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22 changes: 16 additions & 6 deletions 02_activities/assignments/DC_Cohort/Assignment2.md
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Expand Up @@ -14,10 +14,10 @@
* Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.

Checklist:
- [ ] Create a branch called `assignment-two`.
- [ ] Ensure that the repository is public.
- [ ] Review [the PR description guidelines](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md#guidelines-for-pull-request-descriptions) and adhere to them.
- [ ] Verify that the link is accessible in a private browser window.
- [x] Create a branch called `assignment-two`.
- [x] Ensure that the repository is public.
- [x] Review [the PR description guidelines](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md#guidelines-for-pull-request-descriptions) and adhere to them.
- [x] Verify that the link is accessible in a private browser window.

If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.

Expand Down Expand Up @@ -54,7 +54,11 @@ 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...
1. Type 1 slowly changing dimensions (overwrite changes): If the customer already exists than the changed address (column) will be updated.
The history of the old address will not be saved.
2. Type 2 slowly changing dimensions (retain changes): The old address wil have a end-date now and a new row with the new address will be added.
![Tables_example](https://viewer.diagrams.net/?tags=%7B%7D&lightbox=1&highlight=0000ff&edit=_blank&layers=1&nav=1&dark=auto#R%3Cmxfile%3E%3Cdiagram%20name%3D%22Page-1%22%20id%3D%22eoebsmCHAySjbejiEOs3%22%3E7Z1bk6I4FIB%2FTVfNPDhFQFAfW9uZ3a2ZranpqZp9TUtUtpGwELvb%2BfWbQFAxQVHjrTlVXaWEEOGcL%2BFckvSdM5i9fUlwPP1GfRLe2Zb%2Fduc83Nk2atse%2FxAli7yk07PygkkS%2BLLSquAx%2BE1kYVFtHvgkLVVklIYsiMuFIxpFZMRKZThJ6Gu52piG5V%2BN8YQoBY8jHKqlvwKfTfPSrmutyv8gwWRa%2FDKy5JkZLirLgnSKffq6VuQM75xBQinLv83eBiQUwivkkl%2F3ueLs8sYSErE6F%2FS%2BkGGn%2Fc%2F34eAXen76OfwdWL9ayM2becHhXD6xvFu2KEQwSeg8ltVIwsibTvD4qahuqTeGlo%2FLOSF0Rliy4FVkQ215hSSkEO3rStx2Ac10TdTLiliqeLJseSUF%2FkUKYh%2Bh7JYJV2Ysvsrn7qcMJ0zSa%2FFjLhWGg4gk%2FBhlx2GI4zTIpZTVmAah%2FxUv6JwV7RRH%2FTG%2FWjaGPH5cKfx1IW%2FRryp6KWunrqztU8na3lPWP0QX6k9pEvwWIg6lNDfln74GsxBHvG9if6OoT7OxKL8qoc9kQEMq9BREU5IEQv6MxrJGSMZMfn2ijNGZPEikbCytbv2Exj9xMiFFlXEQhsXPRDQSxMQ0iFgmTbfP%2F7h8B9Yn987lTzzgx2h1zP9E9YQNaMTvmGMlmiU4Za8kNYyLnpZdeHScE9HhKHQM5inXAu9XtvXnQyUq%2FEFZgMMfYmCKJlkPnbJZKDvj6zRg5DHGI1H1lb%2B38g67Noxtg4NywY7DbCSfBr5PIr1%2B92Moo4EkwxeSQ4GMqtXeqdZeZz%2BtysZWAt67NRzy540w4%2F1xHvmpgsryPg%2Bnp72NnnvfT0iaAkJHIfRWVvg6A55Romq0dgaiVGtpELAFMGSCIdRzVIje47Dkgclz%2FSbPoozHTguofSILqKPAgmC4qadZ7zCrZ4smDxheNlo7w%2FDS1QwvXshy3QbP%2FKa9iThCNmfbGlA%2BjvDPR8bL8UxAET2lcSb4%2FCp%2BE%2BULixN%2B8LJZlMY4KrHp%2FTcX8Y3%2BEx49izhC5LdGOTH3IpKT4Cgt9NjPggnLc6EQYsvHyfOHZPL0QQwWvLT4%2BJh%2FijO26%2BYH618%2BfszvSv58cZ9t19t85uUzZrdefhxeXHpI6HdH9Ls9TcXDu2GN1s7QDXuqqZhjBxAdD1FtW%2FHWB%2FMiDA7G4g0Yi6itjm3njZchNXRdzQuMOJqu1rwoGVJD8Pd9YUYNpiSSVhIQdAxBDQuSIU3UPkMJKDJAUWPCZEgN3xd%2BmRC21s0UJ1pppgbhQ6JO%2FKb1A38uYvHQSAxvg4c1dy9vucLf46pku%2BEsoygB0zCHuYfLcXwYEQETLxCwBCMc3ssTMw5s1hsyv5kUdtoRZFUkg2Uzne4nV0ELOY5Kg3OqbLCr0%2Fk2c1dj2l4g9b5jfgMqT3DoXjzp7taY9QFexdm8ipz6K0q7u2qKAvLuhyi2YsB9769uV81aQObdNEQN8ypcNa8BuXdjFDXGq3DVuDyYPtdn%2BlxL%2Bt1T4%2B%2BQf6%2Br295h1s%2Bt52w8NQiv5p0BoWMQalgu2VND9JBMNodRY7LJnhqZB%2BPnao2fy6eTPTUcWw0MjDmavta82I%2BnxpYhnWyUoIYFfjxNMBrSyaYoakzgx1Nj0gpCxJ%2BQR3nILYApndAIh8NV6UYqdlXnK81ULlT3L2FsIXWH54yW%2Bct%2FU%2FxQpRqLAAOdJyOy5YmK5fCF6bOvmZGQELPgpXwjxm2IXg25n3mFuVN72fPJlpj3dLP0t1niGqv7WtaY5xquNGMvv8a8BzHfa3J7cvavKN293LMD8t1HavbAiO%2Bt2xbIUkO%2BkPA2TVHD%2FB5kaaK%2BkPE2hVFjHB9kQdT3Fsyfa0l5I0uN%2BkLOu6ZyZWdrXtIbWbo5xbDuHNadX77vNWy2ALI0EXOYLmAOpMbMF0BWjeAtmI7XYjpefsIAstS4djUxMOzoelsTw2dqgB7mDJhlqGnBM83GKTBrwBxHzYmeabZU2XsZenf7MnTRfIOWoVfli69nGTrS7Imy3e7V2LhXvw7d8dTMfAd1VTnbvZPZi5q9QsDBuOD%2BVjn4W01QLSIn3NFq6y7ekJuvr9rdkWnUbpt8hddq7izvcM2%2B3ZCeNw3S24bWSyTYZsGq0dxZwNJEWiFFbwwlu%2BtpUHqfY5SjvueG4zG%2FaTFDV818PQqDh1d%2F4HcFaaNTQtjWTGB9txCqzu42CDdTt8PIByTPgKTb0b1i3yeSCFJQN%2BEhVk9f0juMJ5u%2BhNQUFExfqq3d3Sko%2FchwcKq7VnNnGWjUJJQ6VQkwOgqjvV3EI7Cq0dxZdtfWJKJgMo45lur7iLc%2FRDlqHOsvHLUs1LKt5b8ZBqKOJKq%2Bw%2FcOiFITXX%2FTFz5qCaJcIMoMUfX9tXdAVKeSGnDPLu6e6aYInjmh54B%2Fdrh6nUP9s9sPBDmqfwbbKhrGqIEpvDb4ZydlqUk5vLaaPgFr2jhRTUrIFaPkGlEPZNRyUKvXK7a1AaKOJapJ%2BTRbN58ZHLZrddiWu2JdzmGz1TGoGhkYeXTdrZEOm63O7YZVXWYpaqC%2FZmvmg8PCLmMoNcldc9TZu48kZi27K%2Fw1eMcZQqpJ%2FpqjvvPAXzNOVKP8tRr%2Fq%2FW29q0u9j7euXF1pW9Ue%2Bfq7NL7JMGLtQrSD1y1%2FF0UrOhyOuV1km15%2FLlmfVfumrIiIb8Dw5GhGisnr4iLY5WoSt0pS932NlYD5xzKq7aob3NZrNJQzqnSkDlN1vin103SpNsxpEmv2L5ohyaPHSDcPQcI77gBgh8mVOwjsKrObYLpN%2BoTUeN%2F%3C%2Fdiagram%3E%3C%2Fmxfile%3E)
The tables in the image are simplied in terms of columns. In the real databases, customer address table should include more information such as postal code, etc.
```

***
Expand Down Expand Up @@ -183,5 +187,11 @@ Consider, for example, concepts of labour, bias, LLM proliferation, moderating c


```
Your thoughts...
When we think of AI tools, we often picture this tool that is highly automated and can do tasks which humans cannot do.
However, the reality is that human decisions are central to every stage of the development of an AI tool, especially the curation of large data training sets, as described by the article from Vicki Boykis.
AI tools are not exempted from many potential ethical issues, on the contrary, AIs could potentially amplify existing ethical issues, such as fairness, biases, prejudices, and more. An example I have encountered was that, when an AI tool was asked to make an image of a man and woman, specifically a strong muscular woman, none of the returned images met the requirement as they all showed a muscular man and a slender woman.
It is hard to explain why this occurs without considering the training dataset behind each tool. If AIs have never “seen” something, it’s hard for them to generate what is asked of them.
It is apparent that human labour and input remain essential for AI development, therefore it is important to clarify the involvement of human efforts, especially at early stages of development.
To mitigate incidences like this, the selected training data sets need to be very carefully ascertained to actively avoid biases like this.
It is also as important to educate AI users, which are growing in numbers by the day to understand the capabilities and limitations of the tools at their fingertips so they can be mindful about how they are using the tool and what they are getting out of it.
```
136 changes: 110 additions & 26 deletions 02_activities/assignments/DC_Cohort/assignment2.sql
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Expand Up @@ -7,21 +7,28 @@ We tell them, no problem! We can produce a list with all of the appropriate deta

Using the following syntax you create our super cool and not at all needy manager a list:

SELECT
SELECT
product_name || ', ' || product_size|| ' (' || product_qty_type || ')'
FROM product


But wait! The product table has some bad data (a few NULL values).
Find the NULLs and then using COALESCE, replace the NULL with a blank for the first column with
nulls, and 'unit' for the second column with nulls.
Find the NULLs and then using COALESCE, replace the NULL with a
blank for the first problem, and 'unit' for the second problem.

**HINT**: keep the syntax the same, but edited the correct components with the string.
HINT: keep the syntax the same, but edited the correct components with the string.
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. */

All the other rows will remain the same.) */
SELECT *
FROM product
Where product_size IS NULL
OR product_qty_type IS NULL;

SELECT
product_name || ', ' ||
coalesce( product_size, '') || ' (' ||
coalesce (product_qty_type, 'unit') || ')' AS detailed_products_list
FROM product;

--Windowed Functions
/* 1. Write a query that selects from the customer_purchases table and numbers each customer’s
Expand All @@ -32,18 +39,27 @@ You can either display all rows in the customer_purchases table, with the counte
each new market date for each customer, or select only the unique market dates per customer
(without purchase details) and number those visits.
HINT: One of these approaches uses ROW_NUMBER() and one uses DENSE_RANK(). */

SELECT *
,dense_rank() OVER (PARTITION BY customer_id ORDER BY market_date ASC) as market_visit
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 *
FROM (
SELECT *
,dense_rank() OVER (PARTITION BY customer_id ORDER BY market_date DESC) as market_visit
FROM customer_purchases
) AS x
WHERE x.market_visit = 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 *
, count () OVER (PARTITION BY customer_id, product_id) AS times_of_purchase
FROM customer_purchases;


-- String manipulations
Expand All @@ -57,11 +73,19 @@ Remove any trailing or leading whitespaces. Don't just use a case statement for
| Habanero Peppers - Organic | Organic |

Hint: you might need to use INSTR(product_name,'-') to find the hyphens. INSTR will help split the column. */


SELECT *
,CASE WHEN instr(product_name, '-' ) >0
THEN ltrim (substr(product_name, INSTR (product_name, '-') + 1))
END AS description
FROM product;

/* 2. Filter the query to show any product_size value that contain a number with REGEXP. */

SELECT *
,CASE WHEN instr(product_name, '-' ) >0
THEN ltrim (substr(product_name, INSTR (product_name, '-') + 1))
END AS description
FROM product
WHERE product_size REGEXP '[0-9]';


-- UNION
Expand All @@ -73,8 +97,32 @@ HINT: There are a possibly a few ways to do this query, but if you're struggling
"best day" and "worst day";
3) Query the second temp table twice, once for the best day, once for the worst day,
with a UNION binding them. */


WITH total_sales AS(
SELECT
market_date
, SUM(quantity*cost_to_customer_per_qty) AS total_sale
FROM customer_purchases
GROUP BY market_date),

total_sale_rank AS(
SELECT
market_date,
total_sale
, dense_rank() OVER( ORDER BY total_sale DESC) AS highest_sale
, dense_rank() OVER( ORDER BY total_sale ASC) AS lowest_sale
FROM total_sales
)
SELECT 'best day'
AS day_sale, market_date, total_sale
FROM total_sale_rank
where highest_sale = 1

UNION

SELECT 'worse day'
AS day_sale, market_date, total_sale
FROM total_sale_rank
where lowest_sale = 1;


/* SECTION 3 */
Expand All @@ -89,27 +137,57 @@ Remember, CROSS JOIN will explode your table rows, so CROSS JOIN should likely b
Think a bit about the row counts: how many distinct vendors, product names are there (x)?
How many customers are there (y).
Before your final group by you should have the product of those two queries (x*y). */


WITH vendors_to_sell AS (
SELECT DISTINCT
product_name
,vendor_name
,original_price
FROM product
JOIN vendor_inventory
USING (product_id)
JOIN vendor
USING (vendor_id)
),
customers_to_buy AS (
SELECT
customer_first_name,
customer_last_name,
count(customer_id) AS number_of_customers
FROM customer
)
SELECT
vendor_name
,product_name
, 5* original_price *number_of_customers AS revenue
FROM vendors_to_sell
CROSS JOIN customers_to_buy;

-- 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`. */

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
VALUES (7, 'Apple Pie', '10''''' , 3, '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_id = 7
AND snapshot_timestamp < (
SELECT MAX (snapshot_timestamp)
FROM product_units
WHERE product_id =7) ;


-- UPDATE
Expand All @@ -128,7 +206,13 @@ Third, SET current_quantity = (...your select statement...), remembering that WH
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. */
ALTER TABLE product_units
ADD current_quantity INT;




UPDATE product_units
SET current_quantity =
coalesce ((
SELECT quantity
FROM vendor_inventory
WHERE vendor_inventory.product_id = product_units.product_id
ORDER BY market_date DESC), 0) ;
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