Project - Analyzing Unicorn Companies
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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

    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.
    Unknown integration
    DataFrameavailable as
    df
    variable
    WITH top_industries AS
    (
        SELECT i.industry, 
            COUNT(i.*)
        FROM industries AS i
        INNER JOIN dates AS d
            ON i.company_id = d.company_id
        WHERE EXTRACT(year FROM d.date_joined) in ('2019', '2020', '2021')
        GROUP BY industry
        ORDER BY count DESC
        LIMIT 3
    ),
    
    yearly_rankings AS 
    (
        SELECT COUNT(i.*) AS num_unicorns,
            i.industry,
            EXTRACT(year FROM d.date_joined) AS year,
            AVG(f.valuation) AS average_valuation
        FROM industries AS i
        INNER JOIN dates AS d
            ON i.company_id = d.company_id
        INNER JOIN funding AS f
            ON d.company_id = f.company_id
        GROUP BY industry, year
    )
    
    SELECT industry,
        year,
        num_unicorns,
        ROUND(AVG(average_valuation / 1000000000), 2) AS average_valuation_billions
    FROM yearly_rankings
    WHERE year in ('2019', '2020', '2021')
        AND industry in (SELECT industry
                        FROM top_industries)
    GROUP BY industry, num_unicorns, year, average_valuation
    ORDER BY industry, year DESC
    
    
    /**
    WITH unicorn_year AS(
    SELECT *, EXTRACT(YEAR FROM date_joined) AS year_joined FROM dates
    WHERE EXTRACT(YEAR FROM date_joined) >= 2019 AND EXTRACT(YEAR FROM date_joined) <= 2021), 
    top_companies AS(
    	SELECT i.industry, COUNT(i.*)
    FROM dates AS d
    INNER JOIN public.industries as 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 COUNT DESC
    LIMIT 3
    )
    
    SELECT i.industry, uy.year_joined, COUNT(*) AS num_unicorns, ROUND(AVG(f.valuation)/1000000000,2) AS average_valuation_billions
    FROM unicorn_year as uy
    INNER JOIN public.industries AS i ON uy.company_id = i.company_id
    INNER JOIN public.funding AS f ON uy.company_id = f.company_id
    INNER JOIN top_companies ON top_companies.industry = i.industry
    GROUP BY i.industry, uy.year_joined
    ORDER BY i.industry, uy.year_joined DESC;
    **/
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.