UofT-DSI | SQL - Assignment - TWO

02_activities/assignments/Cohort_8/Assignment1.md

@@ -3,7 +3,7 @@
 🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.
 
 #### Submission Parameters:
-* Submission Due Date: `August 10, 2025`
+* Submission Due Date: `November 17, 2025`
 * Weight: 30% of total grade
 * The branch name for your repo should be: `assignment-one`
 * What to submit for this assignment:

02_activities/assignments/Cohort_8/Assignment2.md

@@ -3,7 +3,7 @@
 🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.
 
 #### Submission Parameters:
-* Submission Due Date: `August 17, 2025`
+* Submission Due Date: `November 24, 2025`
 * Weight: 70% of total grade
 * The branch name for your repo should be: `assignment-two`
 * What to submit for this assignment:

02_activities/assignments/DC_Cohort/Assignment2.md

@@ -54,7 +54,9 @@ 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...
+Type 1 Slowly Changing Dimension is used when the history of the customer’s address is not important. In this approach, the customer’s address record will be overwritten whenever a new address is entered. The store can use this method if they do not need to keep the old address for purposes such as audit, analytics, fraud management, or marketing personalization. This approach is suitable when the store prefers simple processing, requires less storage, and only needs a basic system.
+
+If the store requires a more advanced system and needs to keep historical address information for audit, analytics, fraud management, or marketing personalization, then the store should use Type 2. In Type 2, the old address data is retained, and a new record (row) is created every time the customer changes address. This means one customer can have multiple address records stored over time.
 ```
 
 ***
@@ -183,5 +185,9 @@ Consider, for example, concepts of labour, bias, LLM proliferation, moderating c
 
 
 ```
-Your thoughts...
+Machine learning and artificial intelligence (AI) are relatively new concepts in human history. Because of this, they exist in a grey area of society where there are still no clear rules or regulations to guide or monitor their development. The capabilities of machine learning and AI are highly attractive, and every country, corporation, and researcher wants to gain a strong position in this fast-growing field. In the race to become a major player, many may overlook or ignore the ethical issues involved in developing these technologies in order to stay competitive in the market.
+
+The author of the article clearly highlights these ethical concerns, such as hidden labour, bias in data and classification, and accountability and power. Many AI systems depend on thousands of low-paid workers who manually label data to train algorithms. Their work is often invisible, raising concerns about fairness and exploitation in the global tech industry. In addition, the data used to train AI models often carries social and cultural biases — related to gender, race, or culture — which can lead to unfair or discriminatory outcomes.
+
+Finally, there is the issue of accountability, as it is often unclear who should be responsible when AI systems make mistakes or cause harm — the developers, companies, or the technology itself. This lack of transparency and regulation creates serious ethical challenges. Therefore, while AI offers great potential, it is crucial to ensure that its development and application are guided by strong ethical principles and social responsibility.
 ```

02_activities/assignments/DC_Cohort/assignment2.sql

@@ -20,6 +20,12 @@ 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') || ')' AS product_details
+FROM product;
+
 
 
 --Windowed Functions
@@ -32,17 +38,68 @@ 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,
+    ROW_NUMBER() OVER (
+        PARTITION BY customer_id 
+        ORDER BY market_date
+    ) AS visit_number
+FROM customer_purchases;
 
+SELECT 
+    customer_id,
+    market_date,
+    DENSE_RANK() OVER (
+        PARTITION BY customer_id 
+        ORDER BY market_date
+    ) AS visit_number
+FROM customer_purchases;
 
 /* 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. */
 
-
+SELECT 
+    customer_id,
+    market_date,
+    DENSE_RANK() OVER (
+        PARTITION BY customer_id 
+        ORDER BY market_date DESC
+    ) AS visit_number
+FROM (
+	SELECT DISTINCT
+			customer_id, 
+			market_date 
+		FROM
+	customer_purchases);
+
+ SELECT*
+	FROM( 
+		SELECT
+        customer_id,
+        market_date,
+        DENSE_RANK() OVER (PARTITION BY customer_id 
+            ORDER BY market_date DESC)
+        AS visit_number
+    FROM (
+        SELECT DISTINCT customer_id, market_date
+        FROM customer_purchases)
+	) as visit_ranking
+
+WHERE visit_number = 1;
 
 /* 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(*) OVER (
+        PARTITION BY customer_id, product_id
+    ) AS purchase_count
+FROM customer_purchases;
 
 
 -- String manipulations
@@ -57,11 +114,30 @@ 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,
+    TRIM(
+        SUBSTR(
+            product_name,
+            INSTR(product_name, '-') + 1 
+        )
+    ) AS product_description
+FROM product;
+
+SELECT
+    product_name,
+    CASE 
+        WHEN INSTR(product_name, '-') > 0 THEN
+            TRIM(SUBSTR(product_name, INSTR(product_name, '-') + 1))
+        ELSE NULL
+    END AS product_description
+FROM product;
 
 /* 2. Filter the query to show any product_size value that contain a number with REGEXP. */
 
-
+SELECT *
+FROM product
+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.
@@ -73,8 +149,37 @@ 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. */
 
+--1) Create a CTE/Temp Table to find sales values grouped dates; 
+
+WITH sales_per_day AS (
+    SELECT 
+        market_date,
+        SUM(quantity * cost_to_customer_per_qty) AS total_sales
+    FROM customer_purchases
+    GROUP BY market_date
+)
+
+--2) Create another CTE/Temp table with a rank windowed function on the previous query to create "best day" and "worst day"; 
 
+, ranked_sales AS (
+    SELECT
+        market_date,
+        total_sales,
+        RANK() OVER (ORDER BY total_sales DESC) AS best_rank,
+        RANK() OVER (ORDER BY total_sales ASC) AS worst_rank
+    FROM sales_per_day
+)
+-- 3) Query the second temp table twice, once for the best day, once for the worst day, with a UNION binding them. */
 
+SELECT market_date, total_sales, 'Highest' AS type
+FROM ranked_sales
+WHERE best_rank = 1
+
+UNION
+
+SELECT market_date, total_sales, 'Lowest' AS type
+FROM ranked_sales
+WHERE worst_rank = 1;
 
 /* SECTION 3 */
 
@@ -89,6 +194,18 @@ 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).  */
 
+SELECT 
+    v.vendor_name,
+    p.product_name,
+	vi.original_price,
+    SUM(5 * vi.original_price) AS total_potential_revenue
+FROM vendor_inventory vi
+JOIN vendor v ON vi.vendor_id = v.vendor_id
+JOIN product p ON vi.product_id = p.product_id
+CROSS JOIN customer c
+GROUP BY v.vendor_name, p.product_name
+ORDER BY v.vendor_name, p.product_name;
+
 
 
 -- INSERT
@@ -97,19 +214,45 @@ 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`. */
 
-
+CREATE TABLE product_units AS
+SELECT 
+    *,
+    CURRENT_TIMESTAMP AS snapshot_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). */
 
-
+INSERT INTO product_units (
+    product_id,
+	product_name,
+	product_size,
+	product_category_id,
+    product_qty_type,
+	snapshot_timestamp
+)
+VALUES (
+    24,                   
+    'Apple Pie',           
+    'small',              
+	'Freshly Prepared Food',
+    'unit',                
+    CURRENT_TIMESTAMP      
+);
 
 -- 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 = 'Apple Pie'
+  AND snapshot_timestamp < (
+      SELECT MAX(snapshot_timestamp)
+      FROM product_units
+      WHERE product_name = 'Apple Pie'
+);
 
 -- UPDATE
 /* 1.We want to add the current_quantity to the product_units table. 
@@ -128,6 +271,15 @@ 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. */
 
+--First, add a new column, current_quantity to the table using the following syntax.
 
+ALTER TABLE product_units
+ADD current_quantity INT;
 
-
+UPDATE product_units AS pu
+SET current_quantity = (
+    SELECT COALESCE(MAX(vi.quantity), 0)
+    FROM vendor_inventory vi
+    WHERE vi.product_id = pu.product_id
+)
+WHERE pu.product_qty_type = 'unit';

03_instructional_team/markdown_slides/slides_01.md

@@ -39,17 +39,26 @@ $ echo "Data Sciences Institute"
 
 ---
 
-# About Us (Anjali)
-
-- Holds a Bachelor’s in Electrical Engineering from the University of Mumbai, India
-- IT professional with 13 years of experience in software development, analysis, and design using a variety of programming languages and platforms for diverse clients
-- DSI Cohort 3 and LS for subsequent cohorts
-- Experienced in Agile environments, mentoring, and fostering collaborative, solution-driven work cultures.
-- Works as an Emergency Early Childhood Educator and Education Assistant, supporting students in Elementary (K–5) public school, Peel District School Board
-- Passionate about art, crocheting, and gardening; and actively volunteers with Ecosource’s Community Cultivator Program, growing food for local food banks
+# About Us (Edward)
+
+- Graduated from the Master of Science in Applied Computing program at UofT
+- Currently working as a Research Analyst at the University Health Network
+- Will start PhD in Medical Biophysics at UofT in September
+- Have worked on creating course material for data science and machine learning at UofT
 - Hobbies: Gaming, Crochet, Archery
 
-![bg right:35% w:350](./images/01_anjali.png)
+![bg right:35% w:350](./images/01_edward.png)
+
+---
+
+# About Us (Moniz)
+
+- Master in Biomedical Engineering 🎓
+- Project and Data Coordinator in Healthcare setting 🏥
+- DSI cohort 3 📈
+- Hobbies: camping 🏕️ travelling ✈️ and see the world
+
+![bg right:35% w:350](./images/01_moniz.png)
 
 ---
 
@@ -66,18 +75,6 @@ $ echo "Data Sciences Institute"
 
 ---
 
-# About Us (Sergii)
-
-- Solution Architect with a PhD in Engineering
-- Have national and international awards
-- DSI cohort 5
-- Over 25 years in the IT industry across diverse domains
-- Proficient in multiple programming languages
-
-![bg right:35% w:350](./images/01_sergii.png)
-
----
-
 
 # Welcome / Course Content
 

03_instructional_team/markdown_slides/slides_03.md

@@ -48,7 +48,7 @@ For example, a query wanting to know the number of days in each month:
     ,months
 
     FROM calendar
-    GROUP by months
+    GROUP BY months
 ```
 
 - `GROUP BY` comes after a `WHERE` clause
@@ -71,7 +71,7 @@ For example, a query wanting to know the number of days in each month:
     ,years
 
     FROM calendar
-    GROUP by years
+    GROUP BY years
 ```
 
 ---
@@ -117,7 +117,7 @@ For example, a query wanting to know the number of days in each month:
 
 - `SUM` performs the sum total of any numeric column
   - Be wary, SQLite may be more permissive for columns with numbers; it's best practice to coerce (`CAST`) these values into numbers before summing to be certain of their validity
-    - e.g. `CAST(SUM(column1) AS INTEGER) AS column1`
+    - e.g. `SUM(CAST(column1 AS INTEGER)) AS column1`
 - SUM can accommodate multiple columns using the plus `+` operator
   - e.g. `SUM(column1 + column2)`