Did you know that the average return from investing in stocks is 10% per year (not accounting for inflation)? 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. |
The output
Your query should return a table in the following format:
| industry | year | num_unicorns | average_valuation_billions |
|---|---|---|---|
| industry1 | 2021 | --- | --- |
| industry2 | 2020 | --- | --- |
| industry3 | 2019 | --- | --- |
| industry1 | 2021 | --- | --- |
| industry2 | 2020 | --- | --- |
| industry3 | 2019 | --- | --- |
| industry1 | 2021 | --- | --- |
| industry2 | 2020 | --- | --- |
| industry3 | 2019 | --- | --- |
Where industry1, industry2, and industry3 are the three top-performing industries.
WITH three_best_performing_industries AS(
SELECT i.industry, COUNT(i.company_id)
FROM dates AS d
JOIN industries AS i
ON d.company_id = i.company_id
WHERE d.year_founded IN (2019, 2020, 2021)
GROUP BY i.industry
ORDER BY COUNT(i.company_id) DESC
LIMIT 3)
SELECT i.industry AS industry, EXTRACT(YEAR FROM d.date_joined) AS year, COUNT(i.company_id) AS num_unicorns, ROUND(AVG(f.valuation)/power(10,9)::DECIMAL,2) AS average_valuation_billions
FROM dates AS d
JOIN industries AS i
ON d.company_id = i.company_id
JOIN funding AS f
ON i.company_id = f.company_id
WHERE EXTRACT(YEAR FROM d.date_joined) IN (2019, 2020, 2021)
AND i.industry IN (SELECT industry
FROM three_best_performing_industries)
GROUP BY i.industry, EXTRACT(YEAR FROM d.date_joined)
ORDER BY year DESC, num_unicorns DESC;SELECT i.industry AS industry, EXTRACT(YEAR FROM d.date_joined) AS year, COUNT(i.company_id) AS num_unicorns,
ROUND(AVG(f.valuation)/power(10,9)::DECIMAL,2) AS average_valuation_billions
FROM dates AS d
JOIN industries AS i
ON d.company_id = i.company_id
JOIN funding AS f
ON i.company_id = f.company_id
RANK() OVER (PARTITION BY i.industry, )SELECT i.industry, COUNT(i.company_id)
FROM dates AS d
JOIN industries AS i
ON d.company_id = i.company_id
WHERE d.year_founded IN (2019, 2020, 2021)
GROUP BY i.industry
ORDER BY COUNT(i.company_id) DESC
LIMIT 3;