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Project - Analyzing Unicorn Companies
Did you know that the average return from investing in stocks is 10% per year! 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. |
DataFrameas
df
variable
[32]
select i.industry, d.year_founded as year, count(f.valuation/1000000000) as unicorn, avg(f.valuation/1000000000) as average_valuation_billions
from funding as f
inner join industries as i
on i.company_id = f.company_id
inner join dates as d
on f.company_id = d.company_id
where industry in (
select i.industry
from funding as f
inner join industries as i
on i.company_id = f.company_id
inner join dates as d
on f.company_id = d.company_id
where d.year_founded in ('2019', '2020', '2021') and f.valuation > 1000000000
group by i.industry
order by count(f.valuation/1000000000) desc
limit 3)
and d.year_founded in ('2019', '2020', '2021') and f.valuation > 1000000000
group by i.industry, d.year_founded
order by average_valuation_billions desc, year desc
union