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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 valuation FROM funding
    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 unicorns AS (
    SELECT
    	i.industry
    	,DATE_PART('Year', d.date_joined) AS year
    	,COUNT(*)AS num_unicorns
    	,ROUND(AVG(f.valuation),2) AS average_valuation_billions
    FROM industries AS i
    JOIN dates AS d
    	ON d.company_id = i.company_id
    JOIN funding AS f
    	ON f.company_id = i.company_id
    GROUP BY DATE_PART('Year', d.date_joined), i.industry
    HAVING DATE_PART('Year', d.date_joined) IN (2019,2020,2021)
    )
    SELECT 	
    	industry
    	,year
    	,num_unicorns
    	,RANK() OVER(PARTITION BY industry ORDER BY average_valuation_billions) AS 	average_valuation_billions
    	FROM unicorns;
    	
    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
    	i.industry
        ,COUNT(d.*) AS num_unicorns
    	,DATE_PART('Year', d.date_joined) AS year
    	,AVG(f.valuation) AS average_valuation
    FROM industries AS i
    JOIN dates AS d
            ON i.company_id = d.company_id
    JOIN funding AS f
    	ON d.company_id = f.company_id
    WHERE EXTRACT(year FROM d.date_joined) in ('2019', '2020', '2021')
    GROUP BY i.industry,DATE_PART('Year', d.date_joined)
    )
    
    SELECT industry,
        year,
        num_unicorns,
        ROUND(AVG(average_valuation / 1000000000), 2) AS average_valuation_billions
    FROM yearly_rankings
    WHERE  industry in (SELECT industry
                        FROM top_industries)
    GROUP BY industry, num_unicorns, year, average_valuation
    ORDER BY industry, year 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
    SELECT
    	i.industry
        ,COUNT(d.*)
    	,DATE_PART('Year', d.date_joined) AS year
    	,AVG(f.valuation) AS average_valuation_billions
    FROM industries AS i
    JOIN dates AS d
            ON i.company_id = d.company_id
    JOIN funding AS f
    	ON d.company_id = f.company_id
    WHERE EXTRACT(year FROM d.date_joined) in ('2019', '2020', '2021')
    GROUP BY i.industry,DATE_PART('Year', d.date_joined);
    		
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.