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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
-- Define a Common Table Expression (CTE) to find the top industries based on the count of companies in the last three years.
WITH top_industries AS (
    SELECT i.industry, 
        COUNT(i.*)  -- Count the number of companies in each industry
    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')  -- Filter companies joined in the specified years
    GROUP BY industry
    ORDER BY count DESC
    LIMIT 3  -- Limit the results to the top 3 industries
),

-- Define another CTE to calculate yearly rankings for each industry based on the number of unicorns and average valuation.
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  -- Calculate the average valuation for each industry and year
    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 the final results, including industry, year, number of unicorns, and average valuation in billions.
SELECT industry,
    year,
    num_unicorns,
    ROUND(AVG(average_valuation / 1000000000), 2) AS average_valuation_billions  -- Convert average valuation to billions and round to 2 decimal places
FROM yearly_rankings
WHERE year in ('2019', '2020', '2021')  -- Filter results for the specified years
    AND industry in (SELECT industry
                    FROM top_industries)  -- Filter results to include only the top industries
GROUP BY industry, num_unicorns, year
ORDER BY year DESC, num_unicorns DESC;  -- Order the final results by year and number of unicorns in descending order
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
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