diff --git a/02_activities/assignments/DC_Cohort/Assignment2.md b/02_activities/assignments/DC_Cohort/Assignment2.md
index 9b804e9ee..e93c5b333 100644
--- a/02_activities/assignments/DC_Cohort/Assignment2.md
+++ b/02_activities/assignments/DC_Cohort/Assignment2.md
@@ -45,16 +45,23 @@ There are several tools online you can use, I'd recommend [Draw.io](https://www.
**HINT:** You do not need to create any data for this prompt. This is a conceptual model only.
+
+
+
#### Prompt 2
We want to create employee shifts, splitting up the day into morning and evening. Add this to the ERD.
+
+
+
#### Prompt 3
The store wants to keep customer addresses. Propose two architectures for the CUSTOMER_ADDRESS table, one that will retain changes, and another that will overwrite. Which is type 1, which is type 2?
**HINT:** search type 1 vs type 2 slowly changing dimensions.
```
-Your answer...
+SCD Type 1 (Overwrite) – When an address changes we simply UPDATE the existing row; only the latest address is kept, all history is lost.
+SCD Type 2 (Historical) – Each change triggers an INSERT of a new row (surrogate key + effective dates), preserving every past address for audit or analysis.
```
***
@@ -88,6 +95,12 @@ Find the NULLs and then using COALESCE, replace the NULL with a blank for the fi
-
+SELECT
+ product_name | ', ' | COALESCE(product_size, '') | ' (' | COALESCE(product_qty_type, 'unit') | ')'
+FROM
+ product;
+
+
#### Windowed Functions
1. Write a query that selects from the customer_purchases table and numbers each customer’s visits to the farmer’s market (labeling each market date with a different number). Each customer’s first visit is labeled 1, second visit is labeled 2, etc.
@@ -95,12 +108,85 @@ You can either display all rows in the customer_purchases table, with the counte
**HINT**: One of these approaches uses ROW_NUMBER() and one uses DENSE_RANK().
+SELECT
+ customer_id,
+ market_date,
+ -- DENSE_RANK() assigns the same rank to ties (i.e., multiple purchases on the same date)
+ DENSE_RANK() OVER (
+ -- Restart the numbering for each unique customer
+ PARTITION BY customer_id
+ -- Order by the date to ensure the ranking is chronological
+ ORDER BY market_date ASC
+ ) AS visit_number
+FROM
+ customer_purchases
+ORDER BY
+ customer_id, market_date;
+
+
2. Reverse the numbering of the query from a part so each customer’s most recent visit is labeled 1, then write another query that uses this one as a subquery (or temp table) and filters the results to only the customer’s most recent visit.
+Part 1: Reverse Numbering (Most Recent Visit is Rank 1)
+This query ranks all of a customer's purchases based on market_date, with the most recent date receiving a rank of 1.
+SELECT
+ customer_id,
+ market_date,
+ DENSE_RANK() OVER (
+ PARTITION BY customer_id
+ ORDER BY market_date DESC
+ ) AS visit_number_desc
+FROM
+ customer_purchases
+ORDER BY
+ customer_id,
+ market_date DESC;
+
+Part 2: Filter to Only the Most Recent Visit (Using a Subquery)
+This query uses the logic from Part 1 within a subquery (or derived table, aliased as ranked_visits) and filters the results to only include those rows where the rank is 1.
+SELECT
+ ranked_visits.customer_id,
+ ranked_visits.market_date
+FROM (
+ SELECT
+ customer_id,
+ market_date,
+ DENSE_RANK() OVER (
+ PARTITION BY customer_id
+ ORDER BY market_date DESC
+ ) AS visit_number_desc
+ FROM
+ customer_purchases
+) AS ranked_visits
+WHERE
+ ranked_visits.visit_number_desc = 1
+ORDER BY
+ ranked_visits.customer_id,
+ ranked_visits.market_date DESC;
+
+
+
+
+
3. Using a COUNT() window function, include a value along with each row of the customer_purchases table that indicates how many different times that customer has purchased that product_id.
-
+SELECT
+ customer_id,
+ product_id,
+ market_date,
+ COUNT(product_id) OVER (
+ -- Partition by customer AND product to count purchases of this specific product by this specific customer
+ PARTITION BY customer_id, product_id
+ ) AS times_purchased_this_product
+FROM
+ customer_purchases
+ORDER BY
+ customer_id,
+ product_id,
+ market_date;
+
+
#### String manipulations
1. Some product names in the product table have descriptions like "Jar" or "Organic". These are separated from the product name with a hyphen. Create a column using SUBSTR (and a couple of other commands) that captures these, but is otherwise NULL. Remove any trailing or leading whitespaces. Don't just use a case statement for each product!
@@ -110,10 +196,45 @@ You can either display all rows in the customer_purchases table, with the counte
**HINT**: you might need to use INSTR(product_name,'-') to find the hyphens. INSTR will help split the column.
+SELECT
+ product_name,
+ -- Use INSTR to find the position of the hyphen. If found (>0), extract the substring starting
+ -- immediately after the hyphen (+ 1) all the way to the end of the string.
+ -- TRIM() removes leading/trailing spaces from the extracted description.
+ CASE
+ WHEN INSTR(product_name, '-') > 0
+ THEN TRIM(
+ SUBSTR(
+ product_name,
+ INSTR(product_name, '-') + 1
+ )
+ )
+ ELSE NULL -- Return NULL if no hyphen is present
+ END AS product_description
+FROM
+ product;
+
+
2. Filter the query to show any product_size value that contain a number with REGEXP.
-
+SELECT product_name,
+ CASE
+ WHEN INSTR(product_name, '-') > 0
+ THEN TRIM(
+ SUBSTR(
+ product_name,
+ INSTR(product_name, '-') + 1
+ )
+ )
+ ELSE NULL -- Return NULL if no hyphen is present
+ END AS product_description
+FROM product
+/* Filter the query to show any product_size value that contain a number with REGEXP. */
+WHERE product_size REGEXP '[0-9]';
+
+
#### UNION
1. Using a UNION, write a query that displays the market dates with the highest and lowest total sales.
@@ -121,6 +242,45 @@ You can either display all rows in the customer_purchases table, with the counte
***
+WITH daily_sales AS (
+ SELECT
+ market_date,
+ SUM(quantity * cost_to_customer_per_qty) AS total_sales
+ FROM
+ customer_purchases
+ GROUP BY
+ market_date
+),
+ranked_sales AS (
+ SELECT
+ market_date,
+ total_sales,
+ DENSE_RANK() OVER (ORDER BY total_sales DESC) AS best_rank,
+ DENSE_RANK() OVER (ORDER BY total_sales ASC) AS worst_rank
+ FROM
+ daily_sales
+)
+SELECT
+ market_date,
+ total_sales,
+ 'Best day' AS day_type
+FROM
+ ranked_sales
+WHERE
+ best_rank = 1
+
+UNION
+
+SELECT
+ market_date,
+ total_sales,
+ 'Worst day' AS day_type
+FROM
+ ranked_sales
+WHERE
+ worst_rank = 1;
+
+
## Section 3:
You can start this section following *session 5*.
@@ -139,6 +299,39 @@ Steps to complete this part of the assignment:
-
+WITH vendor_products AS (
+ -- Distinct vendor–product pairs with their price
+ SELECT DISTINCT
+ v.vendor_name,
+ p.product_name,
+ vi.original_price
+ FROM vendor_inventory vi
+ JOIN vendor v ON vi.vendor_id = v.vendor_id
+ JOIN product p ON vi.product_id = p.product_id
+),
+vp_customer_sales AS (
+ -- Cross join each vendor–product with every customer
+ -- Each customer buys 5 units of that product
+ SELECT
+ vp.vendor_name,
+ vp.product_name,
+ 5 * vp.original_price AS sale_amount
+ FROM vendor_products vp
+ CROSS JOIN customer c
+)
+SELECT
+ vendor_name,
+ product_name,
+ SUM(sale_amount) AS total_revenue
+FROM vp_customer_sales
+GROUP BY
+ vendor_name,
+ product_name
+ORDER BY
+ vendor_name,
+ product_name;
+
+
#### INSERT
1. Create a new table "product_units". This table will contain only products where the `product_qty_type = 'unit'`. It should use all of the columns from the product table, as well as a new column for the `CURRENT_TIMESTAMP`. Name the timestamp column `snapshot_timestamp`.
@@ -146,6 +339,29 @@ Steps to complete this part of the assignment:
-
+/* \ STEP 1: CREATE and POPULATE the initial product_units table */
+-- 1.1 Drop the table if it exists to avoid errors
+DROP TABLE IF EXISTS product_units;
+-- 1.2 Create the table with explicit column types (including TEXT for DATETIME in SQLite)
+CREATE TABLE product_units ( product_id INTEGER, product_name TEXT, product_size TEXT, product_category_id INTEGER, product_qty_type TEXT, snapshot_timestamp TEXT -- Using TEXT for SQLite datetime storage
+);
+-- 1.3 Insert all products where product_qty_type = 'unit' from the source table
+INSERT INTO product_units ( product_id, product_name, product_size, product_category_id, product_qty_type, snapshot_timestamp -- Explicitly include the timestamp column
+)
+SELECT product_id, product_name, product_size, product_category_id, product_qty_type, DATETIME('now') -- Generate and use the current timestamp
+FROM product
+WHERE product_qty_type = 'unit';
+
+/*2. Using `INSERT`, add a new row to the product_units table (with an updated timestamp).
+This can be any product you desire (e.g. add another record for Apple Pie). */
+
+/* STEP 2: ADD A NEW ROW */
+-- 2.1 Insert the desired product (e.g., Apple Pie) with a new timestamp
+INSERT INTO product_units ( product_id, product_name, product_size, product_category_id, product_qty_type, snapshot_timestamp
+)
+VALUES ( 9998, -- Unique ID 'Large Apple Pie', 'large', 7, -- Example category ID 'unit', DATETIME('now')
+);
+
#### DELETE
1. Delete the older record for the whatever product you added.
@@ -153,6 +369,15 @@ Steps to complete this part of the assignment:
-
+DELETE FROM product_units
+WHERE product_name = 'Large Apple Pie'
+ AND snapshot_timestamp < (
+ SELECT MAX(snapshot_timestamp)
+ FROM product_units
+ WHERE product_name = 'Large Apple Pie'
+ );
+
+
#### UPDATE
1. We want to add the current_quantity to the product_units table. First, add a new column, `current_quantity` to the table using the following syntax.
```
@@ -165,6 +390,23 @@ Then, using `UPDATE`, change the current_quantity equal to the **last** `quantit
**HINT**: This one is pretty hard. First, determine how to get the "last" quantity per product. Second, coalesce null values to 0 (if you don't have null values, figure out how to rearrange your query so you do.) Third, `SET current_quantity = (...your select statement...)`, remembering that WHERE can only accommodate one column. Finally, make sure you have a WHERE statement to update the right row, you'll need to use `product_units.product_id` to refer to the correct row within the product_units table. When you have all of these components, you can run the update statement.
***
+/*1. Add the new column to product_units*/
+ALTER TABLE product_units
+ADD COLUMN current_quantity INT;
+
+/*2. Update current_quantity to the last (most recent) quantity per product*/
+UPDATE product_units
+SET current_quantity = COALESCE(
+ (
+ SELECT vi.quantity
+ FROM vendor_inventory AS vi
+ WHERE vi.product_id = product_units.product_id
+ ORDER BY vi.market_date DESC
+ LIMIT 1
+ ),
+ 0
+);
+
## Section 4:
You can start this section anytime.
@@ -183,5 +425,15 @@ Consider, for example, concepts of labour, bias, LLM proliferation, moderating c
```
-Your thoughts...
+The core ethical issue in this story is that “AI” is built on invisible people, then sold back to us as magic.
+
+First, labor. ImageNet, WordNet, the Brown Corpus and similar resources are thousands of hours of human work: Turkers clicking labels for cents, grad students tagging parts of speech, linguists deciding which words count as synonyms. That labor is boring, underpaid, and mostly uncredited, while a tiny group of star researchers and companies capture the prestige and profit. Ethically, that is extraction, not “automation.”
+
+Second, bias and classification. The hot-dog app is harmless, but the same pipeline is used to classify people. WordNet’s synsets and ImageNet’s “person” categories encode very specific cultural views about which labels exist and who fits them. Projects like ImageNet Roulette made visible how this turns into insulting or harmful tags on real faces. Those are not random bugs. They are the politics of race, gender, class and “normality” frozen into training data.
+
+Third, deployment into social systems. These models now sit behind facial recognition for police, content moderation and recommendation algorithms. When the underlying labels and taxonomies are skewed, all of that skew shows up in who gets flagged, sorted or excluded in the real world.
+Finally, even though the essay focuses on vision, the same pattern holds for LLMs. Giant text corpora, human raters, moderators and annotators are doing constant cleanup so that the model looks “smart” and “safe” on the surface.
+
+So the ethical questions are: whose work is being used, on what terms, who defines the categories, and who gets hurt when those categories leak into policing, hiring or everyday platform use. Once you see that neural nets are people all the way down, it is hard to pretend this is just neutral technology.
+
```
diff --git a/02_activities/assignments/DC_Cohort/assignment2.sql b/02_activities/assignments/DC_Cohort/assignment2.sql
index d6a10dbe0..5bf95aa97 100644
--- a/02_activities/assignments/DC_Cohort/assignment2.sql
+++ b/02_activities/assignments/DC_Cohort/assignment2.sql
@@ -21,6 +21,10 @@ The `||` values concatenate the columns into strings.
Edit the appropriate columns -- you're making two edits -- and the NULL rows will be fixed.
All the other rows will remain the same. */
+SELECT
+ product_name | ', ' | COALESCE(product_size, '') | ' (' | COALESCE(product_qty_type, 'unit') | ')'
+FROM
+ product;
--Windowed Functions
@@ -33,6 +37,20 @@ each new market date for each customer, or select only the unique market dates p
(without purchase details) and number those visits.
HINT: One of these approaches uses ROW_NUMBER() and one uses DENSE_RANK(). */
+SELECT
+ customer_id,
+ market_date,
+ -- DENSE_RANK() assigns the same rank to ties (i.e., multiple purchases on the same date)
+ DENSE_RANK() OVER (
+ -- Restart the numbering for each unique customer
+ PARTITION BY customer_id
+ -- Order by the date to ensure the ranking is chronological
+ ORDER BY market_date ASC
+ ) AS visit_number
+FROM
+ customer_purchases
+ORDER BY
+ customer_id, market_date;
/* 2. Reverse the numbering of the query from a part so each customer’s most recent visit is labeled 1,
@@ -40,10 +58,61 @@ then write another query that uses this one as a subquery (or temp table) and fi
only the customer’s most recent visit. */
+/*Part 1: Reverse Numbering (Most Recent Visit is Rank 1)
+This query ranks all of a customer's purchases based on market_date, with the most recent date receiving a rank of 1.*/
+SELECT
+ customer_id,
+ market_date,
+ DENSE_RANK() OVER (
+ PARTITION BY customer_id
+ ORDER BY market_date DESC
+ ) AS visit_number_desc
+FROM
+ customer_purchases
+ORDER BY
+ customer_id,
+ market_date DESC;
+
+/*Part 2: Filter to Only the Most Recent Visit (Using a Subquery)
+This query uses the logic from Part 1 within a subquery (or derived table, aliased as ranked_visits) and filters the results to only include those rows where the rank is 1.*/
+SELECT
+ ranked_visits.customer_id,
+ ranked_visits.market_date
+FROM (
+ SELECT
+ customer_id,
+ market_date,
+ DENSE_RANK() OVER (
+ PARTITION BY customer_id
+ ORDER BY market_date DESC
+ ) AS visit_number_desc
+ FROM
+ customer_purchases
+) AS ranked_visits
+WHERE
+ ranked_visits.visit_number_desc = 1
+ORDER BY
+ ranked_visits.customer_id,
+ ranked_visits.market_date DESC;
+
/* 3. Using a COUNT() window function, include a value along with each row of the
customer_purchases table that indicates how many different times that customer has purchased that product_id. */
+SELECT
+ customer_id,
+ product_id,
+ market_date,
+ COUNT(product_id) OVER (
+ -- Partition by customer AND product to count purchases of this specific product by this specific customer
+ PARTITION BY customer_id, product_id
+ ) AS times_purchased_this_product
+FROM
+ customer_purchases
+ORDER BY
+ customer_id,
+ product_id,
+ market_date;
-- String manipulations
@@ -58,10 +127,44 @@ Remove any trailing or leading whitespaces. Don't just use a case statement for
Hint: you might need to use INSTR(product_name,'-') to find the hyphens. INSTR will help split the column. */
+SELECT
+ product_name,
+ -- Use INSTR to find the position of the hyphen. If found (>0), extract the substring starting
+ -- immediately after the hyphen (+ 1) all the way to the end of the string.
+ -- TRIM() removes leading/trailing spaces from the extracted description.
+ CASE
+ WHEN INSTR(product_name, '-') > 0
+ THEN TRIM(
+ SUBSTR(
+ product_name,
+ INSTR(product_name, '-') + 1
+ )
+ )
+ ELSE NULL -- Return NULL if no hyphen is present
+ END AS product_description
+FROM
+ product;
/* 2. Filter the query to show any product_size value that contain a number with REGEXP. */
+SELECT product_name,
+ CASE
+ WHEN INSTR(product_name, '-') > 0
+ THEN TRIM(
+ SUBSTR(
+ product_name,
+ INSTR(product_name, '-') + 1
+ )
+ )
+ ELSE NULL -- Return NULL if no hyphen is present
+ END AS product_description
+FROM product
+/* Filter the query to show any product_size value that contain a number with REGEXP. */
+WHERE product_size REGEXP '[0-9]';
+
+
+
-- UNION
@@ -74,6 +177,43 @@ HINT: There are a possibly a few ways to do this query, but if you're struggling
3) Query the second temp table twice, once for the best day, once for the worst day,
with a UNION binding them. */
+WITH daily_sales AS (
+ SELECT
+ market_date,
+ SUM(quantity * cost_to_customer_per_qty) AS total_sales
+ FROM
+ customer_purchases
+ GROUP BY
+ market_date
+),
+ranked_sales AS (
+ SELECT
+ market_date,
+ total_sales,
+ DENSE_RANK() OVER (ORDER BY total_sales DESC) AS best_rank,
+ DENSE_RANK() OVER (ORDER BY total_sales ASC) AS worst_rank
+ FROM
+ daily_sales
+)
+SELECT
+ market_date,
+ total_sales,
+ 'Best day' AS day_type
+FROM
+ ranked_sales
+WHERE
+ best_rank = 1
+
+UNION
+
+SELECT
+ market_date,
+ total_sales,
+ 'Worst day' AS day_type
+FROM
+ ranked_sales
+WHERE
+ worst_rank = 1;
@@ -90,6 +230,37 @@ Think a bit about the row counts: how many distinct vendors, product names are t
How many customers are there (y).
Before your final group by you should have the product of those two queries (x*y). */
+WITH vendor_products AS (
+ -- Distinct vendor–product pairs with their price
+ SELECT DISTINCT
+ v.vendor_name,
+ p.product_name,
+ vi.original_price
+ FROM vendor_inventory vi
+ JOIN vendor v ON vi.vendor_id = v.vendor_id
+ JOIN product p ON vi.product_id = p.product_id
+),
+vp_customer_sales AS (
+ -- Cross join each vendor–product with every customer
+ -- Each customer buys 5 units of that product
+ SELECT
+ vp.vendor_name,
+ vp.product_name,
+ 5 * vp.original_price AS sale_amount
+ FROM vendor_products vp
+ CROSS JOIN customer c
+)
+SELECT
+ vendor_name,
+ product_name,
+ SUM(sale_amount) AS total_revenue
+FROM vp_customer_sales
+GROUP BY
+ vendor_name,
+ product_name
+ORDER BY
+ vendor_name,
+ product_name;
-- INSERT
@@ -98,18 +269,41 @@ This table will contain only products where the `product_qty_type = 'unit'`.
It should use all of the columns from the product table, as well as a new column for the `CURRENT_TIMESTAMP`.
Name the timestamp column `snapshot_timestamp`. */
-
+/* \ STEP 1: CREATE and POPULATE the initial product_units table */
+-- 1.1 Drop the table if it exists to avoid errors
+DROP TABLE IF EXISTS product_units;
+-- 1.2 Create the table with explicit column types (including TEXT for DATETIME in SQLite)
+CREATE TABLE product_units ( product_id INTEGER, product_name TEXT, product_size TEXT, product_category_id INTEGER, product_qty_type TEXT, snapshot_timestamp TEXT -- Using TEXT for SQLite datetime storage
+);
+-- 1.3 Insert all products where product_qty_type = 'unit' from the source table
+INSERT INTO product_units ( product_id, product_name, product_size, product_category_id, product_qty_type, snapshot_timestamp -- Explicitly include the timestamp column
+)
+SELECT product_id, product_name, product_size, product_category_id, product_qty_type, DATETIME('now') -- Generate and use the current timestamp
+FROM product
+WHERE product_qty_type = 'unit';
/*2. Using `INSERT`, add a new row to the product_units table (with an updated timestamp).
This can be any product you desire (e.g. add another record for Apple Pie). */
-
+/* STEP 2: ADD A NEW ROW */
+-- 2.1 Insert the desired product (e.g., Apple Pie) with a new timestamp
+INSERT INTO product_units ( product_id, product_name, product_size, product_category_id, product_qty_type, snapshot_timestamp
+)
+VALUES ( 9998, -- Unique ID 'Large Apple Pie', 'large', 7, -- Example category ID 'unit', DATETIME('now')
+);
-- DELETE
/* 1. Delete the older record for the whatever product you added.
HINT: If you don't specify a WHERE clause, you are going to have a bad time.*/
+DELETE FROM product_units
+WHERE product_name = 'Large Apple Pie'
+ AND snapshot_timestamp < (
+ SELECT MAX(snapshot_timestamp)
+ FROM product_units
+ WHERE product_name = 'Large Apple Pie'
+ );
-- UPDATE
@@ -129,6 +323,22 @@ Finally, make sure you have a WHERE statement to update the right row,
you'll need to use product_units.product_id to refer to the correct row within the product_units table.
When you have all of these components, you can run the update statement. */
+/*1. Add the new column to product_units*/
+ALTER TABLE product_units
+ADD COLUMN current_quantity INT;
+
+/*2. Update current_quantity to the last (most recent) quantity per product*/
+UPDATE product_units
+SET current_quantity = COALESCE(
+ (
+ SELECT vi.quantity
+ FROM vendor_inventory AS vi
+ WHERE vi.product_id = product_units.product_id
+ ORDER BY vi.market_date DESC
+ LIMIT 1
+ ),
+ 0
+);
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