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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

ColumnDescription
company_idA unique ID for the company.
date_joinedThe date that the company became a unicorn.
year_foundedThe year that the company was founded.

funding

ColumnDescription
company_idA unique ID for the company.
valuationCompany value in US dollars.
fundingThe amount of funding raised in US dollars.
select_investorsA list of key investors in the company.

industries

ColumnDescription
company_idA unique ID for the company.
industryThe industry that the company operates in.

companies

ColumnDescription
company_idA unique ID for the company.
companyThe name of the company.
cityThe city where the company is headquartered.
countryThe country where the company is headquartered.
continentThe continent where the company is headquartered.

The output

Your query should return a table in the following format:

industryyearnum_unicornsaverage_valuation_billions
industry12021------
industry22020------
industry32019------
industry12021------
industry22020------
industry32019------
industry12021------
industry22020------
industry32019------

Where industry1, industry2, and industry3 are the three top-performing industries.

Spinner
DataFrameas
df2
variable
select industry , company_id
from industries 
where company_id in (select company_id 
from dates
where extract(year from date_joined) in ( 2019,2020,2021))
Spinner
DataFrameas
df3
variable
select industry , company_id
from industries 
where company_id in (select company_id 
from dates
where extract(year from date_joined) in ( 2019,2020,2021))
Spinner
DataFrameas
df4
variable
select industry,count(*)
from(

select industry , company_id
from industries 
where company_id in (select company_id 
from dates
where extract(year from date_joined) in ( 2019,2020,2021))) a
group by industry
order by count desc
Spinner
DataFrameas
df6
variable
select extract(year from date_joined) as year,industry
from dates inner join industries 
on dates.company_id=industries.company_id
where dates.company_id in (select company_id 
from dates
where extract(year from date_joined) in ( 2019,2020,2021)) 
and industry in ('Fintech', 'Internet software & services','E-commerce & direct-to-consumer')
group by year,industry
Spinner
DataFrameas
df8
variable
select industry,extract(year from date_joined) as year,count(*) num_unicorns,round(avg(valuation)/1000000000,2) average_valuation_billions

from dates inner join industries 
on dates.company_id=industries.company_id
inner join funding 
on dates.company_id=funding.company_id
where dates.company_id in (select company_id 
from dates
where extract(year from date_joined) in ( 2019,2020,2021)) 
and industry in ('Fintech', 'Internet software & services','E-commerce & direct-to-consumer')
group by industry,year
Spinner
DataFrameas
df1
variable
select industry,extract(year from date_joined) as year,count(*) num_unicorns,round(avg(valuation)/1000000000,2) average_valuation_billions
from dates inner join industries 
on dates.company_id=industries.company_id
inner join funding 
on dates.company_id=funding.company_id
where dates.company_id in (select company_id 
from dates
where extract(year from date_joined) in ( 2019,2020,2021)) 
and industry in ('Fintech', 'Internet software & services','E-commerce & direct-to-consumer')
group by industry,year
order by year desc,num_unicorns desc