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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
WITH top_industries AS (
SELECT
i.industry,
SUM(1) as num_unicorns,
DENSE_RANK() OVER (ORDER BY SUM(1) DESC) as rank
FROM industries i
LEFT JOIN dates d
ON i.company_id = d.company_id
WHERE EXTRACT('Year' FROM d.date_joined) IN (2019, 2020, 2021)
GROUP BY i.industry
)
SELECT
i.industry,
EXTRACT('Year' FROM d.date_joined) as year,
SUM(1) as num_unicorns,
ROUND(AVG(f.valuation) / 1000000000, 2) as average_valuation_billions
FROM companies c
LEFT JOIN dates d
ON c.company_id = d.company_id
LEFT JOIN industries i
ON c.company_id = i.company_id
INNER JOIN top_industries ti
ON ti.industry = i.industry
AND ti.rank <= 3
LEFT JOIN funding f
ON c.company_id = f.company_id
WHERE EXTRACT('Year' FROM d.date_joined) IN (2019, 2020, 2021)
GROUP BY
i.industry,
ti.rank,
EXTRACT('Year' FROM d.date_joined)
ORDER BY i.industry, year desc