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.
-- unicorns in years 2019-2021
SELECT
company_id,
EXTRACT(year FROM date_joined) AS unicorn_year
FROM dates
WHERE EXTRACT(year FROM date_joined) BETWEEN 2019 AND 2021
LIMIT 5--top_3_industries_2019_2021
SELECT
industry
FROM (SELECT
company_id,
EXTRACT(year FROM date_joined) AS unicorn_year
FROM dates
WHERE EXTRACT(year FROM date_joined) BETWEEN 2019 AND 2021
) AS u
LEFT JOIN industries AS i
ON i.company_id = u.company_id
GROUP BY industry
HAVING COUNT(u.company_id) >0
ORDER BY COUNT(u.company_id) DESC
LIMIT 3WITH top_3_industries_2019_2021 AS (
SELECT
industry
FROM (SELECT
company_id,
EXTRACT(year FROM date_joined) AS unicorn_year
FROM dates
WHERE EXTRACT(year FROM date_joined) BETWEEN 2019 AND 2021
) AS u
LEFT JOIN industries AS i
ON i.company_id = u.company_id
GROUP BY industry
HAVING COUNT(u.company_id) >0
ORDER BY COUNT(u.company_id) DESC
LIMIT 3
),
unicorns_in_2019_2021 AS (
SELECT
company_id,
EXTRACT(year FROM date_joined) AS unicorn_year
FROM dates
WHERE EXTRACT(year FROM date_joined) BETWEEN 2019 AND 2021)
SELECT
top3.industry,
unicorn_year AS year,
count(u.company_id) as num_unicorns,
ROUND(AVG(valuation/1000000000),2) AS average_valuation_billions
FROM top_3_industries_2019_2021 AS top3
INNER JOIN industries AS ind
ON top3.industry = ind.industry
INNER JOIN unicorns_in_2019_2021 AS u
ON ind.company_id = u.company_id
INNER JOIN funding AS f
ON u.company_id = f.company_id
GROUP BY top3.industry, unicorn_year
ORDER BY year desc, num_unicorns desc;