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 date_update AS
(
SELECT
public.dates.company_id,
EXTRACT(YEAR from public.dates.date_joined) as year_join
FROM public.dates
WHERE EXTRACT(YEAR from public.dates.date_joined) BETWEEN 2019 AND 2021
)
,count_by_industry AS
(
SELECT
public.industries.industry,
date_update.year_join,
COUNT(date_update.company_id) as num_unicorns,
ROUND(AVG(public.funding.valuation)/1000000000,2) as average_valuation_billions
FROM public.industries
LEFT JOIN date_update
ON public.industries.company_id = date_update.company_id
LEFT JOIN public.funding
ON public.industries.company_id = public.funding.company_id
GROUP BY public.industries.industry, date_update.year_join
HAVING date_update.year_join IS NOT NULL
ORDER BY date_update.year_join desc,num_unicorns DESC
)
,test AS
(
SELECT
industry,
year_join,
num_unicorns,
average_valuation_billions,
RANK() OVER(PARTITION by year_join ORDER BY year_join DESC,num_unicorns DESC ) as rank
FROM count_by_industry
GROUP BY industry, year_join,num_unicorns,average_valuation_billions
)
,top_industries AS
(
SELECT public.industries.industry,
COUNT(public.industries.*)
FROM public.industries
INNER JOIN dates AS d
ON public.industries.company_id = d.company_id
WHERE EXTRACT(year FROM d.date_joined) in ('2019', '2020', '2021')
GROUP BY public.industries.industry
ORDER BY count DESC
LIMIT 3
)
SELECT
test.industry,
test.year_join as year,
test.num_unicorns,
test.average_valuation_billions
FROM test
INNER JOIN top_industries
ON top_industries.industry = test.industry
GROUP BY test.industry, year_join,num_unicorns,average_valuation_billions,rank
ORDER BY rank, year_join DESC
LIMIT 9;