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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
df
variable
SELECT ind.industry,
dt.year_founded AS year,
COUNT(com.company) AS num_unicorns,
ROUND(AVG(fun.valuation), 2) / 1000000000 AS average_valuation_billions
FROM industries AS ind
INNER JOIN companies AS com
USING(company_id)
INNER JOIN funding AS fun
USING(company_id)
INNER JOIN dates AS dt
USING(company_id)
WHERE dt.year_founded IN (2019,2020,2021)
GROUP BY year, industry
ORDER BY industry DESC, year DESC;
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