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.
Step 1. Creation and test of firts CTE to find the top performing industries of 2019, 2020 and 2021 based on the volume of unicorn companies.
SELECT
COUNT(d.company_id) AS num_unicorns,
industry
FROM dates AS d
LEFT JOIN industries AS i
ON d.company_id = i.company_id
WHERE EXTRACT(YEAR FROM date_joined) IN (2019, 2020, 2021)
GROUP BY i.industry
ORDER BY num_unicorns DESC
LIMIT 3;Step 2. Creation and testing of a CTE that returns the the number of unicorn companies per industry and the average valuation by year.
SELECT
COUNT(d.company_id) AS num_unicorns,
i.industry AS industry,
EXTRACT(YEAR FROM d.date_joined) AS year,
AVG(f.valuation) AS average_valuation
FROM dates AS d
LEFT JOIN industries AS i
ON d.company_id = i.company_id
LEFT JOIN funding AS f
ON d.company_id = f.company_id
GROUP BY industry, year;
Step 3. Join both CTEs to rank the industry by number of unicorn companies and the average valuation per year, from 2021, 2020 and 2019.
WITH top_industries AS (
SELECT
COUNT(d.company_id) AS num_unicorns,
industry
FROM dates AS d
LEFT JOIN industries AS i
ON d.company_id = i.company_id
WHERE EXTRACT(YEAR FROM date_joined) IN (2019, 2020, 2021)
GROUP BY i.industry
ORDER BY num_unicorns DESC
LIMIT 3),
rank AS (
SELECT
COUNT(d.company_id) AS num_unicorns,
i.industry AS industry,
EXTRACT(YEAR FROM d.date_joined) AS year,
AVG(f.valuation) AS average_valuation
FROM dates AS d
LEFT JOIN industries AS i
ON d.company_id = i.company_id
LEFT JOIN funding AS f
ON d.company_id = f.company_id
GROUP BY industry, year)
SELECT
r.industry AS industry,
r.year AS year,
r.num_unicorns AS num_unicorns,
ROUND(AVG(average_valuation)/1000000000, 2) AS average_valuation_billions
FROM rank AS r
JOIN top_industries AS t
ON r.industry = t.industry
WHERE r.year IN (2019, 2020, 2021) AND r.industry IN (SELECT industry FROM top_industries)
GROUP BY r.industry, r.year, r.num_unicorns
ORDER BY r.year DESC, r.num_unicorns DESC;