Skip to content
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 AS industry,COUNT(i.*) AS num_unicorns
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 num_unicorns DESC
LIMIT 3
),
yearly_rankings AS(
SELECT i.industry AS industry,
EXTRACT(year from d.date_joined) AS year,
COUNT(i.*) AS num_unicorns,
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
WHERE EXTRACT(year from d.date_joined) in (2019,2020,2021)
GROUP BY industry,year
)
SELECT yr.industry,
yr.year,
yr.num_unicorns,
ROUND(yr.average_valuation/1000000000,2) AS average_valuation_billions
FROM yearly_rankings AS yr
JOIN top_industries AS ti ON yr.industry = ti.industry
WHERE year in (2019,2020,2021)
ORDER BY industry,year DESC;