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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
SELECT i.industry,
EXTRACT (YEAR FROM d.date_joined) AS year,
COUNT(d.company_id) AS num_unicorns,
ROUND(AVG(f.valuation / 1000000000),2) AS average_valuation_billions
FROM industries AS i
LEFT JOIN companies AS c
ON i.company_id = c.company_id
LEFT JOIN dates AS d
ON c.company_id = d.company_id
LEFT JOIN funding as f
ON c.company_id = f.company_id
WHERE i.industry IN (
SELECT i.industry
FROM industries AS i
LEFT JOIN companies AS c
ON i.company_id = c.company_id
LEFT JOIN dates AS d
ON c.company_id = d.company_id
LEFT JOIN funding as f
ON c.company_id = f.company_id
WHERE EXTRACT (YEAR FROM d.date_joined) IN ('2019', '2020', '2021')
GROUP BY i.industry
ORDER BY COUNT(d.company_id) DESC
LIMIT 3 )
AND EXTRACT (YEAR FROM d.date_joined) IN ('2019', '2020', '2021')
GROUP BY i.industry,
EXTRACT (YEAR FROM d.date_joined)
ORDER BY i.industry, year DESC