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
SELECT * FROM companiesDataFrameas
df
variable
WITH B AS(
SELECT industry, count(i.*)
FROM industries AS i
INNER JOIN dates AS d
ON i.company_id = d.company_id
WHERE EXTRACT(year FROM date_joined) IN ('2019','2020','2021')
GROUP BY industry
ORDER BY count DESC
LIMIT 3),
RANKING AS(
SELECT i.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 i.company_id = f.company_id
GROUP BY industry, year)
SELECT industry, year, num_unicorns, ROUND(average_valuation/1000000000,2) AS average_valuation_billions
FROM RANKING
WHERE year in ('2019', '2020', '2021')
AND industry IN (SELECT industry
FROM B)
GROUP BY industry, num_unicorns,year,average_valuation_billions
ORDER BY industry, year DESC;