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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.

    The output

    Your query should return a table in the following format:

    industryyearnum_unicornsaverage_valuation_billions
    industry12021------
    industry22020------
    industry32019------
    industry12021------
    industry22020------
    industry32019------
    industry12021------
    industry22020------
    industry32019------

    Where industry1, industry2, and industry3 are the three top-performing industries.

    Unknown integration
    DataFrameavailable as
    df
    variable
    -- tables: dates, funding, companies, industries
    
    SELECT *
    FROM industries
    LIMIT 10
    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 companies.company_id, company, country, industry, funding /1000000 AS funding, valuation / 1000000 AS valuation, DATE(date_joined), year_founded
    FROM companies
    LEFT JOIN industries USING(company_id)
    LEFT JOIN funding USING(company_id)
    LEFT JOIN dates USING(company_id)
    LIMIT 30;
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

    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
    df3
    variable
    SELECT industry, count(*)
    FROM industries
    LEFT JOIN dates USING(company_id)
    WHERE extract(year from date_joined) in (2019, 2020, 2021)
    GROUP BY industry
    ORDER BY count DESC
    LIMIT 3
    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
    df2
    variable
    WITH top_industries AS (
    	SELECT industry, count(*)
    	FROM industries
    	LEFT JOIN dates USING(company_id)
    	WHERE extract(year from date_joined) in (2019, 2020, 2021)
    	GROUP BY industry
    	ORDER BY count DESC
    	LIMIT 3
    ),
    
    unicorn_data AS (
    	SELECT companies.company_id, company, country, ind.industry, funding, valuation, DATE(date_joined), year_founded, extract(year from date_joined) in (2019, 2020, 2021) AS year
    	FROM companies
    	LEFT JOIN industries AS ind USING(company_id)
    	LEFT JOIN funding USING(company_id)
    	LEFT JOIN dates USING(company_id)
    	INNER JOIN top_industries AS top ON ind.industry = top.industry
    	-- WHERE year_founded IN (2019, 2020, 2021)
    
    )
    
    SELECT industry, count(company) AS num_unicorns, ROUND(AVG(valuation)/1000000000,2) AS average_valuation_billions, year
    FROM unicorn_data
    -- WHERE year_founded IN (2019, 2020, 2021)
    GROUP BY industry, year
    ORDER BY year DESC, num_unicorns DESC
    
    
    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
    df4
    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
    ORDER BY year DESC, num_unicorns DESC
    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
    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
    ORDER BY year DESC, num_unicorns DESC
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.