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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. |
Unknown integration
DataFrameavailable as
df
variable
WITH top_industries as (select industry from industries left join dates using (company_id) left join funding using (company_id) where extract(year from date_joined) in ('2019','2020','2021') group by industry order by count(date_joined) desc limit 3)
SELECT industry, extract(year from date_joined) as year , count(date_joined) as num_unicorns,
round(avg(valuation)/1000000000,2) as average_valuation_billions
FROM companies as c
LEFT JOIN industries as i using (company_id)
LEFT JOIN funding as f using (company_id)
LEFT JOIN dates as d using (company_id)
WHERE industry in (Select industry from top_industries)
AND extract(year from date_joined) in (2019,2020,2021)
Group by industry, extract(year from date_joined)
order by industry, year DESC;
Current Type: Bar
Current X-axis: industry
Current Y-axis: average_valuation_billions
Current Color: year
top 3 industries from 2019 to 2021
Current Type: Bar
Current X-axis: num_unicorns
Current Y-axis: industry
Current Color: year