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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. |
Unknown integration
DataFrameavailable as
df1
variable
WITH top_industries AS (SELECT industry, COUNT(company_id)
FROM companies AS c
LEFT JOIN dates AS d
USING(company_id)
LEFT JOIN funding AS f
USING(company_id)
LEFT JOIN industries AS i
USING(company_id)
WHERE (year_founded IN (2019, 2020, 2021)) AND (valuation >= 1000000000)
GROUP BY industry
ORDER BY 2 DESC
LIMIT 3),
yearly_rankings AS (SELECT industry, date_part('year', date_joined) AS year, COUNT(company_id) AS num_unicorns, AVG(valuation) AS average_valuation
FROM companies AS c
LEFT JOIN dates AS d
USING(company_id)
LEFT JOIN funding AS f
USING(company_id)
LEFT JOIN industries AS i
USING(company_id)
WHERE (date_part('year', date_joined) IN (2019, 2020, 2021)) AND (valuation >= 1000000000)
GROUP BY year, industry)
SELECT industry, year, num_unicorns, ROUND(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)
ORDER BY industry, year DESC;