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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 df AS(SELECT *,
RANK() OVER(PARTITION BY year ORDER BY number_unicorns DESC) AS rank
FROM
(SELECT
COUNT(DISTINCT f.company_id) AS number_unicorns,
industry,
date_part('year',date_joined) AS year,
AVG(valuation) AS average_valuation_billions
FROM funding AS f
LEFT JOIN industries AS i
ON f.company_id=i.company_id
LEFT JOIN dates AS d
ON f.company_id=d.company_id
WHERE date_joined BETWEEN '2018-12-31' AND '2022-01-01'
AND valuation >= 1000000000
GROUP BY year, industry) AS t)
SELECT industry, rank, year, number_unicorns, cast(average_valuation_billions/1000000000 AS numeric(16, 2)) AS average_valuation_billions
FROM df
WHERE rank < 4
ORDER BY rank, year DESC