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Impact Analysis of GoodThought NGO Initiatives

Use SQL to explore and analyze GoodThought NGO's database, uncovering key insights from over 13 years of transformative projects.

Since 2010, GoodThought NGO has led transformative efforts in education, healthcare, and sustainability worldwide. Dive into a PostgreSQL database to analyze key metrics from 2010 to 2023, track donations, and assess program effectiveness. This project offers a deep dive into data, revealing the impact and outcomes of GoodThought's initiatives

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!

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DataFrameas
df
variable
SELECT * FROM assignments
LIMIT 10
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DataFrameas
df1
variable
SELECT * FROM donations
LIMIT 10
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DataFrameas
df2
variable
SELECT * FROM donors
LIMIT 10

1. Identifying the top five assignments with the highest total donations by donor type

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 assignment_name, region, ROUND(SUM(amount),2) AS rounded_total_donation_amount, donor_type
FROM donors AS d1
INNER JOIN donations AS d2
USING(donor_id)
INNER JOIN assignments AS a1
USING(assignment_id)
GROUP BY assignment_name, region, donor_type
ORDER BY rounded_total_donation_amount DESC
LIMIT 5
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DataFrameas
df6
variable
-- Alternative solution to query above

WITH donation_details AS (
    SELECT
        d.assignment_id,
        ROUND(SUM(d.amount), 2) AS rounded_total_donation_amount,
        dn.donor_type
    FROM donations d
    JOIN donors dn ON d.donor_id = dn.donor_id
    GROUP BY d.assignment_id, dn.donor_type
)

SELECT
    a.assignment_name,
    a.region,
    dd.rounded_total_donation_amount,
    dd.donor_type
FROM assignments a
JOIN donation_details dd ON a.assignment_id = dd.assignment_id
ORDER BY dd.rounded_total_donation_amount DESC
LIMIT 5;

2. Identifying the leading assignment by impact in each region

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

WITH num_donation AS (
	SELECT assignment_id, COUNT(donation_id) AS num_total_donations
	FROM donations
	GROUP BY assignment_id
	HAVING COUNT(donation_id) > 0
),

max_impact_score AS (
	SELECT 
		assignment_name, region, impact_score, num_total_donations,
		ROW_NUMBER() OVER (PARTITION BY region ORDER BY impact_score DESC) AS rank_in_region
	FROM assignments
	INNER JOIN num_donation AS nd
	ON assignments.assignment_id = nd.assignment_id
)

SELECT assignment_name, region, impact_score, num_total_donations
FROM max_impact_score
WHERE rank_in_region = 1
ORDER BY region;
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DataFrameas
df7
variable
-- Alternative solution to query above

WITH donation_counts AS (
    SELECT
        assignment_id,
        COUNT(donation_id) AS num_total_donations
    FROM donations
    GROUP BY assignment_id
),

ranked_assignments AS (
    SELECT
        a.assignment_name,
        a.region,
        a.impact_score,
        dc.num_total_donations,
        ROW_NUMBER() OVER (PARTITION BY a.region ORDER BY a.impact_score DESC) AS rank_in_region
    FROM assignments a
    JOIN donation_counts dc ON a.assignment_id = dc.assignment_id
    WHERE dc.num_total_donations > 0
)

SELECT
    assignment_name,
    region,
    impact_score,
    num_total_donations
FROM ranked_assignments
WHERE rank_in_region = 1
ORDER BY region ASC;
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