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.

This query retrieves information about the top 3 industries based on the number of unicorns (companies with a valuation of $1 billion or more) in the years 2019, 2020, and 2021. The query joins three tables: industries, dates, and funding.

The SELECT statement selects the industry, year, num_unicorns, and average_valuation_billions columns.
The FROM clause specifies the tables to join: industries, dates, and funding.
The WHERE clause filters the results to include only the industries that are in the top 3 based on the number of unicorns.
The GROUP BY clause groups the results by industry and year.
The HAVING clause further filters the results to include only the years 2019, 2020, and 2021.
The ORDER BY clause orders the results by industry and year in descending order.

This query provides insights into the top industries and their performance in terms of the number of unicorns and average valuation in billions of dollars.

DataFrameas

df

variable

SELECT 
    industry, 
    date_part('year',date_joined) AS year,
    COUNT(*) AS num_unicorns,
    ROUND(AVG(valuation)/1000000000,2) AS average_valuation_billions
FROM industries AS i
LEFT JOIN dates AS d 
ON i.company_id=d.company_id
LEFT JOIN funding AS f
ON i.company_id=f.company_id
WHERE industry IN (
        SELECT industry
        FROM industries AS i
        LEFT JOIN dates AS d
        ON i.company_id=d.company_id
        WHERE date_part('year',date_joined)='2019'
            OR date_part('year',date_joined)='2020'
            OR date_part('year',date_joined)='2021'
        GROUP BY industry
        ORDER BY COUNT(*) DESC
        LIMIT 3)
GROUP BY industry,year
HAVING date_part('year',date_joined)='2019'
        OR date_part('year',date_joined)='2020'
        OR date_part('year',date_joined)='2021'
ORDER BY industry,year DESC;