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
| 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. |
The output
Your query should return a table in the following format:
| industry | year | num_unicorns | average_valuation_billions |
|---|---|---|---|
| industry1 | 2021 | --- | --- |
| industry2 | 2020 | --- | --- |
| industry3 | 2019 | --- | --- |
| industry1 | 2021 | --- | --- |
| industry2 | 2020 | --- | --- |
| industry3 | 2019 | --- | --- |
| industry1 | 2021 | --- | --- |
| industry2 | 2020 | --- | --- |
| industry3 | 2019 | --- | --- |
Where industry1, industry2, and industry3 are the three top-performing industries.
--Use a CTE aliased as top3_perf_industries to find top three performing industries
WITH top3_perf_industries AS (
--Select industry and count of new unicorns for each industry
SELECT i.industry,
COUNT(*) AS num_new_unicorns
--Inner join industries and dates table on the company_id column
FROM industries AS i
INNER JOIN dates AS d
ON i.company_id = d.company_id
--Filter for the three most recent years
WHERE EXTRACT(YEAR FROM date_joined) IN (2019, 2020, 2021)
--Group by industry
GROUP BY i.industry
--Order by count of new unicorns per industry in descending order
ORDER BY num_new_unicorns DESC
--Limit results to the first three rows
LIMIT 3
),
--Create a second CTE and name it yearly_rankings
yearly_rankings AS (
--Select industry, extract year from date_joined column,count the number new unicorns and average valuation per industry-year pair
SELECT i.industry,
EXTRACT(YEAR FROM d.date_joined) AS year,
COUNT(*) AS num_unicorns,
AVG(f.valuation) AS average_valuation
--Inner join industries,dates and funding table on the company_id field
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
--Group by industry followed by year in ascending order
GROUP BY i.industry, year)
--Select industry,yeaad num_unicorns from yearly_rankings
SELECT industry,
year,
num_unicorns,
--Divide the average_valuation field by 10^6 and round the resulting values to two decimal places
ROUND(AVG(average_valuation/1000000000),2) AS average_valuation_billions
FROM yearly_rankings
--Filter the year for 2019,2020 and 2021 and the industry for the top three performing industries
WHERE YEAR IN (2019, 2020, 2021) AND
industry IN (SELECT industry
FROM top3_perf_industries)
--Group by industry, year and num_unicorns fields
GROUP BY industry,year,num_unicorns
--Order by year and num_unicorns in descending order
ORDER BY year DESC, num_unicorns DESC;