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.
SELECT
i.industry as industry,
count(d.*) as total_unicorns
from dates as d
join industries as i on i.company_id = d.company_id
where extract(year from date_joined) in (2019,2020,2021)
group by industry
order by total_unicorns desc
limit 3
Select industry,
extract(year from d.date_joined) as year,
count(d.company_id) as num_unicorn,
Round(avg(f.valuation)/1000000000,2) as average_valuation_billions
from industries as i
join dates as d on i.company_id = d.company_id
join funding as f on f.company_id =i.company_id
group by 1,2with top_industries as (SELECT
i.industry as industry,
count(d.*) as total_unicorns
from dates as d
join industries as i on i.company_id = d.company_id
where extract(year from date_joined) in (2019,2020,2021)
group by industry
order by total_unicorns desc
limit 3),
Yearly_industry_info as (Select industry,
extract(year from d.date_joined) as year,
count(d.company_id) as num_unicorns,
Round(avg(f.valuation)/1000000000,2) as average_valuation_billions
from industries as i
join dates as d on i.company_id = d.company_id
join funding as f on f.company_id =i.company_id
group by 1,2)
SELECT *
from Yearly_industry_info
where year in (2019,2020,2021) AND industry in (SELECT
industry from top_industries)
order by year desc, num_unicorns desc