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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.

All Datatables

Spinner
DataFrameas
df
variable
SELECT * 
from dates
Spinner
DataFrameas
df1
variable
SELECT * 
FROM funding
Spinner
DataFrameas
df2
variable
SELECT * 
FROM companies
Spinner
DataFrameas
df3
variable
SELECT * 
FROM industries

Using common table expression

1 - Number of unicorns within these industries

2 - The year that they became a unicorn

3 - Average valuation of unicorn companies

Spinner
DataFrameas
df
variable
WITH industry_unicorn_counts AS (
    SELECT 
        i.industry,
        COUNT(d.company_id) AS total_unicorns
    FROM dates d
    JOIN industries i ON d.company_id = i.company_id
    WHERE EXTRACT(YEAR FROM d.date_joined) IN (2019, 2020, 2021)
    GROUP BY i.industry
    ORDER BY total_unicorns DESC
    LIMIT 3
),
industry_performance AS (
    SELECT 
        i.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 dates d
    JOIN funding f ON d.company_id = f.company_id
    JOIN industries i ON d.company_id = i.company_id
    WHERE EXTRACT(YEAR FROM d.date_joined) IN (2019, 2020, 2021)
      AND i.industry IN (SELECT industry FROM industry_unicorn_counts)
    GROUP BY i.industry, year
)

SELECT 
    industry,
    year,
    num_unicorns,
    average_valuation_billions
FROM industry_performance
ORDER BY year DESC, num_unicorns DESC;

Insights from this notebook suggest that

Top 3 industries in 2021 were

  • Fintech
  • Internet software & services
  • E-commerce & direct-to-consumer

Top 3 industries in 2020 were

  • Internet software & services
  • E-commerce & direct-to-consumer
  • Fintech

Top 3 industries in 2019 were

  • Fintech
  • Internet software & services
  • E-commerce & direct-to-consumer