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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 companies
LIMIT 5DataFrameas
df1
variable
WITH top_industries AS
(SELECT ind.industry, COUNT(ind.company_id) num_unicorns
FROM industries ind
LEFT JOIN dates dat
ON ind.company_id = dat.company_id
WHERE DATE_PART('Year', dat.date_joined) IN ('2019', '2020', '2021')
GROUP BY ind.industry
ORDER BY num_unicorns DESC
LIMIT 3),
yearly_rankings AS
(SELECT ind.industry, DATE_PART('Year', dat.date_joined) "year", COUNT(ind.company_id) num_unicorns, ROUND((AVG(fun.valuation)/1000000000), 2) average_valuation_billions
FROM industries ind
LEFT JOIN dates dat
ON ind.company_id = dat.company_id
LEFT JOIN funding fun
ON ind.company_id = fun.company_id
WHERE DATE_PART('Year', dat.date_joined) IN ('2019', '2020', '2021')
GROUP BY ind.industry, "year")
SELECT industry, year, num_unicorns, average_valuation_billions
FROM yearly_rankings
WHERE industry in (SELECT industry
FROM top_industries)
GROUP BY industry, num_unicorns, year, average_valuation_billions
ORDER BY industry, year DESC
LIMIT 30