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GoodThought NGO has been a catalyst for positive change, focusing its efforts on education, healthcare, and sustainable development to make a significant difference in communities worldwide. With this mission, GoodThought has orchestrated an array of assignments aimed at uplifting underprivileged populations and fostering long-term growth.

This project offers a hands-on opportunity to explore how data-driven insights can direct and enhance these humanitarian efforts. In this project, you'll engage with the GoodThought PostgreSQL database, which encapsulates detailed records of assignments, funding, impacts, and donor activities from 2010 to 2023. This comprehensive dataset includes:

  • Assignments: Details about each project, including its name, duration (start and end dates), budget, geographical region, and the impact score.
  • Donations: Records of financial contributions, linked to specific donors and assignments, highlighting how financial support is allocated and utilized.
  • Donors: Information on individuals and organizations that fund GoodThought’s projects, including donor types.

Refer to the below ERD diagram for a visual representation of the relationships between these data tables:

You will execute SQL queries to answer two questions, as listed in the instructions. Good luck!

Task 1

List the top five assignments based on total value of donations, categorized by donor type. The output should include four columns:

  1. assignment_name,
  2. region,
  3. rounded_total_donation_amount rounded to two decimal places, and
  4. donor_type,

sorted by rounded_total_donation_amount in descending order.

Save the result as highest_donation_assignments.

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DataFrameas
highest_donation_assignments
variable
-- highest_donation_assignments
SELECT
	a.assignment_name,
	a.region,
	ROUND(SUM(d.amount), 2) AS rounded_total_donation_amount,
	donors.donor_type
FROM assignments AS a
	JOIN donations AS d
	USING(assignment_id)
	JOIN donors
	USING(donor_id)
GROUP BY a.assignment_name, a.region, donors.donor_type
ORDER BY rounded_total_donation_amount DESC
LIMIT 5;

Task 2

Identify the assignment with the highest impact score in each region, ensuring that each listed assignment has received at least one donation. The output should include four columns:

  1. assignment_name,
  2. region,
  3. impact_score, and
  4. num_total_donations,

sorted by region in ascending order.

Include only the highest-scoring assignment per region, avoiding duplicates within the same region. Save the result as top_regional_impact_assignments.

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DataFrameas
top_regional_impact_assignments
variable
-- top_regional_impact_assignments

-- Calculate the total number of donations for each assignment
WITH assignment_donations AS (
	SELECT
		assignment_id,
		COUNT(donation_id) AS num_total_donations
	FROM donations
	GROUP BY assignment_id
),

-- Rank assignments within each region based on impact score
ranked_regional_assignments AS (
	SELECT
		a.assignment_name,
		a.region,
		a.impact_score,
		d.num_total_donations,
		ROW_NUMBER()
			OVER (PARTITION BY a.region
				  ORDER BY a.impact_score DESC) as row_n
	FROM assignments a
	JOIN assignment_donations d USING(assignment_id)
)

-- Select the top-ranked assignment for each region
SELECT
	assignment_name,
	region,
	impact_score,
	num_total_donations
FROM ranked_regional_assignments
WHERE row_n = 1
ORDER BY region ASC;