id
                                    WHERE year_founded >= 2019
                                    GROUP BY industry
                                    ORDER BY num_unicorns DESC
                                    LIMIT 3) as top_industries)
GROUP BY 
    industry, year
ORDER BY 
    industry , year DESC;

DataFrameas

df

variable

WITH top_industries AS
(
    SELECT i.industry, 
        COUNT(i.*)
    FROM industries AS i
    INNER JOIN dates AS d
        ON i.company_id = d.company_id
    WHERE EXTRACT(year FROM d.date_joined) in ('2019', '2020', '2021')
    GROUP BY industry
    ORDER BY count DESC
    LIMIT 3
),

yearly_rankings AS 
(
    SELECT COUNT(i.*) AS num_unicorns,
        i.industry,
        EXTRACT(year FROM d.date_joined) AS year,
        AVG(f.valuation) AS average_valuation
    FROM industries AS i
    INNER JOIN dates AS d
        ON i.company_id = d.company_id
    INNER JOIN funding AS f
        ON d.company_id = f.company_id
    GROUP BY industry, year
)

SELECT industry,
    year,
    num_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, num_unicorns, year, average_valuation
ORDER BY industry, year DESC