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Project - Analyzing Unicorn Companies
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
| Column | Description |
|---|---|
| company_id | A unique ID for the company. |
| date_joined | The date that the company became a unicorn. |
| year_founded | The year that the company was founded. |
funding
| Column | Description |
|---|---|
| company_id | A unique ID for the company. |
| valuation | Company value in US dollars. |
| funding | The amount of funding raised in US dollars. |
| select_investors | A list of key investors in the company. |
industries
| Column | Description |
|---|---|
| company_id | A unique ID for the company. |
| industry | The industry that the company operates in. |
companies
| Column | Description |
|---|---|
| company_id | A unique ID for the company. |
| company | The name of the company. |
| city | The city where the company is headquartered. |
| country | The country where the company is headquartered. |
| continent | The continent where the company is headquartered. |
DataFrameas
df
variable
SELECT * FROM companiesDataFrameas
df
variable
SELECT * FROM datesDataFrameas
df
variable
SELECT * FROM fundingDataFrameas
df
variable
SELECT * FROM industriesDataFrameas
df
variable
WITH top_three AS
(
SELECT i.industry, COUNT(*) AS num_unicorns
FROM industries AS i
INNER JOIN dates AS d
ON i.company_id = d.company_id
WHERE EXTRACT(YEAR FROM d.date_joined) BETWEEN 2019 AND 2021
GROUP BY i.industry, EXTRACT(YEAR FROM d.date_joined)
ORDER BY num_unicorns DESC
LIMIT 3
),
financial_performance AS
(
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
GROUP BY i.industry, EXTRACT(YEAR FROM d.date_joined)
ORDER BY num_unicorns DESC, average_valuation_billions DESC
)
SELECT industry, year, num_unicorns, average_valuation_billions
FROM financial_performance
WHERE industry in
(SELECT industry
FROM top_three)
GROUP BY industry, year, num_unicorns, average_valuation_billions
ORDER BY industry, year DESC;DataFrameas
df
variable
WITH financial_performance AS
(
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
GROUP BY i.industry, EXTRACT(YEAR FROM d.date_joined)
ORDER BY num_unicorns DESC, average_valuation_billions DESC
)
SELECT industry, year, num_unicorns, average_valuation_billions
FROM financial_performance
WHERE industry in
(SELECT industry
FROM top_three)
GROUP BY industry, year, num_unicorns, average_valuation_billions
ORDER BY industry, year DESC;