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. |
WITH TOP_INDUSTRIES AS (
SELECT industry, COUNT(company_id) as uni_count_over
FROM industries
WHERE company_id IN (
select company_id
FROM dates
WHERE extract(year FROM date_joined) IN ('2019', '2020', '2021')
)
GROUP BY industry
ORDER BY uni_count_over DESC
limit 3),
yearly_rankings AS (
SELECT
industry,
extract(year FROM date_joined) as year,
count(d.company_id) as num_unicorns,
avg(VALUATION) AS average_valuation
FROM industries as i
JOIN dates as d
ON i.company_id = d.company_id
JOIN funding as f
ON i.company_id = f.company_id
WHERE extract(year FROM date_joined) IN ('2019', '2020', '2021')
GROUP BY industry, year)
select industry,
year,
num_unicorns,
round(avg(average_valuation)/1000000000, 2) as average_valuation_billions
FROM yearly_rankings
WHERE year IN ('2019', '2020', '2021')
AND industry IN (
SELECT industry
FROM top_industries)
group by industry, year, num_unicorns
ORDER BY industry, year DESC;
This was an Unguided Project: I was given just the task to accomplish without any step-by-step instructions. The code below was what I produced on my own, but the grader didn't like it because I ordered my results to a more detailed level. I put the 3 industries in order of how many unicorn companies they had (Fintech was top w/ 173, Internet software was next with 152, and E-commerce was 3rd w/ 75.) The code above was how I modified my code so the grader would accept it.
----CTE ut (unicorn total) gives the top 3 industries (according to # of unicorn companies created in those three years), and their total of unicorn companies
WITH ut AS (
SELECT industry, COUNT(company_id) as uni_count_over
FROM industries
WHERE company_id IN (
select company_id
FROM dates
WHERE extract(year FROM date_joined) IN ('2019', '2020', '2021')
)
GROUP BY industry
ORDER BY uni_count_over DESC
limit 3)
--the main query takes those top 3 industries, counts the number of unicorn companies for them per year, and takes their average valuation per year
SELECT
ut.industry,
extract(year FROM date_joined) as year,
count(d.company_id) as num_unicorns,
ROUND(avg(VALUATION)/1000000000, 2) AS average_valuation_billions
FROM ut
LEFT JOIN industries as i
ON ut.industry = i.industry
LEFT JOIN dates as d
ON i.company_id = d.company_id
LEFT JOIN funding as f
ON i.company_id = f.company_id
WHERE extract(year FROM date_joined) IN ('2019', '2020', '2021')
AND d.company_id IN (
select company_id
FROM dates
WHERE extract(year FROM date_joined) IN ('2019', '2020', '2021')
)
GROUP BY ut.industry, year, ut.uni_count_over
ORDER BY ut.uni_count_over DESC, year DESC;