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Project: Analyzing Unicorn Companies
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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.
    Spinner
    DataFrameavailable as
    df2
    variable
    -- Creating a temporary data table(CTE), named 'data', that contains all the desired columns, filtered for the period 2019-2021
    WITH data as (
      	SELECT 
        industry, 
    	count(date_joined) as num_unicorns, 
    	EXTRACT(YEAR FROM date_joined) AS year,
    	ROUND(AVG(valuation/1000000000),2) AS average_valuation_billions
        FROM dates AS d
        LEFT JOIN funding AS f
            ON d.company_id = f.company_id
        LEFT JOIN industries AS i
            ON f.company_id = i.company_id
        LEFT JOIN companies AS c
            ON i.company_id = c.company_id
        WHERE EXTRACT(YEAR FROM d.date_joined) BETWEEN 2019 AND 2021
    	GROUP BY industry, year
    	),
    -- Creating a second CTE named 'top_industry' to find the top 3 industries based on the total number of unicorns during the period 2019-2021
    top_industry AS (
        	SELECT 
            industry, 
            count(*) as n
        	FROM industries as i
    		INNER JOIN dates as d
    		ON i.company_id = d.company_id
    		WHERE EXTRACT(YEAR FROM d.date_joined) BETWEEN 2019 AND 2021
    		GROUP BY industry
    		ORDER BY n DESC
    		LIMIT 3
    		)
    -- Finally we combine the 2 above created CTE's to query for tbe columns of interest
    SELECT industry, year, num_unicorns, average_valuation_billions
    FROM data
    WHERE industry IN (SELECT industry FROM top_industry)
    ORDER BY year DESC, num_unicorns DESC;