Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated:
1
00:00:01,080 --> 00:00:05,880
The first thing the analyst must know is the database and the data stored in it.
2
00:00:07,070 --> 00:00:10,980
Now our company is storing data in three tables.
3
00:00:11,790 --> 00:00:14,910
First is the customers table in this table.
4
00:00:14,940 --> 00:00:22,140
Each registering customer is assigned a unique customer ID and against this ID, all other customer
5
00:00:22,140 --> 00:00:27,690
details such as customer name, age, city, state of residence, etc. are being stored.
6
00:00:28,620 --> 00:00:30,270
Second is the products table.
7
00:00:30,720 --> 00:00:36,360
In this table, our supply chain team maintains a list of all products we are selling.
8
00:00:37,310 --> 00:00:38,150
With this.
9
00:00:38,240 --> 00:00:42,140
They also stored the name, subcategory and category of each product.
10
00:00:43,670 --> 00:00:47,540
Each product is also assigned a unique ID called Product ID.
11
00:00:49,660 --> 00:00:52,390
Third is the transaction table or the sales table.
12
00:00:53,220 --> 00:00:55,530
All its transactions are stored into it.
13
00:00:56,530 --> 00:01:01,660
It has product ID of product being sold, it has customer ID of customer who is buying it.
14
00:01:02,830 --> 00:01:08,740
And it has other details such as order, date, ship, date, selling, price and discount, etc..
15
00:01:09,130 --> 00:01:11,380
Let us go and look at these tables first.
16
00:01:12,680 --> 00:01:13,760
The first query.
17
00:01:17,400 --> 00:01:19,530
Get the result in the window below.
18
00:01:21,420 --> 00:01:29,010
The customer table as this structure in the columns, it has column ID, column name, segment, age,
19
00:01:29,010 --> 00:01:33,240
country, city, state, postal code and the region.
20
00:01:34,590 --> 00:01:35,610
In the rose.
21
00:01:35,610 --> 00:01:38,670
It has all the records of different customers.
22
00:01:40,050 --> 00:01:41,820
Let us look at the product table.
23
00:01:44,790 --> 00:01:49,230
It has only four columns product ID, category subcategory and the product name.
24
00:01:49,680 --> 00:01:52,680
And each product is in the different rows.
25
00:01:53,970 --> 00:01:57,330
That's the third query to look at the sales table.
26
00:01:59,260 --> 00:01:59,930
In this exhibit.
27
00:02:00,010 --> 00:02:02,620
We have lots of rows and columns.
28
00:02:04,000 --> 00:02:10,150
In the columns you can see we have customer ID also, this is the ID of customer who is ordering it.
29
00:02:10,690 --> 00:02:12,520
We have product ID also.
30
00:02:12,550 --> 00:02:14,620
This is the product being ordered.
31
00:02:15,560 --> 00:02:22,880
We have other details, such as sales value, quantity ordered, discount given and the profit earned.
32
00:02:23,240 --> 00:02:26,330
Now, let us see if we can answer the questions of our colleagues.
33
00:02:27,140 --> 00:02:32,660
Now, the marketing manager is asking us to provide the number of customers belonging to the three age
34
00:02:32,660 --> 00:02:34,850
groups in all the four regions.
35
00:02:35,420 --> 00:02:38,870
To do this, I have this query written.
36
00:02:40,100 --> 00:02:41,600
If I run this query.
37
00:02:43,380 --> 00:02:44,790
I get the following result.
38
00:02:46,100 --> 00:02:53,270
You can see that in every region except south Category two as the maximum count.
39
00:02:54,530 --> 00:03:03,350
These are the people between the age of 36 to 54, whereas in south category three has maximum count.
40
00:03:04,310 --> 00:03:11,120
So marketing managers can now choose appropriate marketing channel in South region to focus on customers
41
00:03:11,270 --> 00:03:15,800
who have more than 56 years of age in all the three other regions.
42
00:03:16,070 --> 00:03:20,900
The marketing manager can go ahead with a common marketing channel aimed at middle aged customer.
43
00:03:22,280 --> 00:03:25,460
Let us see what ops manager is asking us.
44
00:03:25,940 --> 00:03:31,190
He wants to find out the top selling products of East and least selling products of South Region.
45
00:03:32,140 --> 00:03:33,610
So we go to the database.
46
00:03:35,080 --> 00:03:37,630
I will write and run the corresponding queries.
47
00:03:39,080 --> 00:03:41,630
First to get the top selling products of East.
48
00:03:41,660 --> 00:03:43,640
We run this query.
49
00:03:45,970 --> 00:03:50,230
You can see these are the top five selling products of East region.
50
00:03:51,930 --> 00:03:57,900
The operations manager should be ordering these five products since these are high selling products
51
00:03:57,900 --> 00:03:58,920
in the East region.
52
00:03:59,950 --> 00:04:04,480
Now let us run the second query to find out the least selling products of South.
53
00:04:07,410 --> 00:04:11,430
We can see that these are the five products with minimum sales.
54
00:04:12,270 --> 00:04:19,200
The operations manager can recall these products to make some space in the South warehouse.
55
00:04:21,640 --> 00:04:24,580
Next, let us see what finance manager is asking us.
56
00:04:25,650 --> 00:04:32,700
He wants the total revenue loss from discounts and the comparison of discounts given versus revenue
57
00:04:32,700 --> 00:04:34,500
generated by each product.
58
00:04:35,940 --> 00:04:38,790
So we write the corresponding query and run it.
59
00:04:39,600 --> 00:04:42,030
If I select this first query and run it.
60
00:04:44,510 --> 00:04:52,730
We get that 322,000 units is the total revenue loss because of the discount given.
61
00:04:54,530 --> 00:04:56,570
When I select and run the second query.
62
00:04:59,280 --> 00:05:03,300
We find out the sales of each product in the revenue column.
63
00:05:04,470 --> 00:05:08,370
We find out the discount given for each product in the discount column.
64
00:05:09,180 --> 00:05:09,590
Indeed.
65
00:05:09,600 --> 00:05:10,560
Each of these two.
66
00:05:11,340 --> 00:05:18,360
I've ordered it in the descending order of this ratio, which means that for the first product, we
67
00:05:18,360 --> 00:05:22,410
have given four times the discount than the revenue it has generated.
68
00:05:24,060 --> 00:05:29,970
Similarly, the situation with the next few product where we have given more discount than the revenue
69
00:05:29,970 --> 00:05:37,590
that particular product was generating, the finance manager needs to study these products and decide
70
00:05:37,620 --> 00:05:44,040
either to raise the price of these products or to discontinue the coupon or to discontinue the sale
71
00:05:44,040 --> 00:05:45,420
of this product altogether.
72
00:05:46,470 --> 00:05:52,860
You can see that by writing these few lines, which seem to be in simple English language, you are
73
00:05:52,860 --> 00:05:55,740
able to get meaningful information out of your data.
74
00:05:56,550 --> 00:06:02,700
We will learn everything about these queries in the videos to come, and I assure you that it is simple
75
00:06:02,700 --> 00:06:06,600
and you will be able to write complex queries after completing the course.
76
00:06:07,560 --> 00:06:13,320
But before starting with query straightaway, let's first learn some basic theory of data management.
77
00:06:14,430 --> 00:06:15,150
For this.
78
00:06:15,630 --> 00:06:17,190
See you in the next section.
7629
Can't find what you're looking for?
Get subtitles in any language from opensubtitles.com, and translate them here.