Assignment 1: Meet the farmersmarket.db and Basic SQL

02_activities/assignments/Assignment1.md

@@ -201,9 +201,66 @@ Link if you encounter a paywall: https://web.archive.org/web/20240422105834/http
 
 **What values systems are embedded in databases and data systems you encounter in your day-to-day life?**
 
-Consider, for example, concepts of fariness, inequality, social structures, marginalization, intersection of technology and society, etc.
+Consider, for example, concepts of fairness, inequality, social structures, marginalization, intersection of technology and society, etc.
 
 
 ```
 Your thoughts...
+Introduction
+
+Databases might seem like neutral tools, but in reality, they carry the values and biases of the societies that create them. The article When Databases Get to Define Family by Qadri R. (2021) explores how Pakistan's digital family registration system enforces a rigid definition of family, excluding marginalized groups and reinforcing social norms. This issue isn’t unique to Pakistan—it’s a global concern that raises questions about fairness, inequality, and how technology shapes our lives.
+
+How Databases Reflect Societal Values
+
+Every day, we interact with databases that influence our opportunities and experiences. From government records to financial systems, these databases shape our access to essential services. But who gets included, and who is left out?
+
+1. Fairness and Inequality
+
+A well-designed database should aim to be inclusive, but many systems reflect existing social biases. For example:
+
+Government databases often fail to recognize non-traditional families, like single-parent households or LGBTQ+ couples, making it harder for them to access rights and benefits.
+
+Credit scoring systems tend to favour wealthier individuals, making it difficult for low-income people to secure loans or mortgages.
+
+Healthcare databases may not fully represent diverse racial and genetic backgrounds, leading to misdiagnoses and inadequate treatment for marginalized communities.
+
+In Pakistan, the National Database and Registration Authority (NADRA) requires women to rely on male guardians for family verification, reinforcing patriarchal structures. This isn’t just a Pakistani issue—many legal and financial systems worldwide fail to recognize relationships that don’t fit traditional norms, creating unnecessary barriers.
+
+2. Social Structures and Marginalization
+
+Data systems tend to mirror existing power structures, often sidelining marginalized groups:
+
+Caste and Ethnicity Bias: Some databases categorize people by race or caste in ways that reinforce discrimination, affecting access to jobs and education.
+
+Gender and Identity: Many systems still use binary gender classifications, making it difficult for non-binary and transgender individuals to have their identities recognized.
+
+Algorithmic Discrimination: AI-driven hiring or law enforcement tools often inherit biases from historical data, leading to unfair treatment, particularly for people of colour.
+
+3. The Intersection of Technology and Society
+
+We often think of technology as neutral, but databases are shaped by human decisions. This raises major ethical concerns:
+
+Surveillance and Privacy: Governments use digital databases for national security, but this can lead to overreach and violations of personal privacy.
+
+Data Ownership and Consent: Most people don’t control their own digital data, allowing corporations and governments to exploit their personal information.
+
+Historical Data Bias: Predictive policing databases, for example, rely on past crime records—but if law enforcement has historically targeted specific racial groups, these databases reinforce discrimination.
+
+Building More Ethical Data Systems
+
+To create fairer, more inclusive data systems, we need to:
+
+-Acknowledge Bias: Database architects and policymakers must actively identify and address biases in data collection and categorization.
+
+-Design for Inclusivity: Systems should recognize diverse identities and family structures, rather than imposing rigid definitions.
+
+-Strengthen Data Protection Laws: Individuals should have control over how their data is collected and used.
+
+-Encourage Public Oversight: Independent organizations should have a role in reviewing and regulating database practices.
+
+Conclusion
+
+Databases influence our lives in ways we often don’t realize. They decide who gets access to resources, how we are identified, and even how we move through the world. But they are not neutral—they reflect the priorities and prejudices of those who build and maintain them. The case of Pakistan’s family database serves as a reminder that we need to actively question and improve the systems we rely on. As we become more dependent on digital systems, ethical considerations must guide database design to ensure fairness and inclusivity for all.
+
+
 ```

02_activities/assignments/Assignment2.md

@@ -54,7 +54,49 @@ The store wants to keep customer addresses. Propose two architectures for the CU
 **HINT:** search type 1 vs type 2 slowly changing dimensions. 
 
 ```
-Your answer...
+To store customer addresses, we propose two different architectures:
+
+Type 1: Overwriting Changes
+
+A simple structure where the latest address replaces the previous one:
+
+Table: CUSTOMER_ADDRESS
+
+customer_id (Primary Key, Foreign Key from Customer)
+
+address
+
+city
+
+state
+
+zip
+
+In this model, whenever a customer updates their address, the old data is overwritten.
+
+Type 2: Retaining History
+
+A more complex structure that maintains historical address changes:
+
+Table: CUSTOMER_ADDRESS_HISTORY
+
+customer_id (Foreign Key from Customer)
+
+address
+
+city
+
+state
+
+zip
+
+start_date
+
+end_date
+
+With this approach, whenever a customer changes their address, a new record is created with the start_date, and the previous record is updated with an end_date, preserving history.
+
+Type 1 is best when historical data is not needed, whereas Type 2 is essential when tracking address changes over time.
 ```
 
 ***
@@ -182,5 +224,36 @@ Consider, for example, concepts of labour, bias, LLM proliferation, moderating c
 
 
 ```
-Your thoughts...
+Section 4: Ethics in AI and Data Processing
+
+Ethical Issues in "Neural Nets are Just People All the Way Down"
+
+The article by Vicki Boykis explores the ethical complexities surrounding AI, specifically Large Language Models (LLMs). Key ethical concerns include:
+
+Bias in AI Models
+
+AI systems inherit biases from their training data, which reflects societal prejudices.
+
+This perpetuates discrimination in automated decision-making.
+
+Labor and Automation
+
+LLMs rely on vast amounts of data labeled by underpaid human workers.
+
+The ethical issue of exploiting global labor for AI development raises concerns.
+
+Challenges in Moderating AI-Generated Content
+
+AI-generated content can be harmful or misleading.
+
+There is no perfect moderation system, as AI models lack human context and ethics.
+
+AI in Society & Ethical Dilemmas
+
+The rapid growth of LLMs creates a monopoly where only a few corporations control AI development.
+
+Ethical concerns arise about transparency, accessibility, and misinformation.
+
+Conclusion
+While AI provides immense benefits, its ethical implications cannot be ignored. To mitigate bias, labor exploitation, and misinformation, there must be continuous oversight, regulation, and a commitment to ethical AI development. AI is ultimately shaped by human values, and ensuring fairness and accountability remains a shared responsibility
 ```

02_activities/assignments/assignment1.sql

@@ -1,23 +1,32 @@
-/* ASSIGNMENT 1 */
-/* SECTION 2 */
+--/* ASSIGNMENT 1 */
+--/* SECTION 2 */
 
 
---SELECT
-/* 1. Write a query that returns everything in the customer table. */
 
+--SELECT
+--/* 1. Write a query that returns everything in the customer table. */
 
+--1. Select everything from the customer table
+SELECT * FROM customer;
 
-/* 2. Write a query that displays all of the columns and 10 rows from the cus- tomer table, 
-sorted by customer_last_name, then customer_first_ name. */
+--/* 2. Write a query that displays all of the columns and 10 rows from the cus- tomer table, 
+--sorted by customer_last_name, then customer_first_ name. */
 
+--2. Select all columns, 10 rows from customer table, sorted by last name then first name
+SELECT * FROM customer 
+ORDER BY customer_last_name, customer_first_name 
+LIMIT 10;
 
 
 --WHERE
-/* 1. Write a query that returns all customer purchases of product IDs 4 and 9. */
+--/* 1. Write a query that returns all customer purchases of product IDs 4 and 9. */
 -- option 1
-
+SELECT * FROM customer_purchases 
+WHERE product_id IN (4, 9);
 
 -- option 2
+SELECT * FROM customer_purchases 
+WHERE product_id = 4 OR product_id = 9;
 
 
 
@@ -27,7 +36,10 @@ filtered by vendor IDs between 8 and 10 (inclusive) using either:
 	2.  one condition using BETWEEN
 */
 -- option 1
-
+-- 4. Select customer purchases with a calculated 'price' column, filtered by vendor IDs between 8 and 10
+SELECT *, (quantity * cost_to_customer_per_qty) AS price 
+FROM customer_purchases 
+WHERE vendor_id BETWEEN 8 AND 10;
 
 -- option 2
 
@@ -39,17 +51,37 @@ Using the product table, write a query that outputs the product_id and product_n
 columns and add a column called prod_qty_type_condensed that displays the word “unit” 
 if the product_qty_type is “unit,” and otherwise displays the word “bulk.” */
 
-
+-- 5. Select product_id, product_name, and create prod_qty_type_condensed column using CASE
+SELECT product_id, product_name, 
+       CASE 
+           WHEN product_qty_type = 'unit' THEN 'unit' 
+           ELSE 'bulk' 
+       END AS prod_qty_type_condensed
+FROM product;
 
 /* 2. We want to flag all of the different types of pepper products that are sold at the market. 
 add a column to the previous query called pepper_flag that outputs a 1 if the product_name 
 contains the word “pepper” (regardless of capitalization), and otherwise outputs 0. */
 
+SELECT product_id, product_name, 
+       CASE 
+           WHEN product_qty_type = 'unit' THEN 'unit' 
+           ELSE 'bulk' 
+       END AS prod_qty_type_condensed,
+       CASE 
+           WHEN LOWER(product_name) LIKE '%pepper%' THEN 1 
+           ELSE 0 
+       END AS pepper_flag
+FROM product;
 
 
 --JOIN
 /* 1. Write a query that INNER JOINs the vendor table to the vendor_booth_assignments table on the 
 vendor_id field they both have in common, and sorts the result by vendor_name, then market_date. */
+SELECT vendor.*, vendor_booth_assignments.* 
+FROM vendor 
+INNER JOIN vendor_booth_assignments ON vendor.vendor_id = vendor_booth_assignments.vendor_id 
+ORDER BY vendor.vendor_name, vendor_booth_assignments.market_date;
 
 
 
@@ -60,6 +92,9 @@ vendor_id field they both have in common, and sorts the result by vendor_name, t
 /* 1. Write a query that determines how many times each vendor has rented a booth 
 at the farmer’s market by counting the vendor booth assignments per vendor_id. */
 
+SELECT vendor_id, COUNT(*) AS booth_rentals
+FROM vendor_booth_assignments
+GROUP BY vendor_id;
 
 
 /* 2. The Farmer’s Market Customer Appreciation Committee wants to give a bumper 
@@ -68,6 +103,13 @@ of customers for them to give stickers to, sorted by last name, then first name.
 
 HINT: This query requires you to join two tables, use an aggregate function, and use the HAVING keyword. */
 
+SELECT c.customer_id, c.customer_first_name, c.customer_last_name, 
+       SUM(cp.quantity * cp.cost_to_customer_per_qty) AS total_spent
+FROM customer_purchases cp
+JOIN customer c ON cp.customer_id = c.customer_id
+GROUP BY c.customer_id
+HAVING total_spent > 2000
+ORDER BY c.customer_last_name, c.customer_first_name;
 
 
 --Temp Table
@@ -81,14 +123,27 @@ When inserting the new vendor, you need to appropriately align the columns to be
 -> To insert the new row use VALUES, specifying the value you want for each column:
 VALUES(col1,col2,col3,col4,col5) 
 */
+-- Drop the temp table if it already exists
+DROP TABLE IF EXISTS new_vendor;
+
+-- Create a temporary table from the vendor table
+CREATE TEMP TABLE new_vendor AS
+SELECT * FROM vendor;
 
+-- Insert a new vendor into the temporary table
+INSERT INTO new_vendor (vendor_id, vendor_name, vendor_type, vendor_owner_first_name, vendor_owner_last_name)
+VALUES (10, 'Thomass Superfood Store', 'Fresh Focused', 'Thomas', 'Rosenthal');
 
 
 -- Date
 /*1. Get the customer_id, month, and year (in separate columns) of every purchase in the customer_purchases table.
 
 HINT: you might need to search for strfrtime modifers sqlite on the web to know what the modifers for month 
 and year are! */
+SELECT customer_id, 
+       STRFTIME('%m', market_date) AS month, 
+       STRFTIME('%Y', market_date) AS year
+FROM customer_purchases;
 
 
 
@@ -98,3 +153,8 @@ Remember that money spent is quantity*cost_to_customer_per_qty.
 HINTS: you will need to AGGREGATE, GROUP BY, and filter...
 but remember, STRFTIME returns a STRING for your WHERE statement!! */
 
+SELECT customer_id, SUM(quantity * cost_to_customer_per_qty) AS total_spent
+FROM customer_purchases
+WHERE STRFTIME('%m', market_date) = '04' AND STRFTIME('%Y', market_date) = '2022'
+GROUP BY customer_id;
+