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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! 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
    SELECT 
     i.industry,
     EXTRACT(year FROM d.date_joined) AS year,
     COUNT(*) AS num_unicorns,
     ROUND(AVG(f.valuation)/1000000000,2) AS average_valuation_billions
     FROM industries AS i
     INNER JOIN dates AS d ON i.company_id = d.company_id
     INNER JOIN funding AS f ON i.company_id = f.company_id
     WHERE EXTRACT(year FROM d.date_joined) BETWEEN 2019 AND 2021
     AND i.industry IN (
    	 SELECT top_industries.industry FROM(
     SELECT i.industry,
    COUNT(CASE WHEN EXTRACT(year FROM d.date_joined) = 2019 THEN 2019
     WHEN EXTRACT(year FROM d.date_joined) = 2020 THEN 2020
     WHEN EXTRACT(year FROM d.date_joined) = 2021 THEN 2021
     END) AS num_per_ind
     FROM industries as i
     INNER join dates as d ON i.company_id = d.company_id
     GROUP BY industry
     ORDER BY num_per_ind DESC
     LIMIT 3
     ) AS top_industries)
     GROUP BY industry, year
     ORDER BY industry, 2 DESC
     LIMIT 9
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
    Unknown integration
    DataFrameavailable as
    df1
    variable
    SELECT
        i.industry,
        EXTRACT(YEAR FROM d.date_joined) AS year,
        COUNT(*) AS num_unicorns,
        ROUND(AVG(f.valuation) / 1000000000) AS average_valuation_billions
    FROM
        industries AS i
    JOIN
        dates AS d
    ON
        i.company_id = d.company_id
    JOIN
        funding AS f
    ON
        i.company_id = f.company_id
    WHERE
        EXTRACT(YEAR FROM d.date_joined) BETWEEN 2019 AND 2021
    GROUP BY
        i.industry, EXTRACT(YEAR FROM d.date_joined)
    HAVING
        COUNT(*) >= 1
    ORDER BY
        num_unicorns DESC
    
     
    
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.