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 : count on by induesties
WITH top_industry AS (
SELECT
i.industry AS industry,
COUNT(i.*) AS count
FROM public.industries AS i
inner JOIN public.dates AS d
ON i.company_id = d.company_id
WHERE EXTRACT (YEAR FROM d.date_joined ) IN ('2019','2020','2021')
GROUP BY i.industry
ORDER BY count DESC
limit 3
),
yearly_ranking AS(
SELECT
i.industry ,
EXTRACT (YEAR FROM d.date_joined) as year ,
COUNT(i.*) AS num_unicorns ,
AVG(f.valuation) AS AVG_valuation
FROM public.industries AS i
INNER JOIN public.dates AS d
ON i.company_id = d.company_id
INNER JOIN public.funding AS f
ON d.company_id = f.company_id
GROUP BY i.industry , EXTRACT (YEAR FROM d.date_joined)
)
SELECT
industry,
year,
num_unicorns,
ROUND(AVG_valuation / 1000000000 , 2) AS average_valuation_billions
FROM yearly_ranking
WHERE YEAR IN ('2019','2020','2021')
AND industry in (SELECT
industry
FROM top_industry)
GROUP BY industry, year, num_unicorns , ROUND(AVG_valuation / 1000000000 , 2)
ORDER BY year DESC , num_unicorns DESC
;