├── Case Study # 1 - Danny's Diner
├── Danny's Diner Solution.md
├── Danny's Diner Solution.sql
├── DinerSchema.sql
├── ERD.jpg
└── README.md
├── Case Study # 2 - Pizza Runner
├── 0. Data Clean.md
├── A. Pizza metrics.md
├── B. Runner and Customer Experience.md
├── C. Ingredient Optimisation.md
├── D. Pricing and Ratings.md
├── E. Bonus Questions.md
├── ERD.jpg
├── Pizza Runner Schema.sql
└── README.md
├── Case Study # 3 - Foodie-Fi
├── A. Customer Journey.md
├── B. Data Analysis Questions.md
├── C. Challenge Payment Question.md
├── ERD.jpg
├── Foodie-Fi Schema.sql
└── README.md
├── Case Study # 4 - Data Bank
├── A. Customer Nodes Exploration.md
├── B. Customer Transactions.md
├── C. Data Allocation Challenge.md
├── Data Bank Schema.sql
└── README.md
├── Case Study # 5 - Data Mart
├── 1. Data Cleansing Steps.md
├── 2. Data Exploration.md
├── 3. Before & After Analysis.md
├── Data Mart Schema.sql
└── README.md
└── README.md
/Case Study # 1 - Danny's Diner/Danny's Diner Solution.md:
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1 | # :ramen: :curry: :sushi: Case Study #1: Danny's Diner
2 |
3 | ## Case Study Questions
4 |
5 | 1. What is the total amount each customer spent at the restaurant?
6 | 2. How many days has each customer visited the restaurant?
7 | 3. What was the first item from the menu purchased by each customer?
8 | 4. What is the most purchased item on the menu and how many times was it purchased by all customers?
9 | 5. Which item was the most popular for each customer?
10 | 6. Which item was purchased first by the customer after they became a member?
11 | 7. Which item was purchased just before the customer became a member?
12 | 10. What is the total items and amount spent for each member before they became a member?
13 | 11. If each $1 spent equates to 10 points and sushi has a 2x points multiplier - how many points would each customer have?
14 | 12. In the first week after a customer joins the program (including their join date) they earn 2x points on all items, not just sushi - how many points do customer A and B have at the end of January?
15 | ***
16 |
17 | ### 1. What is the total amount each customer spent at the restaurant?
18 |
19 | ```sql
20 | SELECT customer_id,
21 | CONCAT('$', sum(price)) AS total_sales
22 | FROM dannys_diner.menu
23 | INNER JOIN dannys_diner.sales ON menu.product_id = sales.product_id
24 | GROUP BY customer_id
25 | ORDER BY customer_id;
26 | ```
27 |
28 | #### Result set:
29 | | customer_id | total_sales |
30 | | ----------- | ----------- |
31 | | A | $76 |
32 | | B | $74 |
33 | | C | $36 |
34 |
35 | ***
36 |
37 | ### 2. How many days has each customer visited the restaurant?
38 |
39 | ```sql
40 | SELECT customer_id,
41 | count(DISTINCT order_date) AS visit_count
42 | FROM dannys_diner.sales
43 | GROUP BY customer_id
44 | ORDER BY customer_id;
45 | ```
46 |
47 | #### Result set:
48 | | customer_id | visit_count |
49 | | ----------- | ----------- |
50 | | A | 4 |
51 | | B | 6 |
52 | | C | 2 |
53 |
54 | ***
55 |
56 | ### 3. What was the first item from the menu purchased by each customer?
57 |
58 | ```sql
59 | WITH order_info_cte AS
60 | (SELECT customer_id,
61 | order_date,
62 | product_name,
63 | DENSE_RANK() OVER(PARTITION BY s.customer_id
64 | ORDER BY s.order_date) AS rank_num
65 | FROM dannys_diner.sales AS s
66 | JOIN dannys_diner.menu AS m ON s.product_id = m.product_id)
67 | SELECT customer_id,
68 | product_name
69 | FROM order_info_cte
70 | WHERE rank_num = 1
71 | GROUP BY customer_id,
72 | product_name;
73 | ```
74 |
75 | #### Result set:
76 | | customer_id | product_name |
77 | | ----------- | ------------ |
78 | | A | curry |
79 | | A | sushi |
80 | | B | curry |
81 | | C | ramen |
82 |
83 | ```sql
84 | WITH order_info_cte AS
85 | (SELECT customer_id,
86 | order_date,
87 | product_name,
88 | DENSE_RANK() OVER(PARTITION BY s.customer_id
89 | ORDER BY s.order_date) AS rank_num
90 | FROM dannys_diner.sales AS s
91 | JOIN dannys_diner.menu AS m ON s.product_id = m.product_id)
92 | SELECT customer_id,
93 | GROUP_CONCAT(DISTINCT product_name
94 | ORDER BY product_name) AS product_name
95 | FROM order_info_cte
96 | WHERE rank_num = 1
97 | GROUP BY customer_id
98 | ;
99 | ```
100 |
101 | #### Result set:
102 | 
103 |
104 | ***
105 |
106 | ### 4. What is the most purchased item on the menu and how many times was it purchased by all customers?
107 |
108 | ```sql
109 | SELECT product_name AS most_purchased_item,
110 | count(sales.product_id) AS order_count
111 | FROM dannys_diner.menu
112 | INNER JOIN dannys_diner.sales ON menu.product_id = sales.product_id
113 | GROUP BY product_name
114 | ORDER BY order_count DESC
115 | LIMIT 1;
116 | ```
117 |
118 | #### Result set:
119 | | most_purchased_item | order_count |
120 | | ------------------- | ----------- |
121 | | ramen | 8 |
122 |
123 | ```sql
124 | SELECT most_purchased_item,
125 | max(order_count) AS order_count
126 | FROM
127 | (SELECT product_name AS most_purchased_item,
128 | count(sales.product_id) AS order_count
129 | FROM dannys_diner.menu
130 | INNER JOIN dannys_diner.sales ON menu.product_id = sales.product_id
131 | GROUP BY product_name
132 | ORDER BY order_count DESC) max_purchased_item;
133 | ```
134 |
135 | #### Result set:
136 | 
137 |
138 | ***
139 |
140 | ### 5. Which item was the most popular for each customer?
141 |
142 | ```sql
143 | WITH order_info AS
144 | (SELECT product_name,
145 | customer_id,
146 | count(product_name) AS order_count,
147 | rank() over(PARTITION BY customer_id
148 | ORDER BY count(product_name) DESC) AS rank_num
149 | FROM dannys_diner.menu
150 | INNER JOIN dannys_diner.sales ON menu.product_id = sales.product_id
151 | GROUP BY customer_id,
152 | product_name)
153 | SELECT customer_id,
154 | product_name,
155 | order_count
156 | FROM order_info
157 | WHERE rank_num =1;
158 | ```
159 |
160 | #### Result set:
161 | | customer_id | product_name | order_count |
162 | | ----------- | ------------ | ----------- |
163 | | A | ramen | 3 |
164 | | B | ramen | 2 |
165 | | B | curry | 2 |
166 | | B | sushi | 2 |
167 | | C | ramen | 3 |
168 |
169 | ```sql
170 | WITH order_info AS
171 | (SELECT product_name,
172 | customer_id,
173 | count(product_name) AS order_count,
174 | rank() over(PARTITION BY customer_id
175 | ORDER BY count(product_name) DESC) AS rank_num
176 | FROM dannys_diner.menu
177 | INNER JOIN dannys_diner.sales ON menu.product_id = sales.product_id
178 | GROUP BY customer_id,
179 | product_name)
180 | SELECT customer_id,
181 | GROUP_CONCAT(DISTINCT product_name
182 | ORDER BY product_name) AS product_name,
183 | order_count
184 | FROM order_info
185 | WHERE rank_num =1
186 | GROUP BY customer_id;
187 | ```
188 |
189 | #### Result set:
190 | 
191 |
192 | ***
193 |
194 | ### 6. Which item was purchased first by the customer after they became a member?
195 |
196 | ```sql
197 | WITH diner_info AS
198 | (SELECT product_name,
199 | s.customer_id,
200 | order_date,
201 | join_date,
202 | m.product_id,
203 | DENSE_RANK() OVER(PARTITION BY s.customer_id
204 | ORDER BY s.order_date) AS first_item
205 | FROM dannys_diner.menu AS m
206 | INNER JOIN dannys_diner.sales AS s ON m.product_id = s.product_id
207 | INNER JOIN dannys_diner.members AS mem ON mem.customer_id = s.customer_id
208 | WHERE order_date >= join_date )
209 | SELECT customer_id,
210 | product_name,
211 | order_date
212 | FROM diner_info
213 | WHERE first_item=1;
214 | ```
215 |
216 | #### Result set:
217 | | customer_id | product_name | order_date |
218 | | ----------- | ------------ | ------------------------ |
219 | | A | curry | 2021-01-07T00:00:00.000Z |
220 | | B | sushi | 2021-01-11T00:00:00.000Z |
221 |
222 | ***
223 |
224 | ### 7. Which item was purchased just before the customer became a member?
225 |
226 | ```sql
227 | WITH diner_info AS
228 | (SELECT product_name,
229 | s.customer_id,
230 | order_date,
231 | join_date,
232 | m.product_id,
233 | DENSE_RANK() OVER(PARTITION BY s.customer_id
234 | ORDER BY s.order_date DESC) AS item_rank
235 | FROM dannys_diner.menu AS m
236 | INNER JOIN dannys_diner.sales AS s ON m.product_id = s.product_id
237 | INNER JOIN dannys_diner.members AS mem ON mem.customer_id = s.customer_id
238 | WHERE order_date < join_date )
239 | SELECT customer_id,
240 | GROUP_CONCAT(DISTINCT product_name
241 | ORDER BY product_name) AS product_name,
242 | order_date,
243 | join_date
244 | FROM diner_info
245 | WHERE item_rank=1
246 | GROUP BY customer_id;
247 | ```
248 |
249 | #### Result set:
250 | | customer_id | product_name | order_date | join_date |
251 | | ----------- | ------------ | ------------------------ | ------------------------ |
252 | | A | curry,sushi | 2021-01-01T00:00:00.000Z | 2021-01-07T00:00:00.000Z |
253 | | B | sushi | 2021-01-04T00:00:00.000Z | 2021-01-09T00:00:00.000Z |
254 |
255 | ***
256 |
257 | ### 8. What is the total items and amount spent for each member before they became a member?
258 |
259 | ```sql
260 | SELECT s.customer_id,
261 | count(product_name) AS total_items,
262 | CONCAT('$', SUM(price)) AS amount_spent
263 | FROM dannys_diner.menu AS m
264 | INNER JOIN dannys_diner.sales AS s ON m.product_id = s.product_id
265 | INNER JOIN dannys_diner.members AS mem ON mem.customer_id = s.customer_id
266 | WHERE order_date < join_date
267 | GROUP BY s.customer_id
268 | ORDER BY customer_id;
269 | ```
270 |
271 | #### Result set:
272 | | customer_id | total_items | amount_spent |
273 | | ----------- | ----------- | ------------ |
274 | | A | 2 | $25 |
275 | | B | 3 | $40 |
276 |
277 | ***
278 |
279 | ### 9. If each $1 spent equates to 10 points and sushi has a 2x points multiplier - how many points would each customer have?
280 |
281 | #### Had the customer joined the loyalty program before making the purchases, total points that each customer would have accrued
282 | ```sql
283 | SELECT customer_id,
284 | SUM(CASE
285 | WHEN product_name = 'sushi' THEN price*20
286 | ELSE price*10
287 | END) AS customer_points
288 | FROM dannys_diner.menu AS m
289 | INNER JOIN dannys_diner.sales AS s ON m.product_id = s.product_id
290 | GROUP BY customer_id
291 | ORDER BY customer_id;
292 | ```
293 |
294 | #### Result set:
295 | | customer_id | customer_points |
296 | | ----------- | --------------- |
297 | | A | 860 |
298 | | B | 940 |
299 | | C | 360 |
300 |
301 | #### Total points that each customer has accrued after taking a membership
302 | ```sql
303 | SELECT s.customer_id,
304 | SUM(CASE
305 | WHEN product_name = 'sushi' THEN price*20
306 | ELSE price*10
307 | END) AS customer_points
308 | FROM dannys_diner.menu AS m
309 | INNER JOIN dannys_diner.sales AS s ON m.product_id = s.product_id
310 | INNER JOIN dannys_diner.members AS mem ON mem.customer_id = s.customer_id
311 | WHERE order_date >= join_date
312 | GROUP BY s.customer_id
313 | ORDER BY s.customer_id;
314 | ```
315 |
316 | #### Result set:
317 | | customer_id | customer_points |
318 | | ----------- | --------------- |
319 | | A | 510 |
320 | | B | 440 |
321 |
322 | ***
323 |
324 | ### 10. In the first week after a customer joins the program (including their join date) they earn 2x points on all items, not just sushi - how many points do customer A and B have at the end of January
325 |
326 | #### Steps
327 | 1. Find the program_last_date which is 7 days after a customer joins the program (including their join date)
328 | 2. Determine the customer points for each transaction and for members with a membership
329 | - During the first week of the membership -> points = price*20 irrespective of the purchase item
330 | - Product = Sushi -> and order_date is not within a week of membership -> points = price*20
331 | - Product = Not Sushi -> and order_date is not within a week of membership -> points = price*10
332 | 3. Conditions in WHERE clause
333 | - order_date <= '2021-01-31' -> Order must be placed before 31st January 2021
334 | - order_date >= join_date -> Points awarded to only customers with a membership
335 |
336 | ```sql
337 | WITH program_last_day_cte AS
338 | (SELECT join_date,
339 | DATE_ADD(join_date, INTERVAL 6 DAY) AS program_last_date,
340 | customer_id
341 | FROM dannys_diner.members)
342 | SELECT s.customer_id,
343 | SUM(CASE
344 | WHEN order_date BETWEEN join_date AND program_last_date THEN price*10*2
345 | WHEN order_date NOT BETWEEN join_date AND program_last_date
346 | AND product_name = 'sushi' THEN price*10*2
347 | WHEN order_date NOT BETWEEN join_date AND program_last_date
348 | AND product_name != 'sushi' THEN price*10
349 | END) AS customer_points
350 | FROM dannys_diner.menu AS m
351 | INNER JOIN dannys_diner.sales AS s ON m.product_id = s.product_id
352 | INNER JOIN program_last_day_cte AS mem ON mem.customer_id = s.customer_id
353 | AND order_date <='2021-01-31'
354 | AND order_date >=join_date
355 | GROUP BY s.customer_id
356 | ORDER BY s.customer_id;
357 | ```
358 | ```sql
359 | SELECT s.customer_id,
360 | SUM(IF(order_date BETWEEN join_date AND DATE_ADD(join_date, INTERVAL 6 DAY), price*10*2, IF(product_name = 'sushi', price*10*2, price*10))) AS customer_points
361 | FROM dannys_diner.menu AS m
362 | INNER JOIN dannys_diner.sales AS s ON m.product_id = s.product_id
363 | INNER JOIN dannys_diner.members AS mem USING (customer_id)
364 | WHERE order_date <='2021-01-31'
365 | AND order_date >=join_date
366 | GROUP BY s.customer_id
367 | ORDER BY s.customer_id;
368 | ```
369 |
370 | #### Result set:
371 | | customer_id | customer_points |
372 | | ----------- | --------------- |
373 | | A | 1020 |
374 | | B | 320 |
375 |
376 | ***
377 |
378 | ### Bonus Questions
379 |
380 | #### Join All The Things
381 | Create basic data tables that Danny and his team can use to quickly derive insights without needing to join the underlying tables using SQL. Fill Member column as 'N' if the purchase was made before becoming a member and 'Y' if the after is amde after joining the membership.
382 |
383 | ```sql
384 | SELECT customer_id,
385 | order_date,
386 | product_name,
387 | price,
388 | IF(order_date >= join_date, 'Y', 'N') AS member
389 | FROM members
390 | RIGHT JOIN sales USING (customer_id)
391 | INNER JOIN menu USING (product_id)
392 | ORDER BY customer_id,
393 | order_date;
394 | ```
395 |
396 | #### Result set:
397 | 
398 |
399 | ***
400 |
401 | #### Rank All The Things
402 | Danny also requires further information about the ranking of customer products, but he purposely does not need the ranking for non-member purchases so he expects null ranking values for the records when customers are not yet part of the loyalty program.
403 |
404 | ```sql
405 | SELECT customer_id,
406 | order_date,
407 | product_name,
408 | price,
409 | IF(order_date >= join_date, 'Y', 'N') AS member
410 | FROM members
411 | RIGHT JOIN sales USING (customer_id)
412 | INNER JOIN menu USING (product_id)
413 | ORDER BY customer_id,
414 | order_date;
415 | ```
416 | ```sql
417 | WITH data_table AS
418 | (SELECT customer_id,
419 | order_date,
420 | product_name,
421 | price,
422 | IF(order_date >= join_date, 'Y', 'N') AS member
423 | FROM members
424 | RIGHT JOIN sales USING (customer_id)
425 | INNER JOIN menu USING (product_id)
426 | ORDER BY customer_id,
427 | order_date)
428 | SELECT *,
429 | IF(member='N', NULL, DENSE_RANK() OVER (PARTITION BY customer_id, member
430 | ORDER BY order_date)) AS ranking
431 | FROM data_table;
432 | ```
433 |
434 | #### Result set:
435 | 
436 |
437 |
438 | ***
439 |
440 |
441 | Click [here](https://github.com/manaswikamila05/8-Week-SQL-Challenge) to move back to the 8-Week-SQL-Challenge repository!
442 |
443 |
444 |
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/Case Study # 1 - Danny's Diner/Danny's Diner Solution.sql:
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1 | ----------------------------------
2 | -- CASE STUDY #1: DANNY'S DINER --
3 | ----------------------------------
4 |
5 | -- Author: Manaswi Kamila
6 | -- Date: 18/04/2022
7 | -- Tool used: MySQL Server
8 |
9 | --------------------------
10 | -- CASE STUDY QUESTIONS --
11 | --------------------------
12 |
13 | -- 1. What is the total amount each customer spent at the restaurant?
14 | SELECT customer_id,
15 | CONCAT('$', sum(price)) AS total_sales
16 | FROM dannys_diner.menu
17 | INNER JOIN dannys_diner.sales ON menu.product_id = sales.product_id
18 | GROUP BY customer_id
19 | ORDER BY customer_id;
20 |
21 | -- 2. How many days has each customer visited the restaurant?
22 | SELECT customer_id,
23 | count(DISTINCT order_date) AS visit_count
24 | FROM dannys_diner.sales
25 | GROUP BY customer_id
26 | ORDER BY customer_id;
27 |
28 | -- 3. What was the first item from the menu purchased by each customer?
29 | -- order_date column is a date column does not include the purchase time details.
30 | -- Asssumption: Since the timestamp is missing, all items bought on the first day is considered as the first item(provided multiple items were purchased on the first day)
31 | -- dense_rank() is used to rank all orders purchased on the same day
32 |
33 | -- Using CTE
34 | WITH order_info_cte AS
35 | (SELECT customer_id,
36 | order_date,
37 | product_name,
38 | DENSE_RANK() OVER(PARTITION BY s.customer_id
39 | ORDER BY s.order_date) AS item_rank
40 | FROM dannys_diner.sales AS s
41 | JOIN dannys_diner.menu AS m ON s.product_id = m.product_id)
42 | SELECT customer_id,
43 | product_name
44 | FROM order_info_cte
45 | WHERE item_rank = 1
46 | GROUP BY customer_id,
47 | product_name;
48 |
49 | -- Using derived table
50 | SELECT customer_id,
51 | product_name
52 | FROM
53 | (SELECT customer_id,
54 | order_date,
55 | product_name,
56 | DENSE_RANK() OVER(PARTITION BY s.customer_id
57 | ORDER BY s.order_date) AS item_rank
58 | FROM dannys_diner.sales AS s
59 | JOIN dannys_diner.menu AS m ON s.product_id = m.product_id) AS first_item
60 | WHERE item_rank=1
61 | GROUP BY customer_id,
62 | product_name;
63 |
64 | -- 4. What is the most purchased item on the menu and how many times was it purchased by all customers?
65 | -- Using LIMIT clause
66 | SELECT product_name AS most_purchased_item,
67 | count(sales.product_id) AS order_count
68 | FROM dannys_diner.menu
69 | INNER JOIN dannys_diner.sales ON menu.product_id = sales.product_id
70 | GROUP BY product_name
71 | ORDER BY order_count DESC
72 | LIMIT 1;
73 |
74 | -- Using derived table
75 | SELECT most_purchased_item,
76 | order_count
77 | FROM
78 | (SELECT product_name AS most_purchased_item,
79 | count(sales.product_id) AS order_count,
80 | rank() over(
81 | ORDER BY count(sales.product_id) DESC) AS order_rank
82 | FROM dannys_diner.menu
83 | INNER JOIN dannys_diner.sales ON menu.product_id = sales.product_id
84 | GROUP BY product_name) item_counts
85 | WHERE order_rank=1;
86 |
87 | -- 5. Which item was the most popular for each customer?
88 | -- Asssumption: Products with the highest purchase counts are all considered to be popular for each customer
89 |
90 | WITH order_info AS
91 | (SELECT product_name,
92 | customer_id,
93 | count(product_name) AS order_count,
94 | rank() over(PARTITION BY customer_id
95 | ORDER BY count(product_name) DESC) AS rank_num
96 | FROM dannys_diner.menu
97 | INNER JOIN dannys_diner.sales ON menu.product_id = sales.product_id
98 | GROUP BY customer_id,
99 | product_name)
100 | SELECT customer_id,
101 | product_name,
102 | order_count
103 | FROM order_info
104 | WHERE rank_num =1;
105 |
106 | -- 6. Which item was purchased first by the customer after they became a member?
107 | -- Asssumption: Since timestamp of purchase is not available, purchase made when order_date is the same or after the join date is used to find the first item
108 |
109 | WITH diner_info AS
110 | (SELECT product_name,
111 | s.customer_id,
112 | order_date,
113 | join_date,
114 | m.product_id,
115 | DENSE_RANK() OVER(PARTITION BY s.customer_id
116 | ORDER BY s.order_date) AS first_item
117 | FROM dannys_diner.menu AS m
118 | INNER JOIN dannys_diner.sales AS s ON m.product_id = s.product_id
119 | INNER JOIN dannys_diner.members AS mem ON mem.customer_id = s.customer_id
120 | WHERE order_date >= join_date )
121 | SELECT customer_id,
122 | product_name,
123 | order_date
124 | FROM diner_info
125 | WHERE first_item=1;
126 |
127 | -- 7. Which item was purchased just before the customer became a member?
128 | -- Asssumption: Since timestamp of purchase is not available, purchase made when order_date is before the join date is used to find the last item purchased just before the customer became a member
129 |
130 | WITH diner_info AS
131 | (SELECT product_name,
132 | s.customer_id,
133 | order_date,
134 | join_date,
135 | m.product_id,
136 | DENSE_RANK() OVER(PARTITION BY s.customer_id
137 | ORDER BY s.order_date DESC) AS item_rank
138 | FROM dannys_diner.menu AS m
139 | INNER JOIN dannys_diner.sales AS s ON m.product_id = s.product_id
140 | INNER JOIN dannys_diner.members AS mem ON mem.customer_id = s.customer_id
141 | WHERE order_date < join_date )
142 | SELECT customer_id,
143 | product_name,
144 | order_date,
145 | join_date
146 | FROM diner_info
147 | WHERE item_rank=1;
148 |
149 | -- 8. What is the total items and amount spent for each member before they became a member?
150 | SELECT s.customer_id,
151 | count(product_name) AS total_items,
152 | CONCAT('$', SUM(price)) AS amount_spent
153 | FROM dannys_diner.menu AS m
154 | INNER JOIN dannys_diner.sales AS s ON m.product_id = s.product_id
155 | INNER JOIN dannys_diner.members AS mem ON mem.customer_id = s.customer_id
156 | WHERE order_date < join_date
157 | GROUP BY s.customer_id
158 | ORDER BY customer_id;
159 |
160 | -- 9. If each $1 spent equates to 10 points and sushi has a 2x points multiplier - how many points would each customer have?
161 | -- Had the customer joined the loyalty program before making the purchases, total points that each customer would have accrued
162 | SELECT customer_id,
163 | SUM(CASE
164 | WHEN product_name = 'sushi' THEN price*20
165 | ELSE price*10
166 | END) AS customer_points
167 | FROM dannys_diner.menu AS m
168 | INNER JOIN dannys_diner.sales AS s ON m.product_id = s.product_id
169 | GROUP BY customer_id
170 | ORDER BY customer_id;
171 |
172 | -- Total points that each customer has accrued after taking a membership
173 | SELECT s.customer_id,
174 | SUM(CASE
175 | WHEN product_name = 'sushi' THEN price*20
176 | ELSE price*10
177 | END) AS customer_points
178 | FROM dannys_diner.menu AS m
179 | INNER JOIN dannys_diner.sales AS s ON m.product_id = s.product_id
180 | INNER JOIN dannys_diner.members AS mem ON mem.customer_id = s.customer_id
181 | WHERE order_date >= join_date
182 | GROUP BY s.customer_id
183 | ORDER BY s.customer_id;
184 |
185 | -- 10. In the first week after a customer joins the program (including their join date) they earn 2x points on all items, not just sushi -
186 | -- how many points do customer A and B have at the end of January
187 | -- Asssumption: Points is rewarded only after the customer joins in the membership program
188 |
189 | -- Steps
190 | -- 1. Find the program_last_date which is 7 days after a customer joins the program (including their join date)
191 | -- 2. Determine the customer points for each transaction and for members with a membership
192 | -- a. During the first week of the membership -> points = price*20 irrespective of the purchase item
193 | -- b. Product = Sushi -> and order_date is not within a week of membership -> points = price*20
194 | -- c. Product = Not Sushi -> and order_date is not within a week of membership -> points = price*10
195 | -- 3. Conditions in WHERE clause
196 | -- a. order_date <= '2021-01-31' -> Order must be placed before 31st January 2021
197 | -- b. order_date >= join_date -> Points awarded to only customers with a membership
198 |
199 | WITH program_last_day_cte AS
200 | (SELECT join_date,
201 | DATE_ADD(join_date, INTERVAL 7 DAY) AS program_last_date,
202 | customer_id
203 | FROM dannys_diner.members)
204 | SELECT s.customer_id,
205 | SUM(CASE
206 | WHEN order_date BETWEEN join_date AND program_last_date THEN price*10*2
207 | WHEN order_date NOT BETWEEN join_date AND program_last_date
208 | AND product_name = 'sushi' THEN price*10*2
209 | WHEN order_date NOT BETWEEN join_date AND program_last_date
210 | AND product_name != 'sushi' THEN price*10
211 | END) AS customer_points
212 | FROM dannys_diner.menu AS m
213 | INNER JOIN dannys_diner.sales AS s ON m.product_id = s.product_id
214 | INNER JOIN program_last_day_cte AS mem ON mem.customer_id = s.customer_id
215 | AND order_date <='2021-01-31'
216 | AND order_date >=join_date
217 | GROUP BY s.customer_id
218 | ORDER BY s.customer_id;
219 |
220 |
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/Case Study # 1 - Danny's Diner/DinerSchema.sql:
--------------------------------------------------------------------------------
1 | ----------------------------------
2 | -- CASE STUDY #1: DANNY'S DINER --
3 | ----------------------------------
4 |
5 | -- Author: Manaswi Kamila
6 | -- Date: 18/04/2022
7 | -- Tool used: MySQL Server
8 |
9 |
10 |
11 | CREATE SCHEMA dannys_diner;
12 | use dannys_diner;
13 |
14 | CREATE TABLE sales (
15 | customer_id VARCHAR(1),
16 | order_date DATE,
17 | product_id INTEGER
18 | );
19 |
20 | INSERT INTO sales (
21 | customer_id, order_date, product_id
22 | )
23 | VALUES
24 | ('A', '2021-01-01', '1'),
25 | ('A', '2021-01-01', '2'),
26 | ('A', '2021-01-07', '2'),
27 | ('A', '2021-01-10', '3'),
28 | ('A', '2021-01-11', '3'),
29 | ('A', '2021-01-11', '3'),
30 | ('B', '2021-01-01', '2'),
31 | ('B', '2021-01-02', '2'),
32 | ('B', '2021-01-04', '1'),
33 | ('B', '2021-01-11', '1'),
34 | ('B', '2021-01-16', '3'),
35 | ('B', '2021-02-01', '3'),
36 | ('C', '2021-01-01', '3'),
37 | ('C', '2021-01-01', '3'),
38 | ('C', '2021-01-07', '3');
39 |
40 | CREATE TABLE menu (
41 | product_id INTEGER,
42 | product_name VARCHAR(5),
43 | price INTEGER
44 | );
45 |
46 | INSERT INTO menu (product_id, product_name, price)
47 | VALUES
48 | (1, 'sushi', 10),
49 | (2, 'curry', 15),
50 | (3, 'ramen', 12);
51 |
52 |
53 | CREATE TABLE members (
54 | customer_id VARCHAR(1),
55 | join_date DATE
56 | );
57 |
58 | INSERT INTO members (customer_id, join_date)
59 | VALUES
60 | ('A', '2021-01-07'),
61 | ('B', '2021-01-09');
62 |
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/Case Study # 1 - Danny's Diner/ERD.jpg:
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https://raw.githubusercontent.com/manaswikamila05/8-Week-SQL-Challenge/f655c7ea30b64979e1980f7030a3740cab034b82/Case Study # 1 - Danny's Diner/ERD.jpg
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/Case Study # 1 - Danny's Diner/README.md:
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1 | # :ramen: :curry: :sushi: Case Study #1: Danny's Diner
2 |
3 | 
4 |
5 | View the case study [here](https://8weeksqlchallenge.com/case-study-1/)
6 |
7 | ## Table Of Contents
8 | - [Introduction](#introduction)
9 | - [Problem Statement](#problem-statement)
10 | - [Datasets used](#datasets-used)
11 | - [Entity Relationship Diagram](#entity-relationship-diagram)
12 | - [Case Study Questions](#case-study-questions)
13 |
14 | ## Introduction
15 | Danny seriously loves Japanese food so in the beginning of 2021, he decides to embark upon a risky venture and opens up a cute little restaurant that sells his 3 favourite foods: sushi, curry and ramen.
16 |
17 | Danny’s Diner is in need of your assistance to help the restaurant stay afloat - the restaurant has captured some very basic data from their few months of operation but have no idea how to use their data to help them run the business.
18 |
19 | ## Problem Statement
20 | Danny wants to use the data to answer a few simple questions about his customers, especially about their visiting patterns, how much money they’ve spent and also which menu items are their favourite. Having this deeper connection with his customers will help him deliver a better and more personalised experience for his loyal customers.
21 | He plans on using these insights to help him decide whether he should expand the existing customer loyalty program.
22 |
23 | ## Datasets used
24 | Three key datasets for this case study
25 | - sales: The sales table captures all customer_id level purchases with an corresponding order_date and product_id information for when and what menu items were ordered.
26 | - menu: The menu table maps the product_id to the actual product_name and price of each menu item.
27 | - members: The members table captures the join_date when a customer_id joined the beta version of the Danny’s Diner loyalty program.
28 |
29 | ## Entity Relationship Diagram
30 | 
31 |
32 | ## Case Study Questions
33 | 1. What is the total amount each customer spent at the restaurant?
34 | 2. How many days has each customer visited the restaurant?
35 | 3. What was the first item from the menu purchased by each customer?
36 | 4. What is the most purchased item on the menu and how many times was it purchased by all customers?
37 | 5. Which item was the most popular for each customer?
38 | 6. Which item was purchased first by the customer after they became a member?
39 | 7. Which item was purchased just before the customer became a member?
40 | 10. What is the total items and amount spent for each member before they became a member?
41 | 11. If each $1 spent equates to 10 points and sushi has a 2x points multiplier - how many points would each customer have?
42 | 12. In the first week after a customer joins the program (including their join date) they earn 2x points on all items, not just sushi - how many points do customer A and B have at the end of January?
43 |
44 | Click [here](https://github.com/manaswikamila05/8-Week-SQL-Challenge/blob/main/Case%20Study%20%23%201%20-%20Danny's%20Diner/Danny's%20Diner%20Solution.md) to view the solution solution of the case study!
45 |
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/Case Study # 2 - Pizza Runner/0. Data Clean.md:
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1 | # Data Cleaning
2 |
3 | ## customer_orders table
4 | - The exclusions and extras columns in customer_orders table will need to be cleaned up before using them in the queries
5 | - In the exclusions and extras columns, there are blank spaces and null values.
6 |
7 | ```sql
8 | DROP TABLE IF EXISTS customer_orders_temp;
9 |
10 | CREATE TEMPORARY TABLE customer_orders_temp AS
11 | SELECT order_id,
12 | customer_id,
13 | pizza_id,
14 | CASE
15 | WHEN exclusions = '' THEN NULL
16 | WHEN exclusions = 'null' THEN NULL
17 | ELSE exclusions
18 | END AS exclusions,
19 | CASE
20 | WHEN extras = '' THEN NULL
21 | WHEN extras = 'null' THEN NULL
22 | ELSE extras
23 | END AS extras,
24 | order_time
25 | FROM customer_orders;
26 |
27 | SELECT * FROM customer_orders_temp;
28 | ```
29 |
30 | #### Result set:
31 | 
32 |
33 |
34 | ***
35 |
36 | ## runner_orders table
37 | - The pickup_time, distance, duration and cancellation columns in runner_orders table will need to be cleaned up before using them in the queries
38 | - In the pickup_time column, there are null values.
39 | - In the distance column, there are null values. It contains unit - km. The 'km' must also be stripped
40 | - In the duration column, there are null values. The 'minutes', 'mins' 'minute' must be stripped
41 | - In the cancellation column, there are blank spaces and null values.
42 |
43 | ```sql
44 | DROP TABLE IF EXISTS runner_orders_temp;
45 |
46 | CREATE TEMPORARY TABLE runner_orders_temp AS
47 |
48 | SELECT order_id,
49 | runner_id,
50 | CASE
51 | WHEN pickup_time LIKE 'null' THEN NULL
52 | ELSE pickup_time
53 | END AS pickup_time,
54 | CASE
55 | WHEN distance LIKE 'null' THEN NULL
56 | ELSE CAST(regexp_replace(distance, '[a-z]+', '') AS FLOAT)
57 | END AS distance,
58 | CASE
59 | WHEN duration LIKE 'null' THEN NULL
60 | ELSE CAST(regexp_replace(duration, '[a-z]+', '') AS FLOAT)
61 | END AS duration,
62 | CASE
63 | WHEN cancellation LIKE '' THEN NULL
64 | WHEN cancellation LIKE 'null' THEN NULL
65 | ELSE cancellation
66 | END AS cancellation
67 | FROM runner_orders;
68 |
69 | SELECT * FROM runner_orders_temp;
70 | ```
71 |
72 | #### Result set:
73 | 
74 |
75 |
76 | ***
77 | # Expanding the comma seperated string into rows
78 |
79 | ## pizza_recipes table
80 |
81 | #### pizza_recipes table
82 | - The toppings column in the pizza_recipes table is a comma separated string.
83 |
84 | 
85 |
86 | ### Method 1: Using a Procedure
87 | - A temporary table is created by calling a procedure which stores the pizza id and the topping in a separate row by splitting the comma separated string into multiple rows.
88 | - String functions are used to split the string
89 |
90 | ```sql
91 | DROP TABLE IF EXISTS pizza_recipes_temp;
92 |
93 | CREATE
94 | TEMPORARY TABLE pizza_recipes_temp(pizza_id int, topping int);
95 |
96 | DROP PROCEDURE IF EXISTS GetToppings;
97 |
98 | DELIMITER $$
99 | CREATE PROCEDURE GetToppings()
100 | BEGIN
101 | DECLARE i INT DEFAULT 0;
102 | DECLARE j INT DEFAULT 0;
103 | DECLARE n INT DEFAULT 0;
104 | DECLARE x INT DEFAULT 0;
105 | DECLARE id INT;
106 | DECLARE topping_in TEXT;
107 | DECLARE topping_out TEXT;
108 |
109 | SET i = 0;
110 | SELECT COUNT(*) FROM pizza_recipes INTO n;
111 |
112 | WHILE i < n DO -- Iterate per row
113 | SELECT pizza_id, toppings INTO id, topping_in FROM pizza_recipes LIMIT i,1 ; -- Select each row and store values in id, topping_in variables
114 | SET x = (CHAR_LENGTH(topping_in) - CHAR_LENGTH( REPLACE ( topping_in, ' ', '') ))+1; -- Find the number of toppings in the row
115 |
116 | SET j = 1;
117 | WHILE j <= x DO -- Iterate over each element in topping
118 | SET topping_out = TRIM(SUBSTRING_INDEX(SUBSTRING_INDEX(topping_in, ',', j), ',', -1));
119 | -- SUBSTRING_INDEX(topping_in, ',', j -> Returns a substring from a string before j occurences of comma
120 | -- (SUBSTRING_INDEX(SUBSTRING_INDEX(topping_in, ',', j), ',', -1)) -> Returns the last topping from the substring found above, element at -1 index
121 | INSERT INTO pizza_recipes_temp VALUES(id, topping_out); -- Insert pizza_id and the topping into table pizza_info
122 | SET j = j + 1; -- Increment the counter to find the next pizza topping in the row
123 | END WHILE;
124 | SET i = i + 1;-- Increment the counter to fetch the next row
125 | END WHILE;
126 | END$$
127 | DELIMITER ;
128 |
129 | CALL GetToppings();
130 |
131 |
132 | SELECT *
133 | FROM pizza_recipes_temp;
134 | ```
135 |
136 | #### Result set:
137 | 
138 |
139 | ### Method 2: Using [JSON table functions](https://dev.mysql.com/doc/refman/8.0/en/json-table-functions.html)
140 | - JSON functions are used to split the comma separated string into multiple rows.
141 | - json_array() converts the string to a JSON array
142 | - We enclose array elements with double quotes, this is performed using the replace function and we trim the resultant array
143 |
144 | ```sql
145 | SELECT *,
146 | json_array(toppings),
147 | replace(json_array(toppings), ',', '","'),
148 | trim(replace(json_array(toppings), ',', '","'))
149 | FROM pizza_runner.pizza_recipes;
150 | ```
151 | 
152 |
153 | - We convert the json data into a tabular data using json_table().
154 | - **Syntax**: JSON_TABLE(expr, path COLUMNS (column_list) [AS] alias)
155 | - It extracts data from a JSON document and returns it as a relational table having the specified columns
156 | - Each match for the path preceding the COLUMNS keyword maps to an individual row in the result table.
157 |
158 | ```sql
159 | '$[*]' -- The expression "$[*]" matches each element of the array and maps it to an individual row in the result table.
160 | columns (topping varchar(50) PATH '$') -- Within a column definition, "$" passes the entire match to the column;
161 | ```
162 |
163 |
164 | ```sql
165 | SELECT t.pizza_id, (j.topping)
166 | FROM pizza_recipes t
167 | JOIN json_table(trim(replace(json_array(t.toppings), ',', '","')), '$[*]' columns (topping varchar(50) PATH '$')) j ;
168 | ```
169 |
170 | #### Result set:
171 | 
172 |
173 | ***
174 | ## customer_orders_temp table
175 |
176 | #### customer_orders_temp table
177 | - The exclusions and extras columns in the pizza_recipes table are comma separated strings.
178 |
179 | 
180 |
181 | ```sql
182 | SELECT t.order_id,
183 | t.customer_id,
184 | t.pizza_id,
185 | trim(j1.exclusions) AS exclusions,
186 | trim(j2.extras) AS extras,
187 | t.order_time
188 | FROM customer_orders_temp t
189 | INNER JOIN json_table(trim(replace(json_array(t.exclusions), ',', '","')), '$[*]' columns (exclusions varchar(50) PATH '$')) j1
190 | INNER JOIN json_table(trim(replace(json_array(t.extras), ',', '","')), '$[*]' columns (extras varchar(50) PATH '$')) j2 ;
191 | ```
192 |
193 | #### Result set:
194 | 
195 |
196 |
197 | ***
198 |
199 | - [A. Pizza Metrics](https://github.com/manaswikamila05/8-Week-SQL-Challenge/blob/main/Case%20Study%20%23%202%20-%20Pizza%20Runner/A.%20Pizza%20metrics.md)
200 | - [B. Runner and Customer Experience](https://github.com/manaswikamila05/8-Week-SQL-Challenge/blob/main/Case%20Study%20%23%202%20-%20Pizza%20Runner/B.%20Runner%20and%20Customer%20Experience.md)
201 | - [C. Ingredient Optimisation](https://github.com/manaswikamila05/8-Week-SQL-Challenge/blob/main/Case%20Study%20%23%202%20-%20Pizza%20Runner/C.%20Ingredient%20Optimisation.md)
202 |
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/Case Study # 2 - Pizza Runner/A. Pizza metrics.md:
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1 | # :pizza: Case Study #2: Pizza runner - Pizza Metrics
2 |
3 | ## Case Study Questions
4 |
5 | 1. How many pizzas were ordered?
6 | 2. How many unique customer orders were made?
7 | 3. How many successful orders were delivered by each runner?
8 | 4. How many of each type of pizza was delivered?
9 | 5. How many Vegetarian and Meatlovers were ordered by each customer?
10 | 6. What was the maximum number of pizzas delivered in a single order?
11 | 7. For each customer, how many delivered pizzas had at least 1 change and how many had no changes?
12 | 8. How many pizzas were delivered that had both exclusions and extras?
13 | 9. What was the total volume of pizzas ordered for each hour of the day?
14 | 10. What was the volume of orders for each day of the week?
15 |
16 | ***
17 |
18 | ### 1. How many pizzas were ordered?
19 |
20 | ```sql
21 | SELECT count(pizza_id) AS "Total Number Of Pizzas Ordered"
22 | FROM pizza_runner.customer_orders;
23 | ```
24 |
25 | #### Result set:
26 | 
27 |
28 | ***
29 |
30 | ### 2. How many unique customer orders were made?
31 |
32 | ```sql
33 | SELECT
34 | COUNT(DISTINCT order_id) AS 'Number Of Unique Orders'
35 | FROM customer_orders_temp;
36 | ```
37 |
38 | #### Result set:
39 | 
40 |
41 | ***
42 |
43 | ### 3. How many successful orders were delivered by each runner?
44 |
45 | ```sql
46 | SELECT runner_id,
47 | count(order_id) AS 'Number Of Successful Orders'
48 | FROM pizza_runner.runner_orders_temp
49 | WHERE cancellation IS NULL
50 | GROUP BY runner_id;
51 | ```
52 |
53 | #### Result set:
54 | 
55 |
56 | ***
57 |
58 | ### 4. How many of each type of pizza was delivered?
59 |
60 | ```sql
61 |
62 | SELECT pizza_id,
63 | pizza_name,
64 | count(pizza_id) AS 'Number Of Pizzas Delivered'
65 | FROM pizza_runner.runner_orders_temp
66 | INNER JOIN customer_orders_temp USING (order_id)
67 | INNER JOIN pizza_names USING (pizza_id)
68 | WHERE cancellation IS NULL
69 | GROUP BY pizza_id;
70 | ```
71 |
72 | #### Result set:
73 | 
74 |
75 | ***
76 |
77 | ### 5. How many Vegetarian and Meatlovers were ordered by each customer?
78 |
79 | ```sql
80 | SELECT customer_id,
81 | pizza_name,
82 | count(pizza_id) AS 'Number Of Pizzas Ordered'
83 | FROM customer_orders_temp
84 | INNER JOIN pizza_names USING (pizza_id)
85 | GROUP BY customer_id,
86 | pizza_id
87 | ORDER BY customer_id ;
88 | ```
89 |
90 | #### Result set:
91 | 
92 |
93 | - The counts of the Meat lover and Vegetarian pizzas ordered by the customers is not discernible.
94 |
95 | ```sql
96 | SELECT customer_id,
97 | SUM(CASE
98 | WHEN pizza_id = 1 THEN 1
99 | ELSE 0
100 | END) AS 'Meat lover Pizza Count',
101 | SUM(CASE
102 | WHEN pizza_id = 2 THEN 1
103 | ELSE 0
104 | END) AS 'Vegetarian Pizza Count'
105 | FROM customer_orders_temp
106 | GROUP BY customer_id
107 | ORDER BY customer_id;
108 | ```
109 |
110 | #### Result set:
111 | 
112 |
113 | ***
114 |
115 | ### 6. What was the maximum number of pizzas delivered in a single order?
116 |
117 | ```sql
118 | SELECT customer_id,
119 | order_id,
120 | count(order_id) AS pizza_count
121 | FROM customer_orders_temp
122 | GROUP BY order_id
123 | ORDER BY pizza_count DESC
124 | LIMIT 1;
125 | ```
126 |
127 | #### Result set:
128 | 
129 |
130 | ***
131 |
132 | ### 7. For each customer, how many delivered pizzas had at least 1 change and how many had no changes?
133 | - at least 1 change -> either exclusion or extras
134 | - no changes -> exclusion and extras are NULL
135 |
136 | ```sql
137 | SELECT customer_id,
138 | SUM(CASE
139 | WHEN (exclusions IS NOT NULL
140 | OR extras IS NOT NULL) THEN 1
141 | ELSE 0
142 | END) AS change_in_pizza,
143 | SUM(CASE
144 | WHEN (exclusions IS NULL
145 | AND extras IS NULL) THEN 1
146 | ELSE 0
147 | END) AS no_change_in_pizza
148 | FROM customer_orders_temp
149 | INNER JOIN runner_orders_temp USING (order_id)
150 | WHERE cancellation IS NULL
151 | GROUP BY customer_id
152 | ORDER BY customer_id;
153 | ```
154 |
155 | #### Result set:
156 | 
157 |
158 | ***
159 |
160 | ### 8. How many pizzas were delivered that had both exclusions and extras?
161 |
162 | ```sql
163 |
164 | SELECT customer_id,
165 | SUM(CASE
166 | WHEN (exclusions IS NOT NULL
167 | AND extras IS NOT NULL) THEN 1
168 | ELSE 0
169 | END) AS both_change_in_pizza
170 | FROM customer_orders_temp
171 | INNER JOIN runner_orders_temp USING (order_id)
172 | WHERE cancellation IS NULL
173 | GROUP BY customer_id
174 | ORDER BY customer_id;
175 | ```
176 |
177 | #### Result set:
178 | 
179 |
180 | ***
181 |
182 | ### 9. What was the total volume of pizzas ordered for each hour of the day?
183 |
184 | ```sql
185 | SELECT hour(order_time) AS 'Hour',
186 | count(order_id) AS 'Number of pizzas ordered',
187 | round(100*count(order_id) /sum(count(order_id)) over(), 2) AS 'Volume of pizzas ordered'
188 | FROM pizza_runner.customer_orders_temp
189 | GROUP BY 1
190 | ORDER BY 1;
191 | ```
192 |
193 | #### Result set:
194 | 
195 |
196 | ***
197 |
198 | ### 10. What was the volume of orders for each day of the week?
199 | - The DAYOFWEEK() function returns the weekday index for a given date ( 1=Sunday, 2=Monday, 3=Tuesday, 4=Wednesday, 5=Thursday, 6=Friday, 7=Saturday )
200 | - DAYNAME() returns the name of the week day
201 |
202 | ```sql
203 | SELECT dayname(order_time) AS 'Day Of Week',
204 | count(order_id) AS 'Number of pizzas ordered',
205 | round(100*count(order_id) /sum(count(order_id)) over(), 2) AS 'Volume of pizzas ordered'
206 | FROM pizza_runner.customer_orders_temp
207 | GROUP BY 1
208 | ORDER BY 2 DESC;
209 | ```
210 |
211 | #### Result set:
212 | 
213 |
214 | ***
215 |
216 | Click [here](https://github.com/manaswikamila05/8-Week-SQL-Challenge/blob/main/Case%20Study%20%23%202%20-%20Pizza%20Runner/B.%20Runner%20and%20Customer%20Experience.md) to view the solution of B. Runner and Customer Experience!
217 |
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/Case Study # 2 - Pizza Runner/B. Runner and Customer Experience.md:
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1 | # :pizza: Case Study #2: Pizza runner - Runner and Customer Experience
2 |
3 | ## Case Study Questions
4 |
5 | 1. How many runners signed up for each 1 week period? (i.e. week starts 2021-01-01)
6 | 2. What was the average time in minutes it took for each runner to arrive at the Pizza Runner HQ to pickup the order?
7 | 3. Is there any relationship between the number of pizzas and how long the order takes to prepare?
8 | 4. What was the average distance travelled for each customer?
9 | 5. What was the difference between the longest and shortest delivery times for all orders?
10 | 6. What was the average speed for each runner for each delivery and do you notice any trend for these values?
11 | 7. What is the successful delivery percentage for each runner?
12 |
13 | ***
14 |
15 | ### 1. How many runners signed up for each 1 week period? (i.e. week starts 2021-01-01)
16 | - Returned week number is between 0 and 52 or 0 and 53.
17 | - Default mode of the week =0 -> First day of the week is Sunday
18 | - Extract week -> WEEK(registration_date) or EXTRACT(week from registration_date)
19 |
20 | ```sql
21 | SELECT week(registration_date) as 'Week of registration',
22 | count(runner_id) as 'Number of runners'
23 | FROM pizza_runner.runners
24 | GROUP BY 1;
25 | ```
26 |
27 | #### Result set:
28 | 
29 |
30 | ***
31 |
32 | ### 2. What was the average time in minutes it took for each runner to arrive at the Pizza Runner HQ to pickup the order?
33 |
34 | ```sql
35 | SELECT runner_id,
36 | TIMESTAMPDIFF(MINUTE, order_time, pickup_time) AS runner_pickup_time,
37 | round(avg(TIMESTAMPDIFF(MINUTE, order_time, pickup_time)), 2) avg_runner_pickup_time
38 | FROM runner_orders_temp
39 | INNER JOIN customer_orders_temp USING (order_id)
40 | WHERE cancellation IS NULL
41 | GROUP BY runner_id;
42 | ```
43 |
44 | #### Result set:
45 | 
46 |
47 | ***
48 |
49 | ### 3. Is there any relationship between the number of pizzas and how long the order takes to prepare?
50 |
51 | ```sql
52 | WITH order_count_cte AS
53 | (SELECT order_id,
54 | COUNT(order_id) AS pizzas_order_count,
55 | TIMESTAMPDIFF(MINUTE, order_time, pickup_time) AS prep_time
56 | FROM runner_orders_temp
57 | INNER JOIN customer_orders_temp USING (order_id)
58 | WHERE cancellation IS NULL
59 | GROUP BY order_id)
60 | SELECT pizzas_order_count,
61 | round(avg(prep_time), 2)
62 | FROM order_count_cte
63 | GROUP BY pizzas_order_count;
64 | ```
65 |
66 | #### Result set:
67 | 
68 |
69 | ***
70 |
71 | ### 4. What was the average distance travelled for each customer?
72 |
73 | ```sql
74 | SELECT customer_id,
75 | round(avg(distance), 2) AS 'average_distance_travelled'
76 | FROM runner_orders_temp
77 | INNER JOIN customer_orders_temp USING (order_id)
78 | WHERE cancellation IS NULL
79 | GROUP BY customer_id;
80 | ```
81 |
82 | #### Result set:
83 | 
84 |
85 | ***
86 |
87 | ### 5. What was the difference between the longest and shortest delivery times for all orders?
88 |
89 | ```sql
90 | SELECT MIN(duration) minimum_duration,
91 | MAX(duration) AS maximum_duration,
92 | MAX(duration) - MIN(duration) AS maximum_difference
93 | FROM runner_orders_temp;
94 | ```
95 |
96 | #### Result set:
97 | 
98 |
99 | ***
100 |
101 | ### 6. What was the average speed for each runner for each delivery and do you notice any trend for these values?
102 |
103 | ```sql
104 | SELECT runner_id,
105 | distance AS distance_km,
106 | round(duration/60, 2) AS duration_hr,
107 | round(distance*60/duration, 2) AS average_speed
108 | FROM runner_orders_temp
109 | WHERE cancellation IS NULL
110 | ORDER BY runner_id;
111 | ```
112 |
113 | #### Result set:
114 | 
115 |
116 | ***
117 |
118 | ### 7. What is the successful delivery percentage for each runner?
119 |
120 | ```sql
121 | SELECT runner_id,
122 | COUNT(pickup_time) AS delivered_orders,
123 | COUNT(*) AS total_orders,
124 | ROUND(100 * COUNT(pickup_time) / COUNT(*)) AS delivery_success_percentage
125 | FROM runner_orders_temp
126 | GROUP BY runner_id
127 | ORDER BY runner_id;
128 | ```
129 |
130 | #### Result set:
131 | 
132 |
133 | ***
134 |
135 | Click [here](https://github.com/manaswikamila05/8-Week-SQL-Challenge/blob/main/Case%20Study%20%23%202%20-%20Pizza%20Runner/C.%20Ingredient%20Optimisation.md) to view the solution of C. Ingredient Optimisation!
136 |
137 |
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/Case Study # 2 - Pizza Runner/C. Ingredient Optimisation.md:
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1 | # :pizza: Case Study #2: Pizza runner - Ingredient Optimisation WIP
2 |
3 | ## Case Study Questions
4 |
5 | 1. What are the standard ingredients for each pizza?
6 | 2. What was the most commonly added extra?
7 | 3. What was the most common exclusion?
8 | 4. Generate an order item for each record in the customers_orders table in the format of one of the following:
9 | - Meat Lovers
10 | - Meat Lovers - Exclude Beef
11 | - Meat Lovers - Extra Bacon
12 | - Meat Lovers - Exclude Cheese, Bacon - Extra Mushroom, Peppers
13 | 5. Generate an alphabetically ordered comma separated ingredient list for each pizza order from the customer_orders table and add a 2x in front of any relevant ingredients
14 | - For example: "Meat Lovers: 2xBacon, Beef, ... , Salami"
15 | 6. What is the total quantity of each ingredient used in all delivered pizzas sorted by most frequent first?
16 |
17 | ***
18 |
19 | ## Temporary tables created to solve the below queries
20 |
21 | ```sql
22 | DROP TABLE row_split_customer_orders_temp;
23 |
24 | CREATE
25 | TEMPORARY TABLE row_split_customer_orders_temp AS
26 | SELECT t.row_num,
27 | t.order_id,
28 | t.customer_id,
29 | t.pizza_id,
30 | trim(j1.exclusions) AS exclusions,
31 | trim(j2.extras) AS extras,
32 | t.order_time
33 | FROM
34 | (SELECT *,
35 | row_number() over() AS row_num
36 | FROM customer_orders_temp) t
37 | INNER JOIN json_table(trim(replace(json_array(t.exclusions), ',', '","')),
38 | '$[*]' columns (exclusions varchar(50) PATH '$')) j1
39 | INNER JOIN json_table(trim(replace(json_array(t.extras), ',', '","')),
40 | '$[*]' columns (extras varchar(50) PATH '$')) j2 ;
41 |
42 |
43 | SELECT *
44 | FROM row_split_customer_orders_temp;
45 | ```
46 | 
47 |
48 |
49 | ```sql
50 | DROP TABLE row_split_pizza_recipes_temp;
51 |
52 | CREATE
53 | TEMPORARY TABLE row_split_pizza_recipes_temp AS
54 | SELECT t.pizza_id,
55 | trim(j.topping) AS topping_id
56 | FROM pizza_recipes t
57 | JOIN json_table(trim(replace(json_array(t.toppings), ',', '","')),
58 | '$[*]' columns (topping varchar(50) PATH '$')) j ;
59 |
60 |
61 | SELECT *
62 | FROM row_split_pizza_recipes_temp;
63 | ```
64 | 
65 |
66 |
67 | ```sql
68 | DROP TABLE IF EXISTS standard_ingredients;
69 |
70 | CREATE
71 | TEMPORARY TABLE standard_ingredients AS
72 | SELECT pizza_id,
73 | pizza_name,
74 | group_concat(DISTINCT topping_name) 'standard_ingredients'
75 | FROM row_split_pizza_recipes_temp
76 | INNER JOIN pizza_names USING (pizza_id)
77 | INNER JOIN pizza_toppings USING (topping_id)
78 | GROUP BY pizza_name
79 | ORDER BY pizza_id;
80 |
81 | SELECT *
82 | FROM standard_ingredients;
83 | ```
84 | 
85 |
86 |
87 | ```sql
88 |
89 | ```
90 |
91 |
92 | ### 1. What are the standard ingredients for each pizza?
93 |
94 | ```sql
95 | SELECT *
96 | FROM standard_ingredients;
97 | ```
98 |
99 | #### Result set:
100 | 
101 |
102 | ***
103 |
104 | ### 2. What was the most commonly added extra?
105 |
106 | ```sql
107 | WITH extra_count_cte AS
108 | (SELECT trim(extras) AS extra_topping,
109 | count(*) AS purchase_counts
110 | FROM row_split_customer_orders_temp
111 | WHERE extras IS NOT NULL
112 | GROUP BY extras)
113 | SELECT topping_name,
114 | purchase_counts
115 | FROM extra_count_cte
116 | INNER JOIN pizza_toppings ON extra_count_cte.extra_topping = pizza_toppings.topping_id
117 | LIMIT 1;
118 | ```
119 |
120 | #### Result set:
121 | 
122 |
123 | ***
124 |
125 | ### 3. What was the most common exclusion?
126 |
127 | ```sql
128 | WITH extra_count_cte AS
129 | (SELECT trim(exclusions) AS extra_topping,
130 | count(*) AS purchase_counts
131 | FROM row_split_customer_orders_temp
132 | WHERE exclusions IS NOT NULL
133 | GROUP BY exclusions)
134 | SELECT topping_name,
135 | purchase_counts
136 | FROM extra_count_cte
137 | INNER JOIN pizza_toppings ON extra_count_cte.extra_topping = pizza_toppings.topping_id
138 | LIMIT 1;
139 | ```
140 |
141 | #### Result set:
142 | 
143 |
144 | ***
145 |
146 | ### 4. Generate an order item for each record in the customers_orders table in the format of one of the following:
147 | - Meat Lovers
148 | - Meat Lovers - Exclude Beef
149 | - Meat Lovers - Extra Bacon
150 | - Meat Lovers - Exclude Cheese, Bacon - Extra Mushroom, Peppers
151 |
152 | ```sql
153 | WITH order_summary_cte AS
154 | (SELECT pizza_name,
155 | row_num,
156 | order_id,
157 | customer_id,
158 | excluded_topping,
159 | t2.topping_name AS extras_topping
160 | FROM
161 | (SELECT *,
162 | topping_name AS excluded_topping
163 | FROM row_split_customer_orders_temp
164 | LEFT JOIN standard_ingredients USING (pizza_id)
165 | LEFT JOIN pizza_toppings ON topping_id = exclusions) t1
166 | LEFT JOIN pizza_toppings t2 ON t2.topping_id = extras)
167 | SELECT order_id,
168 | customer_id,
169 | CASE
170 | WHEN excluded_topping IS NULL
171 | AND extras_topping IS NULL THEN pizza_name
172 | WHEN extras_topping IS NULL
173 | AND excluded_topping IS NOT NULL THEN concat(pizza_name, ' - Exclude ', GROUP_CONCAT(DISTINCT excluded_topping))
174 | WHEN excluded_topping IS NULL
175 | AND extras_topping IS NOT NULL THEN concat(pizza_name, ' - Include ', GROUP_CONCAT(DISTINCT extras_topping))
176 | ELSE concat(pizza_name, ' - Include ', GROUP_CONCAT(DISTINCT extras_topping), ' - Exclude ', GROUP_CONCAT(DISTINCT excluded_topping))
177 | END AS order_item
178 | FROM order_summary_cte
179 | GROUP BY row_num;
180 | ```
181 |
182 | #### Result set:
183 | 
184 |
185 | ***
186 |
187 | ### 5. Generate an alphabetically ordered comma separated ingredient list for each pizza order from the customer_orders table and add a 2x in front of any relevant ingredients
188 | - For example: "Meat Lovers: 2xBacon, Beef, ... , Salami"
189 |
190 | ```sql
191 |
192 | ```
193 |
194 | #### Result set:
195 |
196 | ***
197 |
198 | ### 6. What is the total quantity of each ingredient used in all delivered pizzas sorted by most frequent first?
199 |
200 | ```sql
201 |
202 | ```
203 |
204 | #### Result set:
205 |
206 | ***
207 |
208 | Click [here](https://github.com/manaswikamila05/8-Week-SQL-Challenge/blob/main/Case%20Study%20%23%202%20-%20Pizza%20Runner/D.%20Pricing%20and%20Ratings.md) to view the solution of D. Pricing and Ratings!
209 |
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/Case Study # 2 - Pizza Runner/D. Pricing and Ratings.md:
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1 | # :pizza: Case Study #2: Pizza runner - Pricing and Ratings
2 |
3 | ## Case Study Questions
4 |
5 | 1. If a Meat Lovers pizza costs $12 and Vegetarian costs $10 and there were no charges for changes - how much money has Pizza Runner made so far if there are no delivery fees?
6 | 2. What if there was an additional $1 charge for any pizza extras? Add cheese is $1 extra
7 | 3. The Pizza Runner team now wants to add an additional ratings system that allows customers to rate their runner, how would you design an additional table for this new dataset - generate a schema for this new table and insert your own data for ratings for each successful customer order between 1 to 5.
8 | 4. Using your newly generated table - can you join all of the information together to form a table which has the following information for successful deliveries?
9 | - customer_id
10 | - order_id
11 | - runner_id
12 | - rating
13 | - order_time
14 | - pickup_time
15 | - Time between order and pickup
16 | - Delivery duration
17 | - Average speed
18 | - Total number of pizzas
19 | 5. If a Meat Lovers pizza was $12 and Vegetarian $10 fixed prices with no cost for extras and each runner is paid $0.30 per kilometre traveled - how much money does Pizza Runner have left over after these deliveries?
20 |
21 | ***
22 |
23 | ### 1. If a Meat Lovers pizza costs $12 and Vegetarian costs $10 and there were no charges for changes - how much money has Pizza Runner made so far if there are no delivery fees?
24 |
25 | ```sql
26 | SELECT CONCAT('$', SUM(CASE
27 | WHEN pizza_id = 1 THEN 12
28 | ELSE 10
29 | END)) AS total_revenue
30 | FROM customer_orders_temp
31 | INNER JOIN pizza_names USING (pizza_id)
32 | INNER JOIN runner_orders_temp USING (order_id)
33 | WHERE cancellation IS NULL;
34 | ```
35 |
36 | #### Result set:
37 | 
38 |
39 | ***
40 |
41 | ### 2. What if there was an additional $1 charge for any pizza extras? Add cheese is $1 extra
42 |
43 | ```sql
44 | SELECT CONCAT('$', topping_revenue+ pizza_revenue) AS total_revenue
45 | FROM
46 | (SELECT SUM(CASE
47 | WHEN pizza_id = 1 THEN 12
48 | ELSE 10
49 | END) AS pizza_revenue,
50 | sum(topping_count) AS topping_revenue
51 | FROM
52 | (SELECT *,
53 | length(extras) - length(replace(extras, ",", ""))+1 AS topping_count
54 | FROM customer_orders_temp
55 | INNER JOIN pizza_names USING (pizza_id)
56 | INNER JOIN runner_orders_temp USING (order_id)
57 | WHERE cancellation IS NULL
58 | ORDER BY order_id)t1) t2;
59 | ```
60 |
61 | #### Result set:
62 | 
63 |
64 | ***
65 |
66 | ### 3. The Pizza Runner team now wants to add an additional ratings system that allows customers to rate their runner, how would you design an additional table for this new dataset - generate a schema for this new table and insert your own data for ratings for each successful customer order between 1 to 5.
67 |
68 | ```sql
69 | DROP TABLE IF EXISTS runner_rating;
70 |
71 | CREATE TABLE runner_rating (order_id INTEGER, rating INTEGER, review VARCHAR(100)) ;
72 |
73 | -- Order 6 and 9 were cancelled
74 | INSERT INTO runner_rating
75 | VALUES ('1', '1', 'Really bad service'),
76 | ('2', '1', NULL),
77 | ('3', '4', 'Took too long..."),
78 | ('4', '1','Runner was lost, delivered it AFTER an hour. Pizza arrived cold' ),
79 | ('5', '2', ''Good service'),
80 | ('7', '5', 'It was great, good service and fast'),
81 | ('8', '2', 'He tossed it on the doorstep, poor service'),
82 | ('10', '5', 'Delicious!, he delivered it sooner than expected too!');
83 |
84 |
85 | SELECT *
86 | FROM runner_rating;
87 | ```
88 |
89 | #### Result set:
90 | 
91 |
92 | ***
93 |
94 | ### 4. Using your newly generated table - can you join all of the information together to form a table which has the following information for successful deliveries?
95 | - customer_id
96 | - order_id
97 | - runner_id
98 | - rating
99 | - order_time
100 | - pickup_time
101 | - Time between order and pickup
102 | - Delivery duration
103 | - Average speed
104 | - Total number of pizzas
105 |
106 | ```sql
107 |
108 | SELECT customer_id,
109 | order_id,
110 | runner_id,
111 | rating,
112 | order_time,
113 | pickup_time,
114 | TIMESTAMPDIFF(MINUTE, order_time, pickup_time) pick_up_time,
115 | duration AS delivery_duration,
116 | round(distance*60/duration, 2) AS average_speed,
117 | count(pizza_id) AS total_pizza_count
118 | FROM customer_orders_temp
119 | INNER JOIN runner_orders_temp USING (order_id)
120 | INNER JOIN runner_rating USING (order_id)
121 | GROUP BY order_id ;
122 | ```
123 |
124 | #### Result set:
125 | 
126 |
127 | ***
128 |
129 | ### 5. If a Meat Lovers pizza was $12 and Vegetarian $10 fixed prices with no cost for extras and each runner is paid $0.30 per kilometre traveled - how much money does Pizza Runner have left over after these deliveries?
130 |
131 | ```sql
132 | SELECT concat('$', round(sum(pizza_cost-delivery_cost), 2)) AS pizza_runner_revenue
133 | FROM
134 | (SELECT order_id,
135 | distance,
136 | sum(pizza_cost) AS pizza_cost,
137 | round(0.30*distance, 2) AS delivery_cost
138 | FROM
139 | (SELECT *,
140 | (CASE
141 | WHEN pizza_id = 1 THEN 12
142 | ELSE 10
143 | END) AS pizza_cost
144 | FROM customer_orders_temp
145 | INNER JOIN pizza_names USING (pizza_id)
146 | INNER JOIN runner_orders_temp USING (order_id)
147 | WHERE cancellation IS NULL
148 | ORDER BY order_id) t1
149 | GROUP BY order_id
150 | ORDER BY order_id) t2;
151 | ```
152 |
153 | #### Result set:
154 | 
155 |
156 | ***
157 |
158 | Click [here](https://github.com/manaswikamila05/8-Week-SQL-Challenge/blob/main/Case%20Study%20%23%202%20-%20Pizza%20Runner/E.%20Bonus%20Questions.md) to view the solution of E. Bonus Questions!
159 |
160 | Click [here](https://github.com/manaswikamila05/8-Week-SQL-Challenge) to move back to the 8-Week-SQL-Challenge repository!
161 |
162 |
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/Case Study # 2 - Pizza Runner/E. Bonus Questions.md:
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1 | # :pizza: Case Study #2: Pizza runner - Bonus Question
2 |
3 | If Danny wants to expand his range of pizzas - how would this impact the existing data design? Write an INSERT statement to demonstrate what would happen if a new Supreme pizza with all the toppings was added to the Pizza Runner menu?
4 |
5 | Supreme pizza has all toppings on it.
6 |
7 | 
8 |
9 | We'd have to insert data into pizza_names and pizza_recipes tables
10 |
11 | ***
12 |
13 | ```sql
14 | INSERT INTO pizza_names VALUES(3, 'Supreme');
15 | SELECT * FROM pizza_names;
16 | ```
17 | 
18 |
19 | ```sql
20 | INSERT INTO pizza_recipes
21 | VALUES(3, (SELECT GROUP_CONCAT(topping_id SEPARATOR ', ') FROM pizza_toppings));
22 | ```
23 |
24 | ```sql
25 | SELECT * FROM pizza_recipes;
26 | ```
27 | 
28 |
29 | ***
30 |
31 | ```sql
32 | SELECT *
33 | FROM pizza_names
34 | INNER JOIN pizza_recipes USING(pizza_id);
35 | ```
36 | 
37 |
38 | ***
39 |
40 | Click [here](https://github.com/manaswikamila05/8-Week-SQL-Challenge) to move back to the 8-Week-SQL-Challenge repository!
41 |
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/Case Study # 2 - Pizza Runner/ERD.jpg:
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https://raw.githubusercontent.com/manaswikamila05/8-Week-SQL-Challenge/f655c7ea30b64979e1980f7030a3740cab034b82/Case Study # 2 - Pizza Runner/ERD.jpg
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/Case Study # 2 - Pizza Runner/Pizza Runner Schema.sql:
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1 | DROP SCHEMA pizza_runner;
2 |
3 | CREATE SCHEMA pizza_runner;
4 | USE pizza_runner;
5 |
6 | -- The runners table shows the registration_date for each new runner
7 | DROP TABLE IF EXISTS runners;
8 | CREATE TABLE runners (
9 | runner_id INTEGER,
10 | registration_date DATE
11 | );
12 | INSERT INTO runners
13 | (runner_id, registration_date)
14 | VALUES
15 | (1, '2021-01-01'),
16 | (2, '2021-01-03'),
17 | (3, '2021-01-08'),
18 | (4, '2021-01-15');
19 |
20 | -- Customer pizza orders are captured in the customer_orders table with 1 row for each individual pizza that is part of the order.
21 | DROP TABLE IF EXISTS customer_orders;
22 | CREATE TABLE customer_orders (
23 | order_id INTEGER,
24 | customer_id INTEGER,
25 | pizza_id INTEGER,
26 | exclusions VARCHAR(4),
27 | extras VARCHAR(4),
28 | order_time TIMESTAMP
29 | );
30 |
31 |
32 | INSERT INTO customer_orders
33 | (order_id, customer_id, pizza_id, exclusions, extras, order_time)
34 | VALUES
35 | ('1', '101', '1', '', '', '2020-01-01 18:05:02'),
36 | ('2', '101', '1', '', '', '2020-01-01 19:00:52'),
37 | ('3', '102', '1', '', '', '2020-01-02 23:51:23'),
38 | ('3', '102', '2', '', NULL, '2020-01-02 23:51:23'),
39 | ('4', '103', '1', '4', '', '2020-01-04 13:23:46'),
40 | ('4', '103', '1', '4', '', '2020-01-04 13:23:46'),
41 | ('4', '103', '2', '4', '', '2020-01-04 13:23:46'),
42 | ('5', '104', '1', 'null', '1', '2020-01-08 21:00:29'),
43 | ('6', '101', '2', 'null', 'null', '2020-01-08 21:03:13'),
44 | ('7', '105', '2', 'null', '1', '2020-01-08 21:20:29'),
45 | ('8', '102', '1', 'null', 'null', '2020-01-09 23:54:33'),
46 | ('9', '103', '1', '4', '1, 5', '2020-01-10 11:22:59'),
47 | ('10', '104', '1', 'null', 'null', '2020-01-11 18:34:49'),
48 | ('10', '104', '1', '2, 6', '1, 4', '2020-01-11 18:34:49');
49 |
50 | -- After each orders are received through the system - they are assigned to a runner - however not all orders are fully completed and can be cancelled by the restaurant or the customer.
51 | DROP TABLE IF EXISTS runner_orders;
52 | CREATE TABLE runner_orders (
53 | order_id INTEGER,
54 | runner_id INTEGER,
55 | pickup_time VARCHAR(19),
56 | distance VARCHAR(7),
57 | duration VARCHAR(10),
58 | cancellation VARCHAR(23)
59 | );
60 |
61 | INSERT INTO runner_orders
62 | (order_id, runner_id, pickup_time, distance, duration, cancellation)
63 | VALUES
64 | ('1', '1', '2020-01-01 18:15:34', '20km', '32 minutes', ''),
65 | ('2', '1', '2020-01-01 19:10:54', '20km', '27 minutes', ''),
66 | ('3', '1', '2020-01-03 00:12:37', '13.4km', '20 mins', NULL),
67 | ('4', '2', '2020-01-04 13:53:03', '23.4', '40', NULL),
68 | ('5', '3', '2020-01-08 21:10:57', '10', '15', NULL),
69 | ('6', '3', 'null', 'null', 'null', 'Restaurant Cancellation'),
70 | ('7', '2', '2020-01-08 21:30:45', '25km', '25mins', 'null'),
71 | ('8', '2', '2020-01-10 00:15:02', '23.4 km', '15 minute', 'null'),
72 | ('9', '2', 'null', 'null', 'null', 'Customer Cancellation'),
73 | ('10', '1', '2020-01-11 18:50:20', '10km', '10minutes', 'null');
74 |
75 | -- Pizza Runner only has 2 pizzas available the Meat Lovers or Vegetarian!
76 | DROP TABLE IF EXISTS pizza_names;
77 | CREATE TABLE pizza_names (
78 | pizza_id INTEGER,
79 | pizza_name TEXT
80 | );
81 | INSERT INTO pizza_names
82 | (pizza_id, pizza_name)
83 | VALUES
84 | (1, 'Meatlovers'),
85 | (2, 'Vegetarian');
86 |
87 | -- Each pizza_id has a standard set of toppings which are used as part of the pizza recipe.
88 | DROP TABLE IF EXISTS pizza_recipes;
89 | CREATE TABLE pizza_recipes (
90 | pizza_id INTEGER,
91 | toppings TEXT
92 | );
93 | INSERT INTO pizza_recipes
94 | (pizza_id, toppings)
95 | VALUES
96 | (1, '1, 2, 3, 4, 5, 6, 8, 10'),
97 | (2, '4, 6, 7, 9, 11, 12');
98 |
99 | -- table contains all of the topping_name values with their corresponding topping_id value
100 | DROP TABLE IF EXISTS pizza_toppings;
101 | CREATE TABLE pizza_toppings (
102 | topping_id INTEGER,
103 | topping_name TEXT
104 | );
105 | INSERT INTO pizza_toppings
106 | (topping_id, topping_name)
107 | VALUES
108 | (1, 'Bacon'),
109 | (2, 'BBQ Sauce'),
110 | (3, 'Beef'),
111 | (4, 'Cheese'),
112 | (5, 'Chicken'),
113 | (6, 'Mushrooms'),
114 | (7, 'Onions'),
115 | (8, 'Pepperoni'),
116 | (9, 'Peppers'),
117 | (10, 'Salami'),
118 | (11, 'Tomatoes'),
119 | (12, 'Tomato Sauce');
120 |
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/Case Study # 2 - Pizza Runner/README.md:
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1 | # :pizza: Case Study #2: Pizza Runner
2 |
3 | 
4 |
5 | View the case study [here](https://8weeksqlchallenge.com/case-study-2/)
6 |
7 | ## Table Of Contents
8 | - [Introduction](#introduction)
9 | - [Dataset](#dataset)
10 | - [Entity Relationship Diagram](#entity-relationship-diagram)
11 | - [Data Clean](#data-clean)
12 | - [Case Study Solutions](#case-study-solutions)
13 |
14 | ## Introduction
15 | Danny was scrolling through his Instagram feed when something really caught his eye - “80s Retro Styling and Pizza Is The Future!”
16 |
17 | Danny was sold on the idea, but he knew that pizza alone was not going to help him get seed funding to expand his new Pizza Empire - so he had one more genius idea to combine with it - he was going to Uberize it - and so Pizza Runner was launched!
18 |
19 | Danny started by recruiting “runners” to deliver fresh pizza from Pizza Runner Headquarters (otherwise known as Danny’s house) and also maxed out his credit card to pay freelance developers to build a mobile app to accept orders from customers.
20 |
21 | ## Dataset
22 | Key datasets for this case study
23 | - **runners** : The table shows the registration_date for each new runner
24 | - **customer_orders** : Customer pizza orders are captured in the customer_orders table with 1 row for each individual pizza that is part of the order. The pizza_id relates to the type of pizza which was ordered whilst the exclusions are the ingredient_id values which should be removed from the pizza and the extras are the ingredient_id values which need to be added to the pizza.
25 | - **runner_orders** : After each orders are received through the system - they are assigned to a runner - however not all orders are fully completed and can be cancelled by the restaurant or the customer. The pickup_time is the timestamp at which the runner arrives at the Pizza Runner headquarters to pick up the freshly cooked pizzas. The distance and duration fields are related to how far and long the runner had to travel to deliver the order to the respective customer.
26 | - **pizza_names** : Pizza Runner only has 2 pizzas available the Meat Lovers or Vegetarian!
27 | - **pizza_recipes** : Each pizza_id has a standard set of toppings which are used as part of the pizza recipe.
28 | - **pizza_toppings** : The table contains all of the topping_name values with their corresponding topping_id value
29 |
30 | ## Entity Relationship Diagram
31 | 
32 |
33 | ## Data Clean
34 | There are some known data issues with few tables. Data cleaning was performed and saved in temporary tables before attempting the case study.
35 |
36 | **customer_orders table**
37 | - The exclusions and extras columns in customer_orders table will need to be cleaned up before using them in the queries
38 | - In the exclusions and extras columns, there are blank spaces and null values.
39 |
40 | **runner_orders table**
41 | - The pickup_time, distance, duration and cancellation columns in runner_orders table will need to be cleaned up before using them in the queries
42 | - In the pickup_time column, there are null values.
43 | - In the distance column, there are null values. It contains unit - km. The 'km' must also be stripped
44 | - In the duration column, there are null values. The 'minutes', 'mins' 'minute' must be stripped
45 | - In the cancellation column, there are blank spaces and null values.
46 |
47 | Click [here](https://github.com/manaswikamila05/8-Week-SQL-Challenge/blob/main/Case%20Study%20%23%202%20-%20Pizza%20Runner/Data%20Clean.md) to view the data cleaning peformed.
48 |
49 | ## Case Study Solutions
50 | - [A. Pizza Metrics](https://github.com/manaswikamila05/8-Week-SQL-Challenge/blob/main/Case%20Study%20%23%202%20-%20Pizza%20Runner/A.%20Pizza%20metrics.md)
51 | - [B. Runner and Customer Experience](https://github.com/manaswikamila05/8-Week-SQL-Challenge/blob/main/Case%20Study%20%23%202%20-%20Pizza%20Runner/B.%20Runner%20and%20Customer%20Experience.md)
52 | - [C. Ingredient Optimisation](https://github.com/manaswikamila05/8-Week-SQL-Challenge/blob/main/Case%20Study%20%23%202%20-%20Pizza%20Runner/C.%20Ingredient%20Optimisation.md)
53 | - [D. Pricing and Ratings](https://github.com/manaswikamila05/8-Week-SQL-Challenge/blob/main/Case%20Study%20%23%202%20-%20Pizza%20Runner/D.%20Pricing%20and%20Ratings.md)
54 | - [E. Bonus Questions](https://github.com/manaswikamila05/8-Week-SQL-Challenge/blob/main/Case%20Study%20%23%202%20-%20Pizza%20Runner/E.%20Bonus%20Questions.md)
55 |
--------------------------------------------------------------------------------
/Case Study # 3 - Foodie-Fi/A. Customer Journey.md:
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1 | ## :avocado: Case Study #3: Foodie-Fi - Customer Journey
2 |
3 | Based off the 8 sample customers provided in the sample from the subscriptions table, write a brief description about each customer’s onboarding journey.
4 |
5 | Try to keep it as short as possible - you may also want to run some sort of join to make your explanations a bit easier!
6 |
7 | ***
8 |
9 | ### Distinct customer_id in the dataset
10 |
11 | ```sql
12 | SELECT count(distinct(customer_id)) AS 'distinct customers'
13 | FROM subscriptions;
14 | ```
15 |
16 | #### Result set:
17 | 
18 |
19 | ***
20 |
21 | Selecting the following random customer_id's from the subscriptions table to view their onboarding journey.
22 | Checking the following customer_id's : 1,21,73,87,99,193,290,400
23 |
24 | ### Customer 1
25 |
26 | ```sql
27 | SELECT customer_id,
28 | plan_id,
29 | plan_name,
30 | start_date
31 | FROM subscriptions
32 | JOIN plans USING (plan_id)
33 | WHERE customer_id =1;
34 | ```
35 |
36 | #### Result set:
37 | 
38 |
39 | - Customer started the free trial on 1 August 2020
40 | - They subscribed to the basic monthly during the seven day the trial period to continue the subscription
41 | ***
42 |
43 | ### Customer 21
44 |
45 | ```sql
46 | SELECT customer_id,
47 | plan_id,
48 | plan_name,
49 | start_date
50 | FROM subscriptions
51 | JOIN plans USING (plan_id)
52 | WHERE customer_id =21;
53 | ```
54 |
55 | #### Result set:
56 | 
57 |
58 | - Customer started the free trial on 4 Feb 202 and subscribed to the basic monthly during the seven day the trial period to continue the subscription
59 | - They then upgraded to the pro monthly plan after 4 months
60 | - Customer cancelled their subscription and churned on 27 September 2020
61 | ***
62 |
63 | ### Customer 73
64 |
65 | ```sql
66 | SELECT customer_id,
67 | plan_id,
68 | plan_name,
69 | start_date
70 | FROM subscriptions
71 | JOIN plans USING (plan_id)
72 | WHERE customer_id =73;
73 | ```
74 |
75 | #### Result set:
76 | 
77 |
78 | - Customer started the free trial on 24 March 2020 and subscribed to the basic monthly after the seven day the trial period to continue the subscription
79 | - They then upgraded to the pro monthly plan after 2 months
80 | - They then upgraded to the pro annual plan in October 2020
81 | ***
82 |
83 | ### Customer 87
84 |
85 | ```sql
86 | SELECT customer_id,
87 | plan_id,
88 | plan_name,
89 | start_date
90 | FROM subscriptions
91 | JOIN plans USING (plan_id)
92 | WHERE customer_id =87;
93 | ```
94 |
95 | #### Result set:
96 | 
97 |
98 | - Customer started the free trial on 8 August 2020
99 | - They may have chosen to continue with the pro monthly after the seven day the trial period
100 | - They then upgraded to the pro annual plan in September 2020
101 | ***
102 |
103 | ### Customer 99
104 |
105 | ```sql
106 | SELECT customer_id,
107 | plan_id,
108 | plan_name,
109 | start_date
110 | FROM subscriptions
111 | JOIN plans USING (plan_id)
112 | WHERE customer_id =99;
113 | ```
114 |
115 | #### Result set:
116 | 
117 |
118 | - Customer started the free trial on 5 December 2020
119 | - They chose not to continue with paid subscription and decided to cancel on the last day of the trial period.
120 | ***
121 |
122 | ### Customer 290
123 |
124 | ```sql
125 | SELECT customer_id,
126 | plan_id,
127 | plan_name,
128 | start_date
129 | FROM subscriptions
130 | JOIN plans USING (plan_id)
131 | WHERE customer_id =290;
132 | ```
133 |
134 | #### Result set:
135 | 
136 |
137 | - Customer started the free trial on 10 January 2020
138 | - They subscribed to the basic monthly plan during the seven day the trial period to continue the subscription
139 | ***
140 |
141 | Click [here](https://github.com/manaswikamila05/8-Week-SQL-Challenge/blob/main/Case%20Study%20%23%203%20-%20Foodie-Fi/B.%20Data%20Analysis%20Questions.md) to view the solution solution of B. Data Analysis Questions!
142 |
143 |
144 |
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/Case Study # 3 - Foodie-Fi/B. Data Analysis Questions.md:
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1 | # :avocado: Case Study #3: Foodie-Fi - Data Analysis Questions
2 |
3 | ## Case Study Questions
4 | 1. How many customers has Foodie-Fi ever had?
5 | 2. What is the monthly distribution of trial plan start_date values for our dataset - use the start of the month as the group by value
6 | 3. What plan start_date values occur after the year 2020 for our dataset? Show the breakdown by count of events for each plan_name
7 | 4. What is the customer count and percentage of customers who have churned rounded to 1 decimal place?
8 | 5. How many customers have churned straight after their initial free trial - what percentage is this rounded to the nearest whole number?
9 | 6. What is the number and percentage of customer plans after their initial free trial?
10 | 7. What is the customer count and percentage breakdown of all 5 plan_name values at 2020-12-31?
11 | 8. How many customers have upgraded to an annual plan in 2020?
12 | 9. How many days on average does it take for a customer to an annual plan from the day they join Foodie-Fi?
13 | 10. Can you further breakdown this average value into 30 day periods (i.e. 0-30 days, 31-60 days etc)
14 | 11. How many customers downgraded from a pro monthly to a basic monthly plan in 2020?
15 |
16 | ***
17 |
18 | ### 1. How many customers has Foodie-Fi ever had?
19 |
20 | ```sql
21 | SELECT count(DISTINCT customer_id) AS 'distinct customers'
22 | FROM subscriptions;
23 | ```
24 |
25 | #### Result set:
26 | 
27 |
28 | ***
29 |
30 | ### 2. What is the monthly distribution of trial plan start_date values for our dataset - use the start of the month as the group by value
31 |
32 | ```sql
33 | SELECT month(start_date),
34 | count(DISTINCT customer_id) as 'monthly distribution'
35 | FROM subscriptions
36 | JOIN plans USING (plan_id)
37 | WHERE plan_id=0
38 | GROUP BY month(start_date);
39 | ```
40 |
41 | #### Result set:
42 | 
43 |
44 | ***
45 |
46 | ### 3. What plan start_date values occur after the year 2020 for our dataset? Show the breakdown by count of events for each plan_name
47 |
48 | ```sql
49 | SELECT plan_id,
50 | plan_name,
51 | count(*) AS 'count of events'
52 | FROM subscriptions
53 | JOIN plans USING (plan_id)
54 | WHERE year(start_date) > 2020
55 | GROUP BY plan_id;
56 | ```
57 |
58 | #### Result set:
59 | 
60 |
61 | ***
62 |
63 | ### 4. What is the customer count and percentage of customers who have churned rounded to 1 decimal place?
64 |
65 | ```sql
66 | SELECT plan_name, count(DISTINCT customer_id) as 'churned customers',
67 | round(100 *count(DISTINCT customer_id) / (
68 | SELECT count(DISTINCT customer_id) AS 'distinct customers'
69 | FROM subscriptions
70 | ),2) as 'churn percentage'
71 | FROM subscriptions
72 | JOIN plans USING (plan_id)
73 | where plan_id=4;
74 | ```
75 |
76 | #### Result set:
77 | 
78 |
79 | ```sql
80 | WITH counts_cte AS
81 | (SELECT plan_name,
82 | count(DISTINCT customer_id) AS distinct_customer_count,
83 | SUM(CASE
84 | WHEN plan_id=4 THEN 1
85 | ELSE 0
86 | END) AS churned_customer_count
87 | FROM subscriptions
88 | JOIN plans USING (plan_id))
89 | SELECT *,
90 | round(100*(churned_customer_count/distinct_customer_count), 2) AS churn_percentage
91 | FROM counts_cte;
92 | ```
93 |
94 | #### Result set:
95 | 
96 |
97 | ***
98 |
99 | ### 5. How many customers have churned straight after their initial free trial - what percentage is this rounded to the nearest whole number?
100 |
101 | ```sql
102 | WITH next_plan_cte AS
103 | (SELECT *,
104 | lead(plan_id, 1) over(PARTITION BY customer_id
105 | ORDER BY start_date) AS next_plan
106 | FROM subscriptions),
107 | churners AS
108 | (SELECT *
109 | FROM next_plan_cte
110 | WHERE next_plan=4
111 | AND plan_id=0)
112 | SELECT count(customer_id) AS 'churn after trial count',
113 | round(100 *count(customer_id)/
114 | (SELECT count(DISTINCT customer_id) AS 'distinct customers'
115 | FROM subscriptions), 2) AS 'churn percentage'
116 | FROM churners;
117 | ```
118 |
119 | #### Result set:
120 | 
121 |
122 | ***
123 |
124 | ### 6. What is the number and percentage of customer plans after their initial free trial?
125 |
126 | ```sql
127 | SELECT plan_name,
128 | count(customer_id) customer_count,
129 | round(100 *count(DISTINCT customer_id) /
130 | (SELECT count(DISTINCT customer_id) AS 'distinct customers'
131 | FROM subscriptions), 2) AS 'customer percentage'
132 | FROM subscriptions
133 | JOIN plans USING (plan_id)
134 | WHERE plan_name != 'trial'
135 | GROUP BY plan_name
136 | ORDER BY plan_id;
137 | ```
138 |
139 | #### Result set:
140 | 
141 |
142 | ```sql
143 | WITH previous_plan_cte AS
144 | (SELECT *,
145 | lag(plan_id, 1) over(PARTITION BY customer_id
146 | ORDER BY start_date) AS previous_plan
147 | FROM subscriptions
148 | JOIN plans USING (plan_id))
149 | SELECT plan_name,
150 | count(customer_id) customer_count,
151 | round(100 *count(DISTINCT customer_id) /
152 | (SELECT count(DISTINCT customer_id) AS 'distinct customers'
153 | FROM subscriptions), 2) AS 'customer percentage'
154 | FROM previous_plan_cte
155 | WHERE previous_plan=0
156 | GROUP BY plan_name ;
157 | ```
158 | #### Result set:
159 | 
160 |
161 | ***
162 |
163 | ### 7. What is the customer count and percentage breakdown of all 5 plan_name values at 2020-12-31?
164 |
165 | ```sql
166 | WITH latest_plan_cte AS
167 | (SELECT *,
168 | row_number() over(PARTITION BY customer_id
169 | ORDER BY start_date DESC) AS latest_plan
170 | FROM subscriptions
171 | JOIN plans USING (plan_id)
172 | WHERE start_date <='2020-12-31' )
173 | SELECT plan_id,
174 | plan_name,
175 | count(customer_id) AS customer_count,
176 | round(100*count(customer_id) /
177 | (SELECT COUNT(DISTINCT customer_id)
178 | FROM subscriptions), 2) AS percentage_breakdown
179 | FROM latest_plan_cte
180 | WHERE latest_plan = 1
181 | GROUP BY plan_id
182 | ORDER BY plan_id;
183 | ```
184 |
185 | #### Result set:
186 | 
187 |
188 | ***
189 |
190 | ### 8. How many customers have upgraded to an annual plan in 2020?
191 |
192 | ```sql
193 | SELECT plan_id,
194 | COUNT(DISTINCT customer_id) AS annual_plan_customer_count
195 | FROM foodie_fi.subscriptions
196 | WHERE plan_id = 3
197 | AND year(start_date) = 2020;
198 | ```
199 |
200 | #### Result set:
201 | 
202 |
203 | ```sql
204 | WITH previous_plan_cte AS
205 | (SELECT *,
206 | lag(plan_id, 1) over(PARTITION BY customer_id
207 | ORDER BY start_date) AS previous_plan_id
208 | FROM subscriptions
209 | JOIN plans USING (plan_id))
210 | SELECT count(customer_id) upgraded_plan_customer_count
211 | FROM previous_plan_cte
212 | WHERE previous_plan_id<3
213 | AND plan_id=3
214 | AND year(start_date) = 2020;
215 | ```
216 | 
217 |
218 |
219 | ***
220 |
221 | ### 9. How many days on average does it take for a customer to an annual plan from the day they join Foodie-Fi?
222 |
223 | ```sql
224 | WITH trial_plan_customer_cte AS
225 | (SELECT *
226 | FROM subscriptions
227 | JOIN plans USING (plan_id)
228 | WHERE plan_id=0),
229 | annual_plan_customer_cte AS
230 | (SELECT *
231 | FROM subscriptions
232 | JOIN plans USING (plan_id)
233 | WHERE plan_id=3)
234 | SELECT round(avg(datediff(annual_plan_customer_cte.start_date, trial_plan_customer_cte.start_date)), 2)AS avg_conversion_days
235 | FROM trial_plan_customer_cte
236 | INNER JOIN annual_plan_customer_cte USING (customer_id);
237 | ```
238 |
239 | #### Result set:
240 | 
241 |
242 | ```sql
243 | WITH trial_plan_cte AS
244 | (SELECT *,
245 | first_value(start_date) over(PARTITION BY customer_id
246 | ORDER BY start_date) AS trial_plan_start_date
247 | FROM subscriptions)
248 | SELECT round(avg(datediff(start_date, trial_plan_start_date)), 2)AS avg_conversion_days
249 | FROM trial_plan_cte
250 | WHERE plan_id =3;
251 | ```
252 | ***
253 |
254 | ### 10. Can you further breakdown this average value into 30 day periods (i.e. 0-30 days, 31-60 days etc)
255 | - The days between trial start date and the annual plan start date is computed.
256 | - The days are bucketed in 30 day period by dividing the number of days obtained by 30.
257 |
258 | ```sql
259 | WITH next_plan_cte AS
260 | (SELECT *,
261 | lead(start_date, 1) over(PARTITION BY customer_id
262 | ORDER BY start_date) AS next_plan_start_date,
263 | lead(plan_id, 1) over(PARTITION BY customer_id
264 | ORDER BY start_date) AS next_plan
265 | FROM subscriptions),
266 | window_details_cte AS
267 | (SELECT *,
268 | datediff(next_plan_start_date, start_date) AS days,
269 | round(datediff(next_plan_start_date, start_date)/30) AS window_30_days
270 | FROM next_plan_cte
271 | WHERE next_plan=3)
272 | SELECT window_30_days,
273 | count(*) AS customer_count
274 | FROM window_details_cte
275 | GROUP BY window_30_days
276 | ORDER BY window_30_days;
277 | ```
278 |
279 | #### Result set:
280 | 
281 |
282 | ***
283 |
284 | ### 11. How many customers downgraded from a pro monthly to a basic monthly plan in 2020?
285 |
286 | ```sql
287 | WITH next_plan_cte AS
288 | (SELECT *,
289 | lead(plan_id, 1) over(PARTITION BY customer_id
290 | ORDER BY start_date) AS next_plan
291 | FROM subscriptions)
292 | SELECT count(*) AS downgrade_count
293 | FROM next_plan_cte
294 | WHERE plan_id=2
295 | AND next_plan=1
296 | AND year(start_date);
297 | ```
298 |
299 | #### Result set:
300 | 
301 |
302 | ***
303 |
304 | Click [here](https://github.com/manaswikamila05/8-Week-SQL-Challenge) to move back to the 8-Week-SQL-Challenge repository!
305 |
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/Case Study # 3 - Foodie-Fi/C. Challenge Payment Question.md:
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1 | # :avocado: Case Study #3: Foodie-Fi - Challenge Payment Questions - WIP
2 |
3 | ## Case Study Questions
4 | The Foodie-Fi team wants you to create a new payments table for the year 2020 that includes amounts paid by each customer in the subscriptions table with the following requirements:
5 |
6 | - monthly payments always occur on the same day of month as the original start_date of any monthly paid plan
7 | - upgrades from basic to monthly or pro plans are reduced by the current paid amount in that month and start immediately
8 | - upgrades from pro monthly to pro annual are paid at the end of the current billing period and also starts at the end of the month period
9 | - once a customer churns they will no longer make payments
10 |
11 | ***
12 |
13 | ###
14 |
15 | ```sql
16 |
17 | ```
18 |
19 | #### Result set:
20 |
21 | ***
22 |
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/Case Study # 3 - Foodie-Fi/ERD.jpg:
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https://raw.githubusercontent.com/manaswikamila05/8-Week-SQL-Challenge/f655c7ea30b64979e1980f7030a3740cab034b82/Case Study # 3 - Foodie-Fi/ERD.jpg
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/Case Study # 3 - Foodie-Fi/Foodie-Fi Schema.sql:
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1 | DROP SCHEMA foodie_fi;
2 |
3 | CREATE SCHEMA foodie_fi;
4 | USE foodie_fi;
5 |
6 | CREATE TABLE plans (
7 | plan_id INTEGER,
8 | plan_name VARCHAR(13),
9 | price DECIMAL(5,2)
10 | );
11 |
12 | INSERT INTO plans
13 | (plan_id, plan_name, price)
14 | VALUES
15 | ('0', 'trial', '0'),
16 | ('1', 'basic monthly', '9.90'),
17 | ('2', 'pro monthly', '19.90'),
18 | ('3', 'pro annual', '199'),
19 | ('4', 'churn', null);
20 |
21 |
22 |
23 | CREATE TABLE subscriptions (
24 | customer_id INTEGER,
25 | plan_id INTEGER,
26 | start_date DATE
27 | );
28 |
29 | INSERT INTO subscriptions
30 | (customer_id, plan_id, start_date)
31 | VALUES
32 | ('1', '0', '2020-08-01'),
33 | ('1', '1', '2020-08-08'),
34 | ('2', '0', '2020-09-20'),
35 | ('2', '3', '2020-09-27'),
36 | ('3', '0', '2020-01-13'),
37 | ('3', '1', '2020-01-20'),
38 | ('4', '0', '2020-01-17'),
39 | ('4', '1', '2020-01-24'),
40 | ('4', '4', '2020-04-21'),
41 | ('5', '0', '2020-08-03'),
42 | ('5', '1', '2020-08-10'),
43 | ('6', '0', '2020-12-23'),
44 | ('6', '1', '2020-12-30'),
45 | ('6', '4', '2021-02-26'),
46 | ('7', '0', '2020-02-05'),
47 | ('7', '1', '2020-02-12'),
48 | ('7', '2', '2020-05-22'),
49 | ('8', '0', '2020-06-11'),
50 | ('8', '1', '2020-06-18'),
51 | ('8', '2', '2020-08-03'),
52 | ('9', '0', '2020-12-07'),
53 | ('9', '3', '2020-12-14'),
54 | ('10', '0', '2020-09-19'),
55 | ('10', '2', '2020-09-26'),
56 | ('11', '0', '2020-11-19'),
57 | ('11', '4', '2020-11-26'),
58 | ('12', '0', '2020-09-22'),
59 | ('12', '1', '2020-09-29'),
60 | ('13', '0', '2020-12-15'),
61 | ('13', '1', '2020-12-22'),
62 | ('13', '2', '2021-03-29'),
63 | ('14', '0', '2020-09-22'),
64 | ('14', '1', '2020-09-29'),
65 | ('15', '0', '2020-03-17'),
66 | ('15', '2', '2020-03-24'),
67 | ('15', '4', '2020-04-29'),
68 | ('16', '0', '2020-05-31'),
69 | ('16', '1', '2020-06-07'),
70 | ('16', '3', '2020-10-21'),
71 | ('17', '0', '2020-07-27'),
72 | ('17', '1', '2020-08-03'),
73 | ('17', '3', '2020-12-11'),
74 | ('18', '0', '2020-07-06'),
75 | ('18', '2', '2020-07-13'),
76 | ('19', '0', '2020-06-22'),
77 | ('19', '2', '2020-06-29'),
78 | ('19', '3', '2020-08-29'),
79 | ('20', '0', '2020-04-08'),
80 | ('20', '1', '2020-04-15'),
81 | ('20', '3', '2020-06-05'),
82 | ('21', '0', '2020-02-04'),
83 | ('21', '1', '2020-02-11'),
84 | ('21', '2', '2020-06-03'),
85 | ('21', '4', '2020-09-27'),
86 | ('22', '0', '2020-01-10'),
87 | ('22', '2', '2020-01-17'),
88 | ('23', '0', '2020-05-13'),
89 | ('23', '3', '2020-05-20'),
90 | ('24', '0', '2020-11-10'),
91 | ('24', '2', '2020-11-17'),
92 | ('24', '3', '2021-04-17'),
93 | ('25', '0', '2020-05-10'),
94 | ('25', '1', '2020-05-17'),
95 | ('25', '2', '2020-06-16'),
96 | ('26', '0', '2020-12-08'),
97 | ('26', '2', '2020-12-15'),
98 | ('27', '0', '2020-08-24'),
99 | ('27', '2', '2020-08-31'),
100 | ('28', '0', '2020-06-30'),
101 | ('28', '3', '2020-07-07'),
102 | ('29', '0', '2020-01-23'),
103 | ('29', '2', '2020-01-30'),
104 | ('30', '0', '2020-04-29'),
105 | ('30', '1', '2020-05-06'),
106 | ('31', '0', '2020-06-22'),
107 | ('31', '2', '2020-06-29'),
108 | ('31', '3', '2020-11-29'),
109 | ('32', '0', '2020-06-12'),
110 | ('32', '1', '2020-06-19'),
111 | ('32', '2', '2020-07-18'),
112 | ('33', '0', '2020-09-03'),
113 | ('33', '2', '2020-09-10'),
114 | ('33', '4', '2021-02-05'),
115 | ('34', '0', '2020-12-20'),
116 | ('34', '1', '2020-12-27'),
117 | ('34', '2', '2021-03-26'),
118 | ('35', '0', '2020-09-03'),
119 | ('35', '2', '2020-09-10'),
120 | ('36', '0', '2020-02-25'),
121 | ('36', '2', '2020-03-03'),
122 | ('37', '0', '2020-08-05'),
123 | ('37', '1', '2020-08-12'),
124 | ('37', '2', '2020-11-11'),
125 | ('38', '0', '2020-10-02'),
126 | ('38', '2', '2020-10-09'),
127 | ('38', '3', '2020-11-09'),
128 | ('39', '0', '2020-05-28'),
129 | ('39', '1', '2020-06-04'),
130 | ('39', '2', '2020-08-25'),
131 | ('39', '4', '2020-09-10'),
132 | ('40', '0', '2020-01-22'),
133 | ('40', '1', '2020-01-29'),
134 | ('40', '2', '2020-03-25'),
135 | ('41', '0', '2020-05-16'),
136 | ('41', '2', '2020-05-23'),
137 | ('42', '0', '2020-10-27'),
138 | ('42', '1', '2020-11-03'),
139 | ('42', '2', '2021-04-28'),
140 | ('43', '0', '2020-08-13'),
141 | ('43', '1', '2020-08-20'),
142 | ('43', '2', '2020-12-18'),
143 | ('44', '0', '2020-03-17'),
144 | ('44', '3', '2020-03-24'),
145 | ('45', '0', '2020-02-11'),
146 | ('45', '1', '2020-02-18'),
147 | ('45', '2', '2020-08-12'),
148 | ('46', '0', '2020-04-19'),
149 | ('46', '1', '2020-04-26'),
150 | ('46', '2', '2020-07-06'),
151 | ('46', '3', '2020-08-06'),
152 | ('47', '0', '2020-06-06'),
153 | ('47', '1', '2020-06-13'),
154 | ('47', '3', '2020-10-26'),
155 | ('48', '0', '2020-01-11'),
156 | ('48', '1', '2020-01-18'),
157 | ('48', '4', '2020-06-01'),
158 | ('49', '0', '2020-04-24'),
159 | ('49', '2', '2020-05-01'),
160 | ('49', '3', '2020-08-01'),
161 | ('50', '0', '2020-07-21'),
162 | ('50', '2', '2020-07-28'),
163 | ('51', '0', '2020-01-19'),
164 | ('51', '1', '2020-01-26'),
165 | ('51', '3', '2020-03-09'),
166 | ('51', '4', '2021-03-09'),
167 | ('52', '0', '2020-05-31'),
168 | ('52', '1', '2020-06-07'),
169 | ('52', '4', '2020-07-05'),
170 | ('53', '0', '2020-01-18'),
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2457 | ('916', '3', '2020-02-26'),
2458 | ('917', '0', '2020-07-07'),
2459 | ('917', '1', '2020-07-14'),
2460 | ('917', '3', '2020-10-10'),
2461 | ('918', '0', '2020-06-03'),
2462 | ('918', '1', '2020-06-10'),
2463 | ('918', '2', '2020-09-01'),
2464 | ('918', '3', '2020-12-01'),
2465 | ('919', '0', '2020-09-12'),
2466 | ('919', '4', '2020-09-19'),
2467 | ('920', '0', '2020-08-19'),
2468 | ('920', '2', '2020-08-26'),
2469 | ('921', '0', '2020-07-26'),
2470 | ('921', '1', '2020-08-02'),
2471 | ('921', '2', '2020-11-20'),
2472 | ('922', '0', '2020-11-02'),
2473 | ('922', '2', '2020-11-09'),
2474 | ('922', '3', '2021-02-09'),
2475 | ('923', '0', '2020-08-28'),
2476 | ('923', '2', '2020-09-04'),
2477 | ('923', '4', '2020-11-04'),
2478 | ('924', '0', '2020-06-19'),
2479 | ('924', '1', '2020-06-26'),
2480 | ('924', '2', '2020-12-02'),
2481 | ('925', '0', '2020-09-21'),
2482 | ('925', '1', '2020-09-28'),
2483 | ('925', '2', '2021-02-04'),
2484 | ('926', '0', '2020-07-12'),
2485 | ('926', '1', '2020-07-19'),
2486 | ('926', '2', '2020-10-13'),
2487 | ('926', '3', '2021-02-13'),
2488 | ('927', '0', '2020-01-13'),
2489 | ('927', '2', '2020-01-20'),
2490 | ('928', '0', '2020-07-08'),
2491 | ('928', '4', '2020-07-15'),
2492 | ('929', '0', '2020-04-02'),
2493 | ('929', '1', '2020-04-09'),
2494 | ('929', '3', '2020-08-13'),
2495 | ('930', '0', '2020-02-14'),
2496 | ('930', '1', '2020-02-21'),
2497 | ('930', '2', '2020-03-24'),
2498 | ('931', '0', '2020-01-27'),
2499 | ('931', '1', '2020-02-03'),
2500 | ('931', '2', '2020-02-12'),
2501 | ('931', '3', '2020-04-12'),
2502 | ('932', '0', '2020-11-01'),
2503 | ('932', '3', '2020-11-08'),
2504 | ('933', '0', '2020-05-08'),
2505 | ('933', '1', '2020-05-15'),
2506 | ('933', '2', '2020-06-24'),
2507 | ('934', '0', '2020-01-07'),
2508 | ('934', '2', '2020-01-14'),
2509 | ('934', '4', '2020-04-25'),
2510 | ('935', '0', '2020-01-25'),
2511 | ('935', '4', '2020-02-01'),
2512 | ('936', '0', '2020-09-18'),
2513 | ('936', '2', '2020-09-25'),
2514 | ('937', '0', '2020-02-22'),
2515 | ('937', '2', '2020-02-29'),
2516 | ('937', '3', '2020-08-29'),
2517 | ('938', '0', '2020-08-01'),
2518 | ('938', '1', '2020-08-08'),
2519 | ('938', '3', '2020-11-08'),
2520 | ('939', '0', '2020-03-20'),
2521 | ('939', '1', '2020-03-27'),
2522 | ('939', '2', '2020-08-16'),
2523 | ('940', '0', '2020-01-17'),
2524 | ('940', '2', '2020-01-24'),
2525 | ('940', '4', '2020-03-22'),
2526 | ('941', '0', '2020-09-15'),
2527 | ('941', '2', '2020-09-22'),
2528 | ('942', '0', '2020-12-06'),
2529 | ('942', '1', '2020-12-13'),
2530 | ('942', '4', '2021-01-18'),
2531 | ('943', '0', '2020-11-13'),
2532 | ('943', '1', '2020-11-20'),
2533 | ('943', '3', '2021-01-14'),
2534 | ('944', '0', '2020-09-24'),
2535 | ('944', '1', '2020-10-01'),
2536 | ('944', '2', '2021-01-14'),
2537 | ('945', '0', '2020-01-07'),
2538 | ('945', '1', '2020-01-14'),
2539 | ('945', '3', '2020-03-25'),
2540 | ('946', '0', '2020-07-10'),
2541 | ('946', '2', '2020-07-17'),
2542 | ('946', '3', '2020-12-17'),
2543 | ('947', '0', '2020-07-13'),
2544 | ('947', '1', '2020-07-20'),
2545 | ('947', '3', '2020-09-19'),
2546 | ('948', '0', '2020-03-16'),
2547 | ('948', '1', '2020-03-23'),
2548 | ('948', '4', '2020-08-18'),
2549 | ('949', '0', '2020-10-07'),
2550 | ('949', '2', '2020-10-14'),
2551 | ('950', '0', '2020-09-13'),
2552 | ('950', '2', '2020-09-20'),
2553 | ('950', '3', '2021-01-20'),
2554 | ('951', '0', '2020-07-30'),
2555 | ('951', '2', '2020-08-06'),
2556 | ('952', '0', '2020-08-22'),
2557 | ('952', '2', '2020-08-29'),
2558 | ('953', '0', '2020-09-08'),
2559 | ('953', '1', '2020-09-15'),
2560 | ('954', '0', '2020-11-23'),
2561 | ('954', '4', '2020-11-30'),
2562 | ('955', '0', '2020-07-28'),
2563 | ('955', '2', '2020-08-04'),
2564 | ('956', '0', '2020-02-20'),
2565 | ('956', '1', '2020-02-27'),
2566 | ('956', '2', '2020-07-12'),
2567 | ('956', '3', '2021-01-12'),
2568 | ('957', '0', '2020-04-28'),
2569 | ('957', '4', '2020-05-05'),
2570 | ('958', '0', '2020-07-06'),
2571 | ('958', '1', '2020-07-13'),
2572 | ('958', '2', '2020-11-22'),
2573 | ('958', '3', '2021-01-22'),
2574 | ('959', '0', '2020-04-22'),
2575 | ('959', '1', '2020-04-29'),
2576 | ('960', '0', '2020-10-22'),
2577 | ('960', '2', '2020-10-29'),
2578 | ('960', '3', '2021-02-28'),
2579 | ('961', '0', '2020-09-12'),
2580 | ('961', '1', '2020-09-19'),
2581 | ('961', '3', '2020-11-21'),
2582 | ('962', '0', '2020-06-03'),
2583 | ('962', '1', '2020-06-10'),
2584 | ('962', '2', '2020-09-23'),
2585 | ('962', '4', '2020-11-20'),
2586 | ('963', '0', '2020-01-04'),
2587 | ('963', '2', '2020-01-11'),
2588 | ('964', '0', '2020-10-09'),
2589 | ('964', '1', '2020-10-16'),
2590 | ('964', '4', '2021-02-27'),
2591 | ('965', '0', '2020-06-19'),
2592 | ('965', '1', '2020-06-26'),
2593 | ('966', '0', '2020-02-09'),
2594 | ('966', '1', '2020-02-16'),
2595 | ('967', '0', '2020-08-21'),
2596 | ('967', '1', '2020-08-28'),
2597 | ('967', '2', '2021-01-15'),
2598 | ('967', '3', '2021-04-15'),
2599 | ('968', '0', '2020-11-22'),
2600 | ('968', '2', '2020-11-29'),
2601 | ('969', '0', '2020-02-21'),
2602 | ('969', '2', '2020-02-28'),
2603 | ('969', '3', '2020-06-28'),
2604 | ('970', '0', '2020-10-05'),
2605 | ('970', '1', '2020-10-12'),
2606 | ('971', '0', '2020-01-02'),
2607 | ('971', '2', '2020-01-09'),
2608 | ('972', '0', '2020-02-05'),
2609 | ('972', '3', '2020-02-12'),
2610 | ('973', '0', '2020-01-08'),
2611 | ('973', '4', '2020-01-15'),
2612 | ('974', '0', '2020-09-10'),
2613 | ('974', '1', '2020-09-17'),
2614 | ('974', '3', '2020-10-16'),
2615 | ('975', '0', '2020-11-15'),
2616 | ('975', '1', '2020-11-22'),
2617 | ('975', '3', '2021-04-30'),
2618 | ('976', '0', '2020-11-11'),
2619 | ('976', '1', '2020-11-18'),
2620 | ('976', '3', '2021-02-13'),
2621 | ('977', '0', '2020-08-04'),
2622 | ('977', '1', '2020-08-11'),
2623 | ('977', '4', '2020-11-03'),
2624 | ('978', '0', '2020-08-27'),
2625 | ('978', '2', '2020-09-03'),
2626 | ('978', '3', '2020-11-03'),
2627 | ('979', '0', '2020-12-28'),
2628 | ('979', '1', '2021-01-04'),
2629 | ('980', '0', '2020-06-12'),
2630 | ('980', '2', '2020-06-19'),
2631 | ('981', '0', '2020-02-16'),
2632 | ('981', '2', '2020-02-23'),
2633 | ('982', '0', '2020-06-13'),
2634 | ('982', '1', '2020-06-20'),
2635 | ('982', '2', '2020-10-14'),
2636 | ('983', '0', '2020-12-12'),
2637 | ('983', '1', '2020-12-19'),
2638 | ('984', '0', '2020-06-30'),
2639 | ('984', '1', '2020-07-07'),
2640 | ('984', '4', '2020-09-30'),
2641 | ('985', '0', '2020-08-02'),
2642 | ('985', '4', '2020-08-09'),
2643 | ('986', '0', '2020-11-16'),
2644 | ('986', '1', '2020-11-23'),
2645 | ('986', '4', '2021-04-13'),
2646 | ('987', '0', '2020-01-05'),
2647 | ('987', '4', '2020-01-12'),
2648 | ('988', '0', '2020-05-02'),
2649 | ('988', '4', '2020-05-09'),
2650 | ('989', '0', '2020-09-03'),
2651 | ('989', '2', '2020-09-10'),
2652 | ('989', '3', '2021-01-10'),
2653 | ('990', '0', '2020-07-23'),
2654 | ('990', '1', '2020-07-30'),
2655 | ('991', '0', '2020-10-18'),
2656 | ('991', '2', '2020-10-25'),
2657 | ('992', '0', '2020-10-17'),
2658 | ('992', '2', '2020-10-24'),
2659 | ('993', '0', '2020-10-31'),
2660 | ('993', '4', '2020-11-07'),
2661 | ('994', '0', '2020-07-25'),
2662 | ('994', '1', '2020-08-01'),
2663 | ('994', '2', '2020-08-27'),
2664 | ('995', '0', '2020-06-11'),
2665 | ('995', '1', '2020-06-18'),
2666 | ('995', '2', '2020-12-06'),
2667 | ('996', '0', '2020-11-11'),
2668 | ('996', '1', '2020-11-18'),
2669 | ('996', '4', '2020-12-07'),
2670 | ('997', '0', '2020-07-27'),
2671 | ('997', '1', '2020-08-03'),
2672 | ('997', '2', '2020-08-26'),
2673 | ('997', '4', '2020-11-14'),
2674 | ('998', '0', '2020-10-12'),
2675 | ('998', '2', '2020-10-19'),
2676 | ('999', '0', '2020-10-23'),
2677 | ('999', '2', '2020-10-30'),
2678 | ('999', '4', '2020-12-01'),
2679 | ('1000', '0', '2020-03-19'),
2680 | ('1000', '2', '2020-03-26'),
2681 | ('1000', '4', '2020-06-04');
--------------------------------------------------------------------------------
/Case Study # 3 - Foodie-Fi/README.md:
--------------------------------------------------------------------------------
1 | # :avocado: Case Study #3: Foodie-Fi
2 |
3 | 
4 |
5 | View the case study [here](https://8weeksqlchallenge.com/case-study-3/)
6 |
7 | ## Table Of Contents
8 | - [Introduction](#introduction)
9 | - [Dataset](#datasets)
10 | - [Entity Relationship Diagram](#entity-relationship-diagram)
11 | - [Case Study Solutions](#case-study-solutions)
12 |
13 |
14 | ## Introduction
15 | Subscription based businesses are super popular and Danny realised that there was a large gap in the market - he wanted to create a new streaming service that only had food related content - something like Netflix but with only cooking shows!
16 |
17 | Danny finds a few smart friends to launch his new startup Foodie-Fi in 2020 and started selling monthly and annual subscriptions, giving their customers unlimited on-demand access to exclusive food videos from around the world!
18 |
19 | Danny created Foodie-Fi with a data driven mindset and wanted to ensure all future investment decisions and new features were decided using data. This case study focuses on using subscription style digital data to answer important business questions.
20 |
21 | ## Datasets
22 |
23 | **plans table** : Customers can choose which plans to join Foodie-Fi when they first sign up.
24 |
25 | There are 5 customer plans.
26 | - Basic plan - customers have limited access and can only stream their videos and is only available monthly at $9.90
27 | - Pro plan - customers have no watch time limits and are able to download videos for offline viewing. Pro plans start at $19.90 a month or $199 for an annual subscription.
28 | - Trial plan - Customers can sign up to an initial 7 day free trial will automatically continue with the pro monthly subscription plan unless they cancel, downgrade to basic or upgrade to an annual pro plan at any point during the trial.
29 | - Churn plan - When customers cancel their Foodie-Fi service - they will have a churn plan record with a null price but their plan will continue until the end of the billing period.
30 |
31 | **subscriptions table**
32 | - Customer subscriptions show the *exact date where their specific plan_id starts*.
33 | - If customers *downgrade* from a pro plan or *cancel their subscription* - the higher plan will remain in place until the period is over - the start_date in the subscriptions table will reflect the date that the actual plan changes.
34 | - When customers *upgrade* their account from a basic plan to a pro or annual pro plan - the higher plan will take effect straightaway.
35 | - When customers *churn* - they will keep their access until the end of their current billing period but the start_date will be technically the day they decided to cancel their service.
36 |
37 | ## Entity Relationship Diagram
38 | 
39 |
40 | ## Case Study Solutions
41 | - [A. Customer Journey](https://github.com/manaswikamila05/8-Week-SQL-Challenge/blob/main/Case%20Study%20%23%203%20-%20Foodie-Fi/A.%20Customer%20Journey.md)
42 | - [B. Data Analysis Questions](https://github.com/manaswikamila05/8-Week-SQL-Challenge/blob/main/Case%20Study%20%23%203%20-%20Foodie-Fi/B.%20Data%20Analysis%20Questions.md)
43 |
44 |
--------------------------------------------------------------------------------
/Case Study # 4 - Data Bank/A. Customer Nodes Exploration.md:
--------------------------------------------------------------------------------
1 | ## :technologist::woman_technologist: Case Study #4: Data Bank - Customer Nodes Exploration
2 |
3 | ## Case Study Questions
4 |
5 | 1. How many unique nodes are there on the Data Bank system?
6 | 2. What is the number of nodes per region?
7 | 3. How many customers are allocated to each region?
8 | 4. How many days on average are customers reallocated to a different node?
9 | 5. What is the median, 80th and 95th percentile for this same reallocation days metric for each region?
10 |
11 | ***
12 |
13 | ### 1. How many unique nodes are there on the Data Bank system?
14 |
15 | ```sql
16 | SELECT count(DISTINCT node_id) AS unique_nodes
17 | FROM customer_nodes;
18 | ```
19 |
20 | #### Result set:
21 | 
22 |
23 | ***
24 |
25 | ### 2. What is the number of nodes per region?
26 |
27 | ```sql
28 | SELECT region_id,
29 | region_name,
30 | count(node_id) AS node_count
31 | FROM customer_nodes
32 | INNER JOIN regions USING(region_id)
33 | GROUP BY region_id;
34 | ```
35 |
36 | #### Result set:
37 | 
38 |
39 | ***
40 |
41 | ### 3. How many customers are allocated to each region?
42 |
43 | ```sql
44 | SELECT region_id,
45 | region_name,
46 | count(DISTINCT customer_id) AS customer_count
47 | FROM customer_nodes
48 | INNER JOIN regions USING(region_id)
49 | GROUP BY region_id;
50 | ```
51 |
52 | #### Result set:
53 | 
54 |
55 | ***
56 |
57 | ### 4. How many days on average are customers reallocated to a different node?
58 |
59 | ```sql
60 | SELECT round(avg(datediff(end_date, start_date)), 2) AS avg_days
61 | FROM customer_nodes
62 | WHERE end_date!='9999-12-31';
63 | ```
64 |
65 | #### Result set:
66 | 
67 |
68 | ***
69 |
70 | ### 5. What is the median, 80th and 95th percentile for this same reallocation days metric for each region?
71 | - reallocation days metric: days taken to reallocate to a different node
72 | - Percentile found by partitioning the dataset by regions and arranging it in ascending order of reallocation_days
73 | - 95th percentile -> 95% of the values are less than or equal to the current value.
74 |
75 |
76 | **95th percentile**
77 | ```sql
78 | WITH reallocation_days_cte AS
79 | (SELECT *,
80 | (datediff(end_date, start_date)) AS reallocation_days
81 | FROM customer_nodes
82 | INNER JOIN regions USING (region_id)
83 | WHERE end_date!='9999-12-31'),
84 | percentile_cte AS
85 | (SELECT *,
86 | percent_rank() over(PARTITION BY region_id
87 | ORDER BY reallocation_days)*100 AS p
88 | FROM reallocation_days_cte)
89 | SELECT region_id,
90 | region_name,
91 | reallocation_days
92 | FROM percentile_cte
93 | WHERE p >95
94 | GROUP BY region_id;
95 | ```
96 |
97 | #### Result set:
98 | 
99 |
100 |
101 | **80th percentile**
102 | ```sql
103 | WITH reallocation_days_cte AS
104 | (SELECT *,
105 | (datediff(end_date, start_date)) AS reallocation_days
106 | FROM customer_nodes
107 | INNER JOIN regions USING (region_id)
108 | WHERE end_date!='9999-12-31'),
109 | percentile_cte AS
110 | (SELECT *,
111 | percent_rank() over(PARTITION BY region_id
112 | ORDER BY reallocation_days)*100 AS p
113 | FROM reallocation_days_cte)
114 | SELECT region_id,
115 | region_name,
116 | reallocation_days
117 | FROM percentile_cte
118 | WHERE p >95
119 | GROUP BY region_id;
120 | ```
121 |
122 | #### Result set:
123 | 
124 |
125 |
126 | **50th percentile**
127 | ```sql
128 | WITH reallocation_days_cte AS
129 | (SELECT *,
130 | (datediff(end_date, start_date)) AS reallocation_days
131 | FROM customer_nodes
132 | INNER JOIN regions USING (region_id)
133 | WHERE end_date!='9999-12-31'),
134 | percentile_cte AS
135 | (SELECT *,
136 | percent_rank() over(PARTITION BY region_id
137 | ORDER BY reallocation_days)*100 AS p
138 | FROM reallocation_days_cte)
139 | SELECT region_id,
140 | region_name,
141 | reallocation_days
142 | FROM percentile_cte
143 | WHERE p >50
144 | GROUP BY region_id;
145 | ```
146 |
147 | #### Result set:
148 | 
149 |
150 |
151 | ***
152 |
153 |
154 | Click [here](https://github.com/manaswikamila05/8-Week-SQL-Challenge/blob/main/Case%20Study%20%23%204%20-%20Data%20Bank/B.%20Customer%20Transactions.md) to view the solution of B. Customer Transactions.md!
155 |
--------------------------------------------------------------------------------
/Case Study # 4 - Data Bank/B. Customer Transactions.md:
--------------------------------------------------------------------------------
1 | ## :technologist::woman_technologist: Case Study #4: Data Bank - Customer Transactions - WIP
2 |
3 | ## Case Study Questions
4 |
5 | 1. What is the unique count and total amount for each transaction type?
6 | 2. What is the average total historical deposit counts and amounts for all customers?
7 | 3. For each month - how many Data Bank customers make more than 1 deposit and either 1 purchase or 1 withdrawal in a single month?
8 | 4. What is the closing balance for each customer at the end of the month?
9 | 5. What is the percentage of customers who increase their closing balance by more than 5%?
10 |
11 | ***
12 |
13 | ### 1. What is the unique count and total amount for each transaction type?
14 |
15 | ```sql
16 | SELECT txn_type,
17 | count(*) AS unique_count,
18 | sum(txn_amount) AS total_amont
19 | FROM customer_transactions
20 | GROUP BY txn_type;
21 | ```
22 |
23 | #### Result set:
24 | 
25 |
26 | ***
27 |
28 | ### 2. What is the average total historical deposit counts and amounts for all customers?
29 |
30 | ```sql
31 | SELECT round(count(customer_id)/
32 | (SELECT count(DISTINCT customer_id)
33 | FROM customer_transactions)) AS average_deposit_count,
34 | concat('$', round(avg(txn_amount), 2)) AS average_deposit_amount
35 | FROM customer_transactions
36 | WHERE txn_type = "deposit";
37 | ```
38 |
39 | #### Result set:
40 | 
41 |
42 | ***
43 |
44 | ### 3. For each month - how many Data Bank customers make more than 1 deposit and either 1 purchase or 1 withdrawal in a single month?
45 |
46 | ```sql
47 | WITH transaction_count_per_month_cte AS
48 | (SELECT customer_id,
49 | month(txn_date) AS txn_month,
50 | SUM(IF(txn_type="deposit", 1, 0)) AS deposit_count,
51 | SUM(IF(txn_type="withdrawal", 1, 0)) AS withdrawal_count,
52 | SUM(IF(txn_type="purchase", 1, 0)) AS purchase_count
53 | FROM customer_transactions
54 | GROUP BY customer_id,
55 | month(txn_date))
56 | SELECT txn_month,
57 | count(DISTINCT customer_id) as customer_count
58 | FROM transaction_count_per_month_cte
59 | WHERE deposit_count>1
60 | AND (purchase_count = 1
61 | OR withdrawal_count = 1)
62 | GROUP BY txn_month;
63 | ```
64 |
65 | #### Result set:
66 | 
67 |
68 | ***
69 |
70 | ### 4. What is the closing balance for each customer at the end of the month?
71 |
72 | ```sql
73 | WITH txn_monthly_balance_cte AS
74 | (SELECT customer_id,
75 | txn_amount,
76 | month(txn_date) AS txn_month,
77 | SUM(CASE
78 | WHEN txn_type="deposit" THEN txn_amount
79 | ELSE -txn_amount
80 | END) AS net_transaction_amt
81 | FROM customer_transactions
82 | GROUP BY customer_id,
83 | month(txn_date)
84 | ORDER BY customer_id)
85 | SELECT customer_id,
86 | txn_month,
87 | net_transaction_amt,
88 | sum(net_transaction_amt) over(PARTITION BY customer_id
89 | ORDER BY txn_month ROWS BETWEEN UNBOUNDED preceding AND CURRENT ROW) AS closing_balance
90 | FROM txn_monthly_balance_cte;
91 | ```
92 |
93 | #### Result set:
94 | 
95 |
96 | ***
97 |
98 | ### 5. What is the percentage of customers who increase their closing balance by more than 5%?
99 |
100 | ```sql
101 |
102 | ```
103 |
104 | #### Result set:
105 |
106 | ***
107 |
108 |
--------------------------------------------------------------------------------
/Case Study # 4 - Data Bank/C. Data Allocation Challenge.md:
--------------------------------------------------------------------------------
1 | ## :technologist::woman_technologist: Case Study #4: Data Bank - Data Allocation Challenge
2 |
3 | To test out a few different hypotheses - the Data Bank team wants to run an experiment where different groups of customers would be allocated data using 3 different options:
4 |
5 | - **Option 1**: data is allocated based off the amount of money at the end of the previous month
6 | - **Option 2**: data is allocated on the average amount of money kept in the account in the previous 30 days
7 | - **Option 3**: data is updated real-time
8 |
9 |
10 | For this multi-part challenge question - you have been requested to generate the following data elements to help the Data Bank team estimate how much data will need to be provisioned for each option:
11 | - running customer balance column that includes the impact each transaction
12 | ```sql
13 | WITH transaction_amt_cte AS
14 | (SELECT *,
15 | month(txn_date) AS txn_month,
16 | SUM(CASE
17 | WHEN txn_type="deposit" THEN txn_amount
18 | ELSE -txn_amount
19 | END) AS net_transaction_amt
20 | FROM customer_transactions
21 | GROUP BY customer_id,
22 | txn_date
23 | ORDER BY customer_id,
24 | txn_date),
25 | running_customer_balance_cte AS
26 | (SELECT customer_id,
27 | txn_date,
28 | txn_month,
29 | txn_type,
30 | txn_amount,
31 | sum(net_transaction_amt) over(PARTITION BY customer_id
32 | ORDER BY txn_month ROWS BETWEEN UNBOUNDED preceding AND CURRENT ROW) AS running_customer_balance
33 | FROM transaction_amt_cte)
34 | SELECT *
35 | FROM running_customer_balance_cte;
36 | ```
37 | - customer balance at the end of each month
38 | ```sql
39 | WITH transaction_amt_cte AS
40 | (SELECT *,
41 | month(txn_date) AS txn_month,
42 | SUM(CASE
43 | WHEN txn_type="deposit" THEN txn_amount
44 | ELSE -txn_amount
45 | END) AS net_transaction_amt
46 | FROM customer_transactions
47 | GROUP BY customer_id,
48 | txn_date
49 | ORDER BY customer_id,
50 | txn_date),
51 | running_customer_balance_cte AS
52 | (SELECT customer_id,
53 | txn_date,
54 | txn_month,
55 | txn_type,
56 | txn_amount,
57 | sum(net_transaction_amt) over(PARTITION BY customer_id
58 | ORDER BY txn_month ROWS BETWEEN UNBOUNDED preceding AND CURRENT ROW) AS running_customer_balance
59 | FROM transaction_amt_cte),
60 | month_end_balance_cte AS
61 | (SELECT *,
62 | last_value(running_customer_balance) over(PARTITION BY customer_id, txn_month
63 | ORDER BY txn_month) AS month_end_balance
64 | FROM running_customer_balance_cte
65 | GROUP BY customer_id,
66 | txn_month)
67 | SELECT customer_id,
68 | txn_month,
69 | month_end_balance
70 | FROM month_end_balance_cte;
71 | ```
72 | - minimum, average and maximum values of the running balance for each customer
73 | ```sql
74 | WITH transaction_amt_cte AS
75 | (SELECT *,
76 | month(txn_date) AS txn_month,
77 | SUM(CASE
78 | WHEN txn_type="deposit" THEN txn_amount
79 | ELSE -txn_amount
80 | END) AS net_transaction_amt
81 | FROM customer_transactions
82 | GROUP BY customer_id,
83 | txn_date
84 | ORDER BY customer_id,
85 | txn_date),
86 | running_customer_balance_cte AS
87 | (SELECT customer_id,
88 | txn_date,
89 | txn_month,
90 | txn_type,
91 | txn_amount,
92 | sum(net_transaction_amt) over(PARTITION BY customer_id
93 | ORDER BY txn_month ROWS BETWEEN UNBOUNDED preceding AND CURRENT ROW) AS running_customer_balance
94 | FROM transaction_amt_cte
95 | GROUP BY customer_id,
96 | txn_month)
97 | SELECT customer_id,
98 | min(running_customer_balance),
99 | max(running_customer_balance),
100 | round(avg(running_customer_balance), 2) AS 'avg(running_customer_balance)'
101 | FROM running_customer_balance_cte
102 | GROUP BY customer_id
103 | ORDER BY customer_id ;
104 | ```
105 |
106 |
107 | Using all of the data available - how much data would have been required for each option on a monthly basis?
108 |
109 | ### **Option 1**: Data is allocated based off the amount of money at the end of the previous month
110 | How much data would have been required on a monthly basis?
111 |
112 | ```sql
113 | WITH transaction_amt_cte AS
114 | (SELECT *,
115 | month(txn_date) AS txn_month,
116 | SUM(CASE
117 | WHEN txn_type="deposit" THEN txn_amount
118 | ELSE -txn_amount
119 | END) AS net_transaction_amt
120 | FROM customer_transactions
121 | GROUP BY customer_id,
122 | txn_date
123 | ORDER BY customer_id,
124 | txn_date),
125 | running_customer_balance_cte AS
126 | (SELECT customer_id,
127 | txn_date,
128 | txn_month,
129 | txn_type,
130 | txn_amount,
131 | sum(net_transaction_amt) over(PARTITION BY customer_id
132 | ORDER BY txn_month ROWS BETWEEN UNBOUNDED preceding AND CURRENT ROW) AS running_customer_balance
133 | FROM transaction_amt_cte),
134 | month_end_balance_cte AS
135 | (SELECT *,
136 | last_value(running_customer_balance) over(PARTITION BY customer_id, txn_month
137 | ORDER BY txn_month) AS month_end_balance
138 | FROM running_customer_balance_cte),
139 | customer_month_end_balance_cte AS
140 | (SELECT customer_id,
141 | txn_month,
142 | month_end_balance
143 | FROM month_end_balance_cte
144 | GROUP BY customer_id,
145 | txn_month)
146 | SELECT txn_month,
147 | sum(month_end_balance) AS data_required_per_month
148 | FROM customer_month_end_balance_cte
149 | GROUP BY txn_month
150 | ORDER BY txn_month
151 | ```
152 |
153 | #### Result set:
154 | 
155 |
156 | **Observed**: Data required per month is negative. This is caused due to negative account balance maintained by customers at the end of the month.
157 |
158 | **Assumption**: Some customers do not maintain a positive account balance at the end of the month. I'm assuming that no data is allocated when the
159 | amount of money at the end of the previous month is negative. we can use **SUM(IF(month_end_balance > 0, month_end_balance, 0))** in the select clause to compute the total data requirement per month.
160 |
161 | #### Result set:
162 | 
163 |
164 | ***
165 |
166 | ### **Option 2**: Data is allocated on the average amount of money kept in the account in the previous 30 days
167 | How much data would have been required on a monthly basis?
168 |
169 | ```sql
170 | WITH transaction_amt_cte AS
171 | (SELECT *,
172 | month(txn_date) AS txn_month,
173 | SUM(CASE
174 | WHEN txn_type="deposit" THEN txn_amount
175 | ELSE -txn_amount
176 | END) AS net_transaction_amt
177 | FROM customer_transactions
178 | GROUP BY customer_id,
179 | txn_date
180 | ORDER BY customer_id,
181 | txn_date),
182 | running_customer_balance_cte AS
183 | (SELECT customer_id,
184 | txn_date,
185 | txn_month,
186 | txn_type,
187 | txn_amount,
188 | sum(net_transaction_amt) over(PARTITION BY customer_id
189 | ORDER BY txn_month ROWS BETWEEN UNBOUNDED preceding AND CURRENT ROW) AS running_customer_balance
190 | FROM transaction_amt_cte
191 | GROUP BY customer_id,
192 | txn_month),
193 | avg_running_customer_balance AS
194 | (SELECT customer_id,
195 | txn_month,
196 | avg(running_customer_balance) over(PARTITION BY customer_id) AS 'avg_running_customer_balance'
197 | FROM running_customer_balance_cte
198 | GROUP BY customer_id,
199 | txn_month
200 | ORDER BY customer_id)
201 | SELECT txn_month,
202 | round(sum(avg_running_customer_balance)) AS data_required_per_month
203 | FROM avg_running_customer_balance
204 | GROUP BY txn_month;
205 | ```
206 |
207 | #### Result set:
208 | 
209 |
210 |
211 |
212 | ### **Option 3**: Data is updated real-time
213 | How much data would have been required on a monthly basis?
214 |
215 | ```sql
216 | WITH transaction_amt_cte AS
217 | (SELECT *,
218 | month(txn_date) AS txn_month,
219 | SUM(CASE
220 | WHEN txn_type="deposit" THEN txn_amount
221 | ELSE -txn_amount
222 | END) AS net_transaction_amt
223 | FROM customer_transactions
224 | GROUP BY customer_id,
225 | txn_date
226 | ORDER BY customer_id,
227 | txn_date),
228 | running_customer_balance_cte AS
229 | (SELECT customer_id,
230 | txn_date,
231 | txn_month,
232 | txn_type,
233 | txn_amount,
234 | net_transaction_amt,
235 | sum(net_transaction_amt) over(PARTITION BY customer_id
236 | ORDER BY txn_month ROWS BETWEEN UNBOUNDED preceding AND CURRENT ROW) AS running_customer_balance
237 | FROM transaction_amt_cte)
238 | SELECT txn_month,
239 | SUM(running_customer_balance) AS data_required_per_month
240 | FROM running_customer_balance_cte
241 | GROUP BY txn_month;
242 | ```
243 |
244 | #### Result set:
245 | 
246 |
247 | ***
248 |
249 |
250 | Click [here](https://github.com/manaswikamila05/8-Week-SQL-Challenge) to move back to the 8-Week-SQL-Challenge repository!
251 |
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/Case Study # 4 - Data Bank/README.md:
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1 | # :technologist: :moneybag: :cloud: :chart: Case Study #4: Data Bank
2 |
3 | 
4 |
5 | View the case study [here](https://8weeksqlchallenge.com/case-study-4/)
6 |
7 | ## Table Of Contents
8 | - [Introduction](#introduction)
9 | - [Problem Statement](#problem-statement)
10 | - [Datasets used](#datasets-used)
11 | - [Entity Relationship Diagram](#entity-relationship-diagram)
12 | - [Case Study Solutions](#case-study-solutions)
13 |
14 | ## Introduction
15 | There is a new innovation in the financial industry called Neo-Banks: new aged digital only banks without physical branches.
16 |
17 | Danny thought that there should be some sort of intersection between these new age banks, cryptocurrency and the data world…so he decides to launch a new initiative - Data Bank!
18 |
19 | Data Bank runs just like any other digital bank - but it isn’t only for banking activities, they also have the world’s most secure distributed data storage platform!
20 |
21 | Customers are allocated cloud data storage limits which are directly linked to how much money they have in their accounts. There are a few interesting caveats that go with this business model, and this is where the Data Bank team need your help!
22 |
23 | ## Problem Statement
24 | The management team at Data Bank want to increase their total customer base - but also need some help tracking just how much data storage their customers will need.
25 |
26 | This case study is all about calculating metrics, growth and helping the business analyse their data in a smart way to better forecast and plan for their future developments!
27 |
28 |
29 | ## Datasets used
30 | Just like popular cryptocurrency platforms - Data Bank is also run off a network of nodes where both money and data is stored across the globe. In a traditional banking sense - you can think of these nodes as bank branches or stores that exist around the world. The regions table contains the region_id and their respective region_name values.
31 |
32 | 
33 |
34 | Customers are randomly distributed across the nodes according to their region - this also specifies exactly which node contains both their cash and data.
35 | This random distribution changes frequently to reduce the risk of hackers getting into Data Bank’s system and stealing customer’s money and data!
36 |
37 | Below is a sample of the top 10 rows of the data_bank.customer_nodes
38 | 
39 |
40 | Customer transaction table stores all customer deposits, withdrawals and purchases made using their Data Bank debit card
41 |
42 | 
43 |
44 |
45 | ## Entity Relationship Diagram
46 | 
47 |
48 | ## Case Study Solutions
49 | - [A. Customer Nodes Exploration](https://github.com/manaswikamila05/8-Week-SQL-Challenge/blob/main/Case%20Study%20%23%204%20-%20Data%20Bank/A.%20Customer%20Nodes%20Exploration.md)
50 | - [B. Customer Transactions](https://github.com/manaswikamila05/8-Week-SQL-Challenge/blob/main/Case%20Study%20%23%204%20-%20Data%20Bank/B.%20Customer%20Transactions.md)
51 | - [C. Data Allocation Challenge](https://github.com/manaswikamila05/8-Week-SQL-Challenge/blob/main/Case%20Study%20%23%204%20-%20Data%20Bank/C.%20Data%20Allocation%20Challenge.md)
52 |
53 |
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/Case Study # 5 - Data Mart/1. Data Cleansing Steps.md:
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1 | ## :shopping_cart: Case Study #5: Data Mart - Data Cleansing Steps
2 |
3 | In a single query, perform the following operations and generate a new table in the data_mart schema named clean_weekly_sales:
4 |
5 | - Convert the week_date to a DATE format
6 | - Add a **week_number** as the second column for each week_date value, for example any value from the 1st of January to 7th of January will be 1, 8th to 14th will be 2 etc
7 | - Add a **month_number** with the calendar month for each week_date value as the 3rd column
8 | - Add a **calendar_year** column as the 4th column containing either 2018, 2019 or 2020 values
9 | - Add a new column called **age_band** after the original segment column using the following mapping on the number inside the segment value
10 |
11 | 
12 |
13 | - Add a new **demographic** column using the following mapping for the first letter in the segment values
14 |
15 | 
16 |
17 | - Ensure all null string values with an "unknown" string value in the original segment column as well as the new age_band and demographic columns
18 | - Generate a new **avg_transaction** column as the sales value divided by transactions rounded to 2 decimal places for each record
19 |
20 | ***
21 |
22 | ```sql
23 | CREATE TABLE data_mart.clean_weekly_sales
24 | (WITH date_cte AS
25 | (SELECT *,
26 | str_to_date(week_date, '%d/%m/%Y') AS formatted_date
27 | FROM weekly_sales) SELECT formatted_date AS week_date,
28 | extract(WEEK
29 | FROM formatted_date) week_number,
30 | extract(MONTH
31 | FROM formatted_date) month_number,
32 | extract(YEAR
33 | FROM formatted_date) calendar_year,
34 | SEGMENT,
35 | CASE
36 | WHEN RIGHT(SEGMENT, 1) = '1' THEN 'Young Adults'
37 | WHEN RIGHT(SEGMENT, 1) = '2' THEN 'Middle Aged'
38 | WHEN RIGHT(SEGMENT, 1) in ('3',
39 | '4') THEN 'Retirees'
40 | ELSE 'unknown'
41 | END AS age_band,
42 | CASE
43 | WHEN LEFT(SEGMENT, 1) = 'C' THEN 'Couples'
44 | WHEN LEFT(SEGMENT, 1) = 'F' THEN 'Families'
45 | ELSE 'unknown'
46 | END AS demographic,
47 | ROUND(sales/transactions, 2) avg_transaction,
48 | region,
49 | platform,
50 | customer_type,
51 | sales,
52 | transactions
53 | FROM date_cte);
54 | ```
55 |
56 | ***
57 |
58 | ```sql
59 | SELECT *
60 | FROM clean_weekly_sales;
61 | ```
62 | #### Result set:
63 | 
64 |
65 | ***
66 |
67 | ```sql
68 | DESC clean_weekly_sales;
69 | ```
70 | #### Result set:
71 | 
72 |
73 | Click [here](https://github.com/manaswikamila05/8-Week-SQL-Challenge/blob/main/Case%20Study%20%23%205%20-%20Data%20Mart/2.%20Data%20Exploration.md) to view the solution of 2. Data Exploration!
74 |
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/Case Study # 5 - Data Mart/2. Data Exploration.md:
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1 | ## :shopping_cart: Case Study #5: Data Mart - Data Exploration
2 |
3 | ## Case Study Questions
4 | 1. What day of the week is used for each week_date value?
5 | 2. What range of week numbers are missing from the dataset?
6 | 3. How many total transactions were there for each year in the dataset?
7 | 4. What is the total sales for each region for each month?
8 | 5. What is the total count of transactions for each platform
9 | 6. What is the percentage of sales for Retail vs Shopify for each month?
10 | 7. What is the percentage of sales by demographic for each year in the dataset?
11 | 8. Which age_band and demographic values contribute the most to Retail sales?
12 | 9. Can we use the avg_transaction column to find the average transaction size for each year for Retail vs Shopify? If not - how would you calculate it instead?
13 |
14 | ***
15 |
16 | ### 1. What day of the week is used for each week_date value?
17 |
18 | ```sql
19 | SELECT DISTINCT dayname(week_date) AS day_of_week
20 | FROM clean_weekly_sales;
21 | ```
22 |
23 | #### Result set:
24 | 
25 |
26 | ***
27 |
28 | ### 2. What range of week numbers are missing from the dataset?
29 | - To get the current value of default_week_format variable : SHOW VARIABLES LIKE 'default_week_format';
30 |
31 | ```sql
32 | -- Range 0 to 52
33 |
34 | SELECT DISTINCT week(week_date) AS week_number
35 | FROM clean_weekly_sales
36 | ORDER BY week(week_date) ASC;
37 |
38 | -- Missing week numbers: Week 1 to 11 and week 36 to 52
39 | ```
40 |
41 | #### Result set:
42 | 
43 |
44 | ***
45 |
46 | ### 3. How many total transactions were there for each year in the dataset?
47 |
48 | ```sql
49 | SELECT year(week_date) AS YEAR,
50 | sum(transactions) AS total_transactions
51 | FROM clean_weekly_sales
52 | GROUP BY year(week_date)
53 | ORDER BY 1;
54 | ```
55 |
56 | #### Result set:
57 | 
58 |
59 | ***
60 |
61 | ### 4. What is the total sales for each region for each month?
62 |
63 | ```sql
64 | SELECT region,
65 | month_number,
66 | monthname(week_date) as month_name,
67 | sum(sales) AS total_sales
68 | FROM clean_weekly_sales
69 | GROUP BY region,
70 | month_number
71 | ORDER BY 1,
72 | 2;
73 | ```
74 |
75 | #### Result set:
76 | 
77 |
78 | ***
79 |
80 | ### 5. What is the total count of transactions for each platform
81 |
82 | ```sql
83 | SELECT platform,
84 | sum(transactions) AS transactions_count
85 | FROM clean_weekly_sales
86 | GROUP BY 1;
87 | ```
88 |
89 | #### Result set:
90 | 
91 |
92 | ***
93 |
94 | ### 6. What is the percentage of sales for Retail vs Shopify for each month?
95 |
96 | Using GROUP BY and WINDOW FUNCTION
97 | ```sql
98 | WITH sales_contribution_cte AS
99 | (SELECT calendar_year,
100 | month_number,
101 | platform,
102 | sum(sales) AS sales_contribution
103 | FROM clean_weekly_sales
104 | GROUP BY 1,
105 | 2,
106 | 3
107 | ORDER BY 1,
108 | 2),
109 | total_sales_cte AS
110 | (SELECT *,
111 | sum(sales_contribution) over(PARTITION BY calendar_year, month_number) AS total_sales
112 | FROM sales_contribution_cte)
113 | SELECT calendar_year,
114 | month_number,
115 | ROUND(sales_contribution/total_sales*100, 2) AS retail_percent,
116 | 100-ROUND(sales_contribution/total_sales*100, 2) AS shopify_percent
117 | FROM total_sales_cte
118 | WHERE platform = "Retail"
119 | ORDER BY 1,
120 | 2;
121 | ```
122 |
123 | Using GROUP BY AND CASE statements
124 | ```sql
125 | WITH sales_cte AS
126 | (SELECT calendar_year,
127 | month_number,
128 | SUM(CASE
129 | WHEN platform="Retail" THEN sales
130 | END) AS retail_sales,
131 | SUM(CASE
132 | WHEN platform="Shopify" THEN sales
133 | END) AS shopify_sales,
134 | sum(sales) AS total_sales
135 | FROM clean_weekly_sales
136 | GROUP BY 1,
137 | 2
138 | ORDER BY 1,
139 | 2)
140 | SELECT calendar_year,
141 | month_number,
142 | ROUND(retail_sales/total_sales*100, 2) AS retail_percent,
143 | ROUND(shopify_sales/total_sales*100, 2) AS shopify_percent
144 | FROM sales_cte;
145 | ```
146 |
147 | #### Result set:
148 | 
149 |
150 |
151 | ***
152 |
153 | ### 7. What is the percentage of sales by demographic for each year in the dataset?
154 |
155 | ```sql
156 | WITH sales_contribution_cte AS
157 | (SELECT calendar_year,
158 | demographic,
159 | sum(sales) AS sales_contribution
160 | FROM clean_weekly_sales
161 | GROUP BY 1,
162 | 2
163 | ORDER BY 1),
164 | total_sales_cte AS
165 | (SELECT *,
166 | sum(sales_contribution) over(PARTITION BY calendar_year) AS total_sales
167 | FROM sales_contribution_cte)
168 | SELECT calendar_year,
169 | demographic,
170 | ROUND(100*sales_contribution/total_sales, 2) AS percent_sales_contribution
171 | FROM total_sales_cte
172 | GROUP BY 1,
173 | 2;
174 | ```
175 |
176 | #### Result set:
177 | 
178 |
179 | ```sql
180 | WITH sales_cte AS
181 | (SELECT calendar_year,
182 | SUM(CASE
183 | WHEN demographic="Couples" THEN sales
184 | END) AS couple_sales,
185 | SUM(CASE
186 | WHEN demographic="Families" THEN sales
187 | END) AS family_sales,
188 | SUM(CASE
189 | WHEN demographic="unknown" THEN sales
190 | END) AS unknown_sales,
191 | sum(sales) AS total_sales
192 | FROM clean_weekly_sales
193 | GROUP BY 1
194 | ORDER BY 1)
195 | SELECT calendar_year,
196 | ROUND(couple_sales/total_sales*100, 2) AS couple_percent,
197 | ROUND(family_sales/total_sales*100, 2) AS family_percent,
198 | ROUND(unknown_sales/total_sales*100, 2) AS unknown_percent
199 | FROM sales_cte;
200 | ```
201 | #### Result set:
202 | 
203 |
204 |
205 | ***
206 |
207 | ### 8. Which age_band and demographic values contribute the most to Retail sales?
208 |
209 | ```sql
210 | SELECT age_band,
211 | demographic,
212 | ROUND(100*sum(sales)/
213 | (SELECT SUM(sales)
214 | FROM clean_weekly_sales
215 | WHERE platform="Retail"), 2) AS retail_sales_percentage
216 | FROM clean_weekly_sales
217 | WHERE platform="Retail"
218 | GROUP BY 1,
219 | 2
220 | ORDER BY 3 DESC;
221 | ```
222 |
223 | #### Result set:
224 |
225 |
226 | ***
227 |
228 | ### 9. Can we use the avg_transaction column to find the average transaction size for each year for Retail vs Shopify? If not - how would you calculate it instead?
229 |
230 | Let's try this mathematically.
231 | Consider average of (4,4,4,4,4,4) = (4*6)/6 = 4 and average(5) = 5
232 | Average of averages = (4+5)/2 = 4.5
233 | Average of all numbers = (24+5)/ = 4.1428
234 |
235 | Hence, we can not use avg_transaction column to find the average transaction size for each year and sales platform, because the result will be incorrect if we calculate average of an average to calculate the average.
236 |
237 | ```sql
238 | SELECT calendar_year,
239 | platform,
240 | ROUND(SUM(sales)/SUM(transactions), 2) AS correct_avg,
241 | ROUND(AVG(avg_transaction), 2) AS incorrect_avg
242 | FROM clean_weekly_sales
243 | GROUP BY 1,
244 | 2
245 | ORDER BY 1,
246 | 2;
247 | ```
248 |
249 | #### Result set:
250 | 
251 |
252 |
253 | ***
254 |
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/Case Study # 5 - Data Mart/3. Before & After Analysis.md:
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1 | ## :shopping_cart: Case Study #5: Data Mart - Before & After Analysis
2 |
3 | This technique is usually used when we inspect an important event and want to inspect the impact before and after a certain point in time.
4 |
5 | Taking the week_date value of **2020-06-15** as the baseline week where the Data Mart sustainable packaging changes came into effect.
6 |
7 | We would include all week_date values for 2020-06-15 as the start of the period after the change and the previous week_date values would be before
8 |
9 | Using this analysis approach - answer the following questions:
10 | 1. What is the total sales for the 4 weeks before and after 2020-06-15? What is the growth or reduction rate in actual values and percentage of sales?
11 | 2. What about the entire 12 weeks before and after?
12 | 3. How do the sale metrics for these 2 periods before and after compare with the previous years in 2018 and 2019?
13 |
14 |
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/Case Study # 5 - Data Mart/README.md:
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1 | # :convenience_store: :shopping_cart: Case Study #5: Data Mart
2 |
3 | 
4 |
5 | View the case study [here](https://8weeksqlchallenge.com/case-study-5/)
6 |
7 | ## Table Of Contents
8 | - [Introduction](#introduction)
9 | - [Problem Statement](#problem-statement)
10 | - [Dataset used](#dataset-used)
11 | - [Case Study Solutions](#case-study-solutions)
12 |
13 | ## Introduction
14 | Data Mart is Danny’s latest venture and after running international operations for his online supermarket that specialises in fresh produce - Danny is asking for your support to analyse his sales performance.
15 |
16 | In June 2020 - large scale supply changes were made at Data Mart. All Data Mart products now use sustainable packaging methods in every single step from the farm all the way to the customer.
17 |
18 | Danny needs your help to quantify the impact of this change on the sales performance for Data Mart and it’s separate business areas.
19 |
20 | ## Problem Statement
21 | The key business question he wants you to help him answer are the following:
22 |
23 | - What was the quantifiable impact of the changes introduced in June 2020?
24 | - Which platform, region, segment and customer types were the most impacted by this change?
25 | What can we do about future introduction of similar sustainability updates to the business to minimise impact on sales?
26 |
27 | ## Dataset used
28 | 
29 |
30 | 1. Data Mart has international operations using a multi-region strategy
31 | 2. Data Mart has both, a retail and online platform in the form of a Shopify store front to serve their customers
32 | 3. Customer segment and customer_type data relates to personal age and demographics information that is shared with Data Mart
33 | 4. transactions is the count of unique purchases made through Data Mart and sales is the actual dollar amount of purchases
34 | Each record in the dataset is related to a specific aggregated slice of the underlying sales data rolled up into a week_date value which represents the start of the sales week.
35 |
36 | 10 random rows are shown in the table output below from data_mart.weekly_sales
37 | 
38 |
39 |
40 | ## Case Study Solutions
41 | - [A. Data Cleansing Steps](https://github.com/manaswikamila05/8-Week-SQL-Challenge/blob/main/Case%20Study%20%23%205%20-%20Data%20Mart/1.%20Data%20Cleansing%20Steps.md)
42 | - [B. Data Exploration]()
43 | - [C. Before & After Analysis]()
44 | - [D. Bonus Question]()
45 |
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/README.md:
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1 | # 8-Week-SQL-Challenge
2 |
3 | This repository contains the solutions for the case studies in **[8WeekSQLChallenge](https://8weeksqlchallenge.com)**.
4 | The 8 Week SQL Challenge is started by Danny Ma through Data With Danny virtual data apprenticeship program, which consists of 8 different SQL challenges.
5 |
6 | Each case-study folder contains the following files
7 | - A readme file explaining the problem statement and datasets used
8 | - SQL file to create the schema, tables and loading data
9 | - MD files which contains SQL queries answering the respective challenge questions
10 |
11 | **Note**:
12 | - Solutions are coded in **MySQL**
13 |
14 | ## Challenge case studies
15 | * 🍜[Case Study #1 - Danny's Diner](https://github.com/manaswikamila05/8-Week-SQL-Challenge/tree/main/Case%20Study%20%23%201%20-%20Danny's%20Diner)
16 | * 🍕[Case Study #2 - Pizza Runner](https://github.com/manaswikamila05/8-Week-SQL-Challenge/tree/main/Case%20Study%20%23%202%20-%20Pizza%20Runner)
17 | * 🥑[Case Study #3 - Foodie-Fi](https://github.com/manaswikamila05/8-Week-SQL-Challenge/tree/main/Case%20Study%20%23%203%20-%20Foodie-Fi)
18 | * 🪙[Case Study #4 - Data Bank](https://github.com/manaswikamila05/8-Week-SQL-Challenge/tree/main/Case%20Study%20%23%204%20-%20Data%20Bank)
19 |
20 | ## Support
21 | Give a ⭐️ if you like this project!
22 |
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