M-Howorucha · M-Howorucha · Mar 31, 2026 · Mar 31, 2026
diff --git a/02_activities/assignments/DC_Cohort/Assignment1.md b/02_activities/assignments/DC_Cohort/Assignment1.md
@@ -209,5 +209,5 @@ Consider, for example, concepts of fariness, inequality, social structures, marg
 
 
 ```
-Your thoughts...
+My day to day work involves data systems primarily within the field of experimentally derived atmospheric observations. Although these datasets are intended to be entirely unbiased, with respect to fairness, inequality, and social structures, there are still avenues for such concepts to impact the data and the downstream processes they inform. For example, global datasets of atmospheric pollutants are often concentrated in regions of the world with high economic and political influence such as North America and Europe. These also tend to be the regions least impacted by the detrimental health impacts of prolonged air quality hazards, due in part to reductions in pollutants, informed by these observations and air quality warnings, again, informed by observations. Conversely, developing nations, particularly in the global south, are disproportionally impacted by air quality risks, while simultaneously having fewer, and less reliable air quality warnings, due to the limited observational datasets available. Similarly, and closer to home, the Canadian high arctic is a region of the world were the inhabitants (primarily indigenous peoples) are simultaneously impacted by extreme weather events and less reliable weather forecasts. This is almost entirely due to the population extremes in Canada driving economic and technological progress further south, leaving the higher latitude regions to lag behind. Although this is simply a result of the distribution of resources, it nonetheless results in a marginalized group in Canada to be negatively impacted. There are ongoing efforts in the Canadian atmospheric research community to address this issue, but it is ongoing and will likely remain for many years to come, as well as become more significant under future climate change scenarios. 
 ```
diff --git a/02_activities/assignments/DC_Cohort/assignment1.sql b/02_activities/assignments/DC_Cohort/assignment1.sql
@@ -7,8 +7,7 @@
 /* 1. Write a query that returns everything in the customer table. */
 --QUERY 1
 
-
-
+Select * FROM customer;
 
 --END QUERY
 
@@ -17,8 +16,10 @@
 sorted by customer_last_name, then customer_first_ name. */
 --QUERY 2
 
+SELECT * FROM customer
 
-
+ORDER BY customer_last_name ASC, customer_first_name ASC
+LIMIT 10;
 
 --END QUERY
 
@@ -28,9 +29,10 @@ sorted by customer_last_name, then customer_first_ name. */
 Limit to 25 rows of output. */
 --QUERY 3
 
-
-
-
+SELECT * FROM customer_purchases
+WHERE product_id = 4
+OR product_id = 9
+LIMIT 25;
 --END QUERY
 
 
@@ -43,8 +45,9 @@ Limit to 25 rows of output.
 */
 --QUERY 4
 
-
-
+SELECT *, (quantity*cost_to_customer_per_qty) AS price FROM customer_purchases
+WHERE customer_id BETWEEN 8 AND 10
+LIMIT 25;
 
 --END QUERY
 
@@ -56,8 +59,14 @@ columns and add a column called prod_qty_type_condensed that displays the word
 if the product_qty_type is “unit,” and otherwise displays the word “bulk.” */
 --QUERY 5
 
+SELECT product_id, product_name, --product_qty_type,
+CASE 
+	WHEN product_qty_type = 'unit' 
+	THEN 'unit'
+	ELSE 'bulk'
+END AS prod_qty_type_condensed
 
-
+FROM product;
 
 --END QUERY
 
@@ -67,7 +76,19 @@ add a column to the previous query called pepper_flag that outputs a 1 if the pr
 contains the word “pepper” (regardless of capitalization), and otherwise outputs 0. */
 --QUERY 6
 
+SELECT product_id, product_name --product_qty_type,
+,CASE 
+	WHEN product_qty_type = 'unit' 
+	THEN 'unit'
+	ELSE 'bulk'
+END AS prod_qty_type_condensed
 
+,CASE 
+	WHEN product_name LIKE '%pepper%' THEN 1
+	ELSE 0
+END AS pepper_flag
+
+FROM product;
 
 
 --END QUERY
@@ -79,9 +100,17 @@ vendor_id field they both have in common, and sorts the result by market_date, t
 Limit to 24 rows of output. */
 --QUERY 7
 
+SELECT * FROM vendor v
 
+INNER JOIN vendor_booth_assignments vba 
+    ON v.vendor_id = vba.vendor_id
 
+ORDER BY 
+vba.market_date ASC, 
+v.vendor_name ASC
 
+LIMIT 24;
+
 --END QUERY
 
 
@@ -93,8 +122,16 @@ Limit to 24 rows of output. */
 at the farmer’s market by counting the vendor booth assignments per vendor_id. */
 --QUERY 8
 
-
-
+SELECT 
+    vendor_id, 
+    COUNT(*) AS vendors_count
+FROM 
+    vendor_booth_assignments
+GROUP BY 
+    vendor_id
+ORDER BY 
+    vendors_count DESC;
+
 
 --END QUERY
 
@@ -106,8 +143,23 @@ 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. */
 --QUERY 9
 
+SELECT 
+customer.customer_id,
+customer.customer_first_name,
+customer.customer_last_name,
+SUM(customer_purchases.quantity * customer_purchases.cost_to_customer_per_qty) AS total_spend
 
+FROM customer_purchases
 
+INNER JOIN 
+	customer
+	ON customer_purchases.customer_id = customer.customer_id
+
+GROUP BY customer.customer_id
+HAVING total_spend > 2000
+ORDER BY 
+    customer.customer_last_name,
+	customer.customer_first_name;
 
 --END QUERY
 
@@ -125,7 +177,14 @@ VALUES(col1,col2,col3,col4,col5)
 */
 --QUERY 10
 
+-- if a table named temp.new_vendor exists, delete it, otherwise do NOTHING
+DROP TABLE IF EXISTS temp.new_vendor;
+CREATE TABLE temp.new_vendor AS
+SELECT *
+FROM vendor;
 
+INSERT INTO temp.new_vendor
+VALUES(10,'Thomass Superfood Store', 'Fresh Focused', 'Thomas', 'Rosenthal');
 
 
 --END QUERY

diff --git a/02_activities/assignments/DC_Cohort/section1_logical_data_model.png b/02_activities/assignments/DC_Cohort/section1_logical_data_model.png
-Original file line number
+Diff line change
@@ Expand Up @@
     ```
-    Your thoughts...
+    My day to day work involves data systems primarily within the field of experimentally derived atmospheric observations. Although these datasets are intended to be entirely unbiased, with respect to fairness, inequality, and social structures, there are still avenues for such concepts to impact the data and the downstream processes they inform. For example, global datasets of atmospheric pollutants are often concentrated in regions of the world with high economic and political influence such as North America and Europe. These also tend to be the regions least impacted by the detrimental health impacts of prolonged air quality hazards, due in part to reductions in pollutants, informed by these observations and air quality warnings, again, informed by observations. Conversely, developing nations, particularly in the global south, are disproportionally impacted by air quality risks, while simultaneously having fewer, and less reliable air quality warnings, due to the limited observational datasets available. Similarly, and closer to home, the Canadian high arctic is a region of the world were the inhabitants (primarily indigenous peoples) are simultaneously impacted by extreme weather events and less reliable weather forecasts. This is almost entirely due to the population extremes in Canada driving economic and technological progress further south, leaving the higher latitude regions to lag behind. Although this is simply a result of the distribution of resources, it nonetheless results in a marginalized group in Canada to be negatively impacted. There are ongoing efforts in the Canadian atmospheric research community to address this issue, but it is ongoing and will likely remain for many years to come, as well as become more significant under future climate change scenarios.
     ```