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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 *
FROM industries;
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 industry, EXTRACT('year' FROM date_joined) AS year, COUNT(company_id) AS num_unicorns
FROM industries
INNER JOIN dates
USING(company_id)
WHERE EXTRACT('year' FROM date_joined) IN ('2019', '2020', '2021')
GROUP BY industry, year
ORDER BY num_unicorns DESC, industry DESC, 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 EXTRACT('year' FROM date_joined) AS year, industry, COUNT(company_id) AS num_unicorns, AVG(valuation) AS avg_valuation
FROM industries
INNER JOIN funding
USING(company_id)
INNER JOIN dates
USING (company_id)
WHERE EXTRACT('year' FROM date_joined) IN ('2019', '2020', '2021')
GROUP BY year, industry;
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 industry, COUNT(company_id) AS num_unicorns
FROM industries
INNER JOIN dates
USING(company_id)
WHERE EXTRACT('year' FROM date_joined) IN ('2019', '2020', '2021')
GROUP BY industry
ORDER BY num_unicorns DESC
LIMIT 3),

yearly_rankings AS
(SELECT EXTRACT('year' FROM date_joined) AS year, industry, COUNT(company_id) AS num_unicorns, AVG(valuation) AS avg_valuation
FROM industries
INNER JOIN funding
USING(company_id)
INNER JOIN dates
USING (company_id)
WHERE EXTRACT('year' FROM date_joined) IN ('2019', '2020', '2021')
GROUP BY year, industry)

SELECT industry, year, num_unicorns, ROUND(AVG(avg_valuation / 1000000000), 2) AS average_valuation_billions
FROM yearly_rankings
WHERE year IN (2019, 2020, 2021) AND industry IN 
    (SELECT industry
     FROM top_industries
     ORDER BY num_unicorns DESC)
GROUP BY industry, year, num_unicorns
ORDER BY industry, 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
df1
variable
SELECT industry, COUNT(company_id) AS num_unicorns
FROM industries
INNER JOIN dates
USING(company_id)
WHERE EXTRACT('year' FROM date_joined) IN ('2019', '2020', '2021')
GROUP BY industry
ORDER BY num_unicorns 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
SELECT EXTRACT('year' FROM date_joined) AS year, industry, COUNT(company_id) AS num_unicorns, AVG(valuation) AS avg_valuation
FROM industries
INNER JOIN funding
USING(company_id)
INNER JOIN dates
USING (company_id)
WHERE EXTRACT('year' FROM date_joined) IN ('2019', '2020', '2021')
GROUP BY year, industry;
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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