# Data Analytics With Tableau - Session 14

https://www.youtube.com/watch?v=potW-fe1HrI

[06:10] Anyone?
[27:30] Let's see the count is right now.
[27:33] We'll wait for some time and then start asking.
[37:40] Hello all.
[37:43] Very good afternoon to all the participants.
[37:44] I hope your voice is audible.
[37:50] Yes or no?
[37:50] Okay, great.
[37:53] So when you're talking to all the participants, I can see uh just because the telegram group is not working.
[37:57] So a lot of students are facing the communication issues.
[38:02] Anyways guys uh we are also doing the campaigning before launching the session.
[38:07] Uh I hope every student will receive the invitations of these YouTube links in their emails.
[38:12] Right.
[38:16] I request all the participants to please be active on the emails during this particular period since the telegram is restricted till 22nd of June.
[38:24] Okay.
[38:27] So please active please keep uh checking your inbox of your emails for any kind of important informations.
[38:32] All right.
[38:36] So I welcome you all in again in today's session on today's session number 14.
[38:42] Right. Uh we are uh about to conclude our training session tomorrow.
[38:50] So uh very good guys.
[38:53] Uh you have come up to so long in this in this journey.
[38:55] I am very happy that uh you are actively participating in this session and uh giving the proper with proper engagement.
[39:08] Okay. So uh I just want to ask you guys if any of your friend is facing any difficulty guys, we have shared you the YouTube link in the chat box.
[39:19] Okay, please check it and share it with your friends.
[39:24] Okay, I've also shared the gate link in the YouTube also.
[39:26] If any of your friends is willing to uh directly join the YouTube link, you can do join.
[39:33] Okay. All right.
[39:33] So, can we recap like what we have seen in the last session?
[39:47] What we have seen in the last session.
[39:49] Guys,
[39:54] Can you recap?
[40:04] Omar Danisha says QTC quick calculations.
[40:11] Great.
[40:15] So how many clickable calculations we have seen yesterday?
[40:17] How many clickable calculations we have seen?
[40:26] We have seen two.
[40:26] We have seen four.
[40:28] We have seen six.
[40:28] We have seen 20.
[40:32] 11 cable calculations.
[40:35] 11 critable calculations was seen yesterday.
[40:37] Okay.
[40:37] So what areable calculations?
[40:38] Descriptive calculations are a pre-built calculator uh calculations which is present in Tableau to calculate the things without writing down any
[40:47] particular syntax.
[40:51] Okay, I hope calculations is clear to all the participants.
[40:55] Right.
[40:57] Now today's agenda I'll tell you uh today's agenda.
[41:02] Today's agenda is to discuss about visual analytics pain in Tableau.
[41:07] Okay. And if time permits we'll see bit of uh KPI also like how to create KPI in the table.
[41:13] Okay let's uh let me share my screen please confirm if my screen is visible yes or no.
[41:28] Uh common please share that uh Google drive link also here so that participants can see to it.
[41:42] I hope all the participants can see my screen.
[41:44] Correct?
[41:47] Yes or no?
[41:51] So guys, uh as I mentioned you today we are going to discuss about visual analytics P.
[41:56] Okay, visual analytics P.
[42:00] Okay, but why do we need this visual analytics P?
[42:03] The question is why do we need this visual and explain?
[42:09] Okay, if you open Tableau, if you open Tableau, there are two PES, right?
[42:16] Let me show you.
[42:25] Let me show you when I'm opening my Tableau.
[42:31] Okay.
[42:34] I'll connect to my data set in my sheet.
[42:45] You can see there are two pins.
[42:48] One is data
[42:51] pin and second is analytic pin.
[42:54] Second is analytics pin.
[42:57] We have data pin and we have analytics pin.
[42:59] So can you tell me guys uh why this analytics pane is required?
[43:10] See uh imagine that you have created one chart.
[43:13] Okay, you have created one chart which is just showing you the sales data.
[43:17] Okay, it is showing you the sales data.
[43:20] Now this chart which you have created, it just shows you uh numbers or kind of a bar graph, right?
[43:26] But if you want to answer some different questions, for example, if you want to answer like is the sales increasing?
[43:33] Okay.
[43:33] Uh if you want to find answers like uh what will be the sale for the next month?
[43:42] Okay.
[43:42] Which customers are similar?
[43:45] Which product is unusual?
[43:48] Okay.
[43:48] So instead of writing any kind of complex calculation, Tableau gives you this analytics train.
[43:53] Okay.
[43:56] So what is analytics pane?
[43:58] Analytics pane is a collection of readymade analytical tools which can be used by dragging and dropping into the view.
[44:08] Okay, it's a kind of a smart toolbox which will help you to transform your simple chart into an intelligent chart.
[44:17] Okay,
[44:21] you have basic idea about visual and explain.
[44:29] All right.
[44:30] Now I'll jump to my PPT.
[44:35] Okay.
[44:35] I'll jump to my PPT and here we'll discuss about what is visual exp and why we use visual explain.
[44:48] Okay.
[44:48] So what is visual explain?
[44:48] So it's a panel in Tableau that provides you built-in analytical tools.
[44:52] Okay.
[44:52] It's a
[44:55] panel which gives you built-in analytical tools for uh converting your simple chart into an intelligent chart.
[45:03] Okay.
[45:03] So what it does it enhances your visualizations by giving you trends, forecast, clusters, summaries and many more options.
[45:10] Okay, the best part is it is as same as uh your normal visualizations that you create.
[45:19] You can simply drag and drop them into the view.
[45:22] Okay.
[45:22] And using this analytical analytical pane, you can make your visualization or any dashboard very interactive.
[45:28] Okay?
[45:32] I hope this makes some sense.
[45:33] Yes or no?
[45:40] Okay.
[45:40] Now the question is why do we need this visual explain?
[45:43] Why do we need it?
[45:46] I think by now you have clarity but still I'll try to cover this point as well.
[45:50] So it quickly adds statistical insights.
[45:53] Okay.
[45:53] See nowadays guys uh
[45:56] business they don't business owners or business users they don't need any kind of reports.
[46:01] Okay, they don't need only the reports.
[46:03] They also need insights.
[46:05] Okay, they also need the insights.
[46:08] Analytical pain will help you to find trend.
[46:10] It will help you to find all the patterns, some unusual behavior like your outliers.
[46:15] You can actually compare your performance with the target.
[46:17] Okay, got it.
[46:22] As I mentioned, the very easiest way or the best part here is it's a simple drag and drop approach.
[46:37] So, uh for example, let's talk about uh normal case.
[46:40] Let's suppose there is a principle.
[46:42] Okay, and there are a lot of students in different classes, right?
[46:47] There might be certain set of students in 12th, there might be certain student in set of uh the 10th class, there may be certain set of students in 11th class, right?
[46:55] Now the
[46:58] principal instead of getting a graph related to attendance or not he wants to uh understand is the attendance of the students is improving or not.
[47:08] Okay.
[47:11] Or which class uh is performing below average.
[47:14] Okay.
[47:18] He can also expect the attendance for the next month based on the current ones.
[47:23] Okay. based on the previous and the current month he can uh he can expect or he can forecast the attendance for the upcoming months.
[47:31] Okay.
[47:34] So instead of manually calculating everything it will provide you instant answers.
[47:37] Okay.
[47:38] Tableau is going to provide you instant answers.
[47:42] All right.
[47:45] Now what are the key features of uh visual and explain?
[47:51] Okay.
[47:53] So these are some of the options which we can create in uh analytical thing.
[47:55] First is trend line.
[47:55] Okay.
[47:55] So with help
[47:58] of trend line you can understand whether your performance is improving or not.
[48:02] Then forecasting like you can predict like what will happen next.
[48:06] Reference line. So you can mark some line as a uh reference and you can compare like uh are you trying to are you near to your target?
[48:19] Okay. Have you achieved your target like that?
[48:22] Clusters basically when you want to form a group of similar items or similar objects, you can create clusters.
[48:29] Okay. Box plot will help you to identify any kind of outliers.
[48:33] Totals just gives you the totals.
[48:37] Okay. And constant line if you want to set any line constant you can use it.
[48:41] Constant line is a fixed line.
[48:43] Right?
[48:47] Clear. Yes or no?
[48:50] Yes or no?
[48:50] Guys, I need thumbs up.
[48:57] I need every participant to respond and give thumbs up.
[48:57] Okay, meanwhile, let me have some
[48:59] water.
[49:19] Okay, let's move ahead.
[49:23] So let's understand like what is trend line.
[49:25] Okay, so trend line shows the direction of movement of data over the time.
[49:30] Basically, it is just it will show you overall direction of the data.
[49:36] Okay, with trend line you can understand whether your business is growing or declining.
[49:39] Whether it is a positive relation or negative relation you can find.
[49:43] Okay, so if the line is upward it means it's growing.
[49:47] Okay.
[49:52] If the line is downwards it means it's declining.
[49:56] Okay.
[49:56] Next is forecasting.
[49:56] With
[49:59] Forecasting, you can predict the future values using the historical depth.
[50:01] Okay.
[50:04] You can predict the future values using the historical data.
[50:10] Okay.
[50:10] So generally business organizations use forecasting for planning purpose.
[50:14] Okay.
[50:18] So useful for sales forecasting, demand prediction and inventory means.
[50:22] I hope you are able to follow this.
[50:27] Okay.
[50:28] Uh let's suppose there is a bakery.
[50:33] There is a bakery who is selling some certain amount of cakes in January.
[50:39] Let's suppose he sold 100 cakes.
[50:43] In February he sold 120 cakes.
[50:45] In March he sold 140 cakes.
[50:49] Like that he made some sales of the cakes till June.
[50:49] Okay.
[50:52] Now if you want to find the prediction of the next three months, you can easily get it.
[50:56] Okay.
[50:56] You can get it for the next coming months.
[50:58] All right.
[51:03] After this we have reference line.
[51:05] Reference line what is reference line?
[51:08] Uh reference line you can as I said it.
[51:11] Can you can assume reference line kind of a target or benchmark.
[51:13] Okay.
[51:17] You can consider reference line as a target or a benchmark.
[51:20] Okay.
[51:23] So this target or the benchmark as I said will help you to compare your active performance against the expected performance.
[51:28] Okay.
[51:33] It will help you to compare your performance versus the expected performance.
[51:39] Okay.
[51:41] Let's suppose you all are there in your colleges, right?
[51:44] So, you'll be having some uh benchmark in your uh in your exams, right?
[51:49] I want to achieve this much.
[51:50] I want to achieve more than this, right?
[51:52] Yes or no?
[51:54] You will be having a benchmark of 85%.
[51:56] Obviously, correct?
[51:59] That is nothing but a reference point or reference line that you have set in your academics to achieve.
[52:01] Okay, that is the expected
[52:04] Okay, but whatever score you get that is your actual performance.
[52:09] Okay, so then later you can compare what is what was expected and how much you have achieved.
[52:14] All right.
[52:18] All right.
[52:22] In business organizations, uh businesses set some target.
[52:24] Okay.
[52:27] They set some target as a reference target.
[52:30] Okay.
[52:33] So if the target is achieved, if the target is achieved, it's good.
[52:35] If the target is not achieved, it will fall below the target.
[52:37] Okay.
[52:40] Below the target line or below the reference line.
[52:44] Okay.
[52:46] So similar to reference line, we have reference band and distributions.
[52:49] Okay.
[52:52] So reference band basically highlights a acceptable range.
[52:55] Okay.
[52:56] reference band highlights acceptable range.
[52:59] For example, you set a target of 85% in your academics.
[52:59] Now you can assume that the
[53:04] band of reference band can be plus five or minus five, right?
[53:11] You can assume that yes or no.
[53:13] Let's suppose you have written down an exam and somebody asks you what is the expected marks that you might get.
[53:18] So you'll not say like I will get 85%.
[53:20] You'll say an expected range, right?
[53:22] You get you give a band correct.
[53:25] You give a range to him.
[53:27] Yes or no?
[53:27] I might get uh 70 to 80.
[53:30] I might get 80 to 90.
[53:30] I might get 90 95 like that.
[53:34] Yes or no?
[53:34] You give a kind of a uh range to the user, right?
[53:40] That is nothing but here you can consider it as a reference plan.
[53:42] Okay.
[53:44] It just highlights an acceptable range.
[53:44] Okay, got it?
[53:49] Yes or no?
[53:52] For example, uh the organization in the same organization also sets a target, right?
[53:57] For example, they keep the reference as 15,000.
[54:01] Suppose this is
[54:04] a sales target they keep.
[54:07] So they they can keep the reference band from 10,000 to 20,000.
[54:09] Okay.
[54:12] So in between this band, in between this range, they can get anything.
[54:15] Okay.
[54:17] They should get anything.
[54:17] Okay.
[54:20] Yes or no?
[54:24] So uh let me give you a very popular example of uh health sector.
[54:26] Okay.
[54:30] How many you know right uh there is a blood pressure uh there is a machine which comes to measure your blood pressure right.
[54:36] So the acceptable range of a blood pressure is from uh think from 120 to 140 right?
[54:41] The acceptable range of the blood pressure is from 120 to 140.
[54:47] So we say that if our value or if the if our blood pressure falls in between this range we consider it as an acceptable range.
[54:56] Yes or no?
[54:56] Yes or no?
[55:02] Right?
[55:02] The blood pressure.
[55:05] And any value which is lying outside this range needs the attention.
[55:07] Got it?
[55:11] any value it is lying outside this uh band or range needs your attention.
[55:17] Okay,
[55:20] I hope this makes some sense and you're able to follow me.
[55:23] The reference man clear uh can I get a quick thumbs up the difference between reference line and reference band?
[55:30] Reference line and reference band.
[55:32] Participants on the YouTube are you able to follow?
[55:49] Okay, great.
[55:49] Next is clustering.
[55:52] Uh, can somebody tell me what do you mean by cluster?
[55:57] I need some reply from you guys.
[55:59] What do you mean by what do you mean by clusters?
[56:05] Groups, right? Groups. Cluster means
[56:09] groups. Let's suppose uh there are 100
[56:12] students in your class. Okay. Now all of
[56:15] you have written down some exam. Okay.
[56:19] So you want to form a group of students
[56:23] who got grade A. You want to form a
[56:26] group of students who got grade B. You
[56:28] want to form a group of group of
[56:30] students who got grade C. Okay. So what
[56:33] we doing? We are creating a group of
[56:35] students with different
[56:37] grades. Okay. That is nothing but uh
[56:40] each group is nothing but called as a
[56:41] cluster. Okay. Uh let's suppose in
[56:46] shopping malls in premium shopping malls
[56:48] there are different types of customer.
[56:50] They have premium customer, they have
[56:51] regular customer, they have occasional
[56:53] customer, right? These are nothing but a
[56:56] small group of customers which are
[56:57] classified into different categories.
[57:00] Okay.
[57:01] Clear groups. Clear. Cluster clear to
[57:04] everyone. What is cluster?
[57:07] So with the help of clustering you can
[57:11] uh automatically create a group of
[57:13] similar records. Okay, you can create a
[57:16] group of similar records.
[57:20] Then you have total and subtotal. So
[57:23] it's very obvious we have seen this
[57:25] totals and subtotals during our
[57:26] practical also. Total provides you the
[57:28] quick summation of the data. Okay.
[57:31] Instead of manually adding the values,
[57:33] it calculates the total automatically.
[57:35] Okay. Similarly, sub totals also can be
[57:37] calculated for each uh section wise.
[57:41] Okay, each section you can calculate.
[57:46] I hope totals and sub totals is also
[57:48] clear to everyone.
[57:52] Okay,
[57:54] let's go to the next one. Uh constant
[57:58] line. Constant line. So what is constant
[58:01] line?
[58:03] Constant line are nothing but the fixed
[58:06] value across your chart. Okay, it's the
[58:08] fixed value across your chart. For
[58:11] example, uh
[58:14] you have a target of 10 lakh. Okay, in
[58:17] your organization, you have a target of
[58:18] 10 lakh. Okay, so you can set this
[58:21] constant line in your visual and compare
[58:24] the remaining values with it. Okay, you
[58:28] can compare the remaining values
[58:30] uh with it like which uh which suppose
[58:34] which region meet your target which
[58:36] region is unable to meet your target
[58:37] like that. Okay,
[58:46] got it. Constant line. So it's a fixed
[58:49] line across the chart.
[58:52] Okay,
[58:54] then you have box plot. So we have
[58:56] already seen box plot in the
[58:57] visualizations also. Box plot just uh
[59:00] it's a data distribution graph. It helps
[59:02] you to identify outliers. Okay, it's a
[59:05] data distribution graph which helps you
[59:07] to find outliers. Okay, in box loop plot
[59:10] you will find median values, you'll find
[59:12] quartiles all those things. Okay,
[59:14] outliers are nothing but the unusual
[59:16] value
[59:17] which can be either very high or which
[59:20] can be either very low. Okay, lower than
[59:22] normal.
[59:24] Got it?
[59:29] Uh let me give you a small example of
[59:31] outlier. For example, there are so many
[59:33] customers. Okay. From so many customers,
[59:36] you are generating profit. Okay. But
[59:38] your from your analysis, you found that
[59:41] most of the customers are generating
[59:42] profit from 8 100 to 500. Okay. Most of
[59:45] the customers are generating profit of
[59:48] 100 to 500. But just one customer, you
[59:51] can see he's generating a profit of
[59:53] 8,000. Okay. Then the profit then this
[59:56] particular customer who is generating
[59:58] 8,000 profit is nothing but your
[59:59] outlier. Okay. He's your outlier.
[01:00:03] Got it?
[01:00:10] So uh this is what your analytics plane
[01:00:13] is. So basically anx helps you to move
[01:00:16] your data from the visualization window
[01:00:19] to actually understanding it. Okay. to
[01:00:22] actually understanding it. Clear
[01:00:25] everyone clear.
[01:00:29] Yes or no?
[01:00:51] Okay.
[01:00:55] participants those who are not
[01:00:57] responding please participate in this uh
[01:01:02] interaction so that your doubt gets
[01:01:04] clear. Okay.
[01:01:07] So we have just discussed about the
[01:01:08] theoretical part. Now we'll jump to
[01:01:10] practical also for this. Okay. We'll try
[01:01:13] to see some simple use cases and
[01:01:16] generate this profit sorry generate this
[01:01:18] visualization. Okay.
[01:01:22] So I have already loaded my data set
[01:01:26] uh that is sample super store data set.
[01:01:29] Okay.
[01:01:31] So if you see analytic section it is
[01:01:32] analytic section is divided into uh
[01:01:35] three subsections. One is summarize then
[01:01:38] you have model and then you have custom.
[01:01:41] Okay. Summarize is just used to
[01:01:43] summarize and compare your data. Model
[01:01:46] is used to uh make use of those features
[01:01:49] which can predict which can analyze
[01:01:50] trends and which can group the data and
[01:01:52] custom is used to create your own
[01:01:54] analytical rules and range. Okay, these
[01:01:56] are the three sections. Under each
[01:01:58] section you'll find some other some
[01:02:00] other feature. For example, in summarize
[01:02:01] you find constant line, average line,
[01:02:03] median with quartiles, box plot, totals.
[01:02:05] In model you find these values and in
[01:02:07] custom you get these features. Okay. Now
[01:02:11] let's start with the first one that is
[01:02:13] constant line. Okay, constant line.
[01:02:18] Okay, so what is constant line? What I
[01:02:20] told you? Constant line is nothing but a
[01:02:22] fixed line. Okay, you can assume you can
[01:02:24] keep a target as fixed like that. Okay,
[01:02:27] let me do one thing. I have this order
[01:02:30] date. Okay, I have this order date. I'll
[01:02:34] drop it here
[01:02:36] into the columns. Let me drop. And I
[01:02:39] have sales. Okay, I have uh let me make
[01:02:44] this once.
[01:02:47] Okay,
[01:02:49] let me start it up. So, what you can see
[01:02:53] here, what you can see here, this is
[01:02:56] month
[01:02:57] and this is your sales. Okay, these are
[01:03:00] your sales. Let me enable the labels
[01:03:02] also for this.
[01:03:08] Not this one labels.
[01:03:17] One second. And it's same
[01:03:37] uh number custom.
[01:03:51] Okay.
[01:03:56] So what you can see here I just have
[01:03:58] created one visualization. Okay. Here I
[01:04:01] have month wise sales. Okay. These are
[01:04:03] month by sales. So let's suppose there
[01:04:05] is a company who has got this much sales
[01:04:09] every month. Okay. But I want to keep a
[01:04:12] target for this month. Okay. I want to
[01:04:15] set a target for every minute. For
[01:04:17] example, my target will be uh in between
[01:04:20] this range 115 and 100. Okay, I want to
[01:04:23] set a target of 125K. Okay, I want to
[01:04:26] set a target of 125K. So, I can go here
[01:04:30] to analytics. Okay,
[01:04:33] I can go here to analytics and in this I
[01:04:36] can go to the constant line. I will
[01:04:38] click on this constant line. I hold my
[01:04:40] cursor and drag and drop it on the
[01:04:42] table. Okay. Okay. Now you can set the
[01:04:45] value here. You can set the value in
[01:04:47] this popup. Let's suppose I set it to
[01:04:48] 125,000. Okay. Now you can see from this
[01:04:53] graph from this particular graph we can
[01:04:57] understand in which month we have
[01:04:59] achieved our sales target and which
[01:05:02] month we have not achieved. So from this
[01:05:04] it's clearly uh understandable that uh
[01:05:07] in January and February we were not able
[01:05:09] to achieve our sales target but in the
[01:05:11] rest of every month we have achieved our
[01:05:13] sales targets. Okay, we have achieved
[01:05:15] our sales target.
[01:05:19] You can edit the line also. Suppose I
[01:05:21] want to edit the line this one in this
[01:05:24] thickness bless.
[01:05:27] Okay. Now you can see
[01:05:31] this is my
[01:05:35] you can see this is my
[01:05:35] constant line. Okay, this is my constant
[01:05:38] line. Clear everybody? Yes or no?
[01:05:43] Yes or no?
[01:05:52] Okay, let me go to the new sheet
[01:05:56] again. Uh let me take order date here.
[01:06:02] Let me go to months.
[01:06:05] Okay.
[01:06:08] And let me take profit this time.
[01:06:15] Okay. Let me stop it. Next is average
[01:06:18] line. Next in the analytics is average
[01:06:20] line. Right in analytics. Next is
[01:06:23] average line. So what is average line?
[01:06:25] to do loop average line will create a
[01:06:27] average of all of these values. Okay,
[01:06:30] average line will create average value
[01:06:33] of all of these uh bars. Okay, so how
[01:06:37] what is the average value? You can just
[01:06:38] hold it and you can drag and drop it on
[01:06:40] the table section. Okay, so this is the
[01:06:43] average value. This is the average
[01:06:45] value. Okay, 24,358 is average sales
[01:06:50] average profit. Okay. See in average you
[01:06:53] get one more option pain and sell. Pain
[01:06:55] is same as uh your uh table option only.
[01:06:58] But in cells what happens? You get for
[01:07:01] each specific uh bars. Okay.
[01:07:05] This is 9497.
[01:07:12] So this is the average profits. Okay.
[01:07:15] But this is not the average profit. This
[01:07:18] is the average profit of all uh all the
[01:07:21] funds here. The scheme is
[01:07:55] I hope average line is clear to
[01:07:57] everyone. Average line is clear to
[01:08:00] everyone. Okay,
[01:08:04] it's just the average value of all the
[01:08:06] given values. Okay,
[01:08:08] let me go to the new sheet. We'll
[01:08:10] discuss the third one that is median
[01:08:13] with quartiles. Okay. Again uh let me
[01:08:17] take
[01:08:19] uh
[01:08:21] the same example
[01:08:28] the category. Let me take this time to
[01:08:30] browse
[01:08:32] and I'll take the sales here.
[01:08:36] Okay.
[01:08:38] Let me stop it.
[01:08:50] Better option will be customer name.
[01:08:51] Let's see
[01:08:53] the numbers.
[01:08:58] Okay.
[01:09:00] Now for all the customers assume that I
[01:09:04] want to understand median with quartile
[01:09:07] values. Okay.
[01:09:09] Go to anx. I'll take this and I'll drop
[01:09:10] it on the table.
[01:09:13] You can see this is the median value
[01:09:16] which is around 1474.
[01:09:20] Okay, let me take one thing. Uh let me
[01:09:22] take month only
[01:09:25] again I'll take month.
[01:09:42] And now let me
[01:09:46] now you can see of all the quarters
[01:09:50] uh sales this value is the median value
[01:09:53] 535 016. Okay this is the median value
[01:09:57] and this is the upper quartile and this
[01:09:59] is the lower quartile. Okay. This is the
[01:10:01] median value. This is the upper quartile
[01:10:03] range and this is the lower quartile
[01:10:05] range. Okay.
[01:10:10] So basically this is the highest value
[01:10:13] and this is the lowest quarter. 75
[01:10:16] percentile we can say or 25%. This is
[01:10:18] 25%. Let me show you. Uh if you want to
[01:10:21] edit it, you can edit here also. Okay.
[01:10:25] You can set the line. It's the mother
[01:10:27] color. Okay.
[01:10:32] You can fill above and you can fill
[01:10:33] below this line also. For example, you
[01:10:36] want to fill above. You can see above
[01:10:38] that line the value is getting filled.
[01:10:39] Let me select. Okay.
[01:10:44] Here. Yes or no.
[01:10:48] Naga joti car. Please drop your concern
[01:10:50] in the chat box.
[01:11:01] Okay. This is your median value. Median
[01:11:05] value is the middlemost value. Okay,
[01:11:08] median value is the middlemost value.
[01:11:10] And this is upper quarter and lower
[01:11:11] quartile. Median is almost equal to 50%.
[01:11:15] It's equal to the 50%.
[01:11:19] Okay,
[01:11:20] it's equal to the 50%.
[01:11:24] Let me go to the next one that is box
[01:11:27] plot. So what is box plot? I said what
[01:11:30] is box plot?
[01:11:34] What is box plot?
[01:11:39] Box plot helps you to see the
[01:11:41] distribution and outliers in the data.
[01:11:43] Right? Box plot helps you to see the
[01:11:46] distribution and outliers in the data.
[01:11:48] Right? So for creating box plot you
[01:11:51] should have a huge data. Okay? You
[01:11:53] should have a huge data. For example,
[01:11:56] let me take order date.
[01:11:59] I convert it to
[01:12:02] months. Then I'll take uh subcategory to
[01:12:06] the details and I'll take quantity here.
[01:12:10] Okay. Let me change the marks here to uh
[01:12:14] shapes
[01:12:15] with this one.
[01:12:19] Okay.
[01:12:22] Yep. Now let me go to analytics box plot
[01:12:27] on the cell.
[01:12:29] So in general you can see the box plot
[01:12:33] in general how the box plot is looking.
[01:12:35] You can see
[01:12:39] you can see
[01:12:41] uh for better analysis. Let me do one.
[01:12:43] Let me keep this order date only.
[01:13:03] Now you can see you can see the box.
[01:13:07] So
[01:13:08] basically uh this many quantities are
[01:13:11] there for all the subcategories. Okay.
[01:13:15] This many quantities are there for all
[01:13:17] the subcategories for each year. Okay.
[01:13:21] Any quantity outside this uh upper
[01:13:24] whisker upper hinge and lower hinge is
[01:13:26] your outlier. Okay. Sorry. This upper
[01:13:29] whiskers and lower whiskers is your
[01:13:31] outlier. Okay.
[01:13:34] And this is the median value which is
[01:13:36] coming around. You just hover your curs
[01:13:37] and you can see the values. Upper hinge
[01:13:39] 680, lower hinge 206, median is 326,
[01:13:43] lower whisker 31 and for whisk
[01:13:46] 180. Okay, this is a box plot. So what
[01:13:49] box plot will do? Box plot will help you
[01:13:51] to see the distribution of your data and
[01:13:54] find the outliers. Okay, see the
[01:13:57] distribution of the data and find the
[01:14:00] outliers.
[01:14:02] Okay. So only the requirement for box
[01:14:04] plot is you should have more data. You
[01:14:06] should have more data. Okay. Instead of
[01:14:08] subcategory if I want to try for
[01:14:11] customer names also.
[01:14:14] You can see how big this is. How big
[01:14:18] this is.
[01:14:20] We could have more data.
[01:14:22] Okay. Box plot. In general guys we
[01:14:25] create the box plot using this option
[01:14:27] only. the one we have seen uh in our
[01:14:32] regular sessions.
[01:14:33] Okay, this box plot
[01:14:36] this is just a reference this is for
[01:14:38] analytics purpose means from this box
[01:14:40] plot you'll find out which data is an
[01:14:42] outlier which data is coming in which
[01:14:45] going out like
[01:14:47] okay box plot
[01:14:52] okay if you uh if you try to create uh
[01:14:55] this box plot with small data it won't
[01:14:57] be eligible it won't be enabled for
[01:14:59] example let's suppose I take category
[01:15:02] here Okay to the columns and if I take
[01:15:06] uh profit to the rows okay now if I go
[01:15:10] to the anx section you can see the box
[01:15:12] plot is disabled okay convert this to
[01:15:15] circle shapes
[01:15:18] okay I need to give the detail here okay
[01:15:20] I need to give detail here so let's
[01:15:22] suppose I take category here will be
[01:15:25] okay
[01:15:28] yeah now this is uh enabled now this is
[01:15:31] enabled Okay. Now this is inverse. Now
[01:15:34] if I take the box plot here
[01:15:36] you can see
[01:15:38] you can see the box plot.
[01:15:42] Clear everybody. Yes or no? Please tell
[01:15:44] me.
[01:15:50] Okay.
[01:15:51] In general you use uh this component box
[01:15:54] code rarely we use. Okay.
[01:15:58] We use 10 line we use forecast clusters
[01:16:00] all those things. Okay. We'll learn
[01:16:02] others also. Now let's talk to the next
[01:16:05] one. Uh that is totals. Okay, totals we
[01:16:08] have already seen. Total is just to
[01:16:10] calculate the grand total. Okay, total
[01:16:13] is used to calculate the grand total.
[01:16:14] For example, I have category, I have
[01:16:18] subcategory.
[01:16:19] Okay, and I have sales
[01:16:25] and I will take sales. Okay. So I have
[01:16:29] category, subcategory and sales. Now
[01:16:32] here if I want to get total. So there
[01:16:34] are two ways. Either I can go to
[01:16:35] analysis and we can calculate the total
[01:16:38] or else I can go to analytics tab. And
[01:16:39] here you have totals option. So what you
[01:16:42] want? You want column grand total. Row
[01:16:45] grand total is disabled because uh it's
[01:16:47] not eligible yet. If you have multiple
[01:16:49] columns then that will be also uh
[01:16:51] possible. Okay. For example, uh let's
[01:16:54] suppose if you order date here
[01:16:58] date
[01:17:02] order date
[01:17:03] now you can see we have multiple values
[01:17:06] we have multiple values if now I want to
[01:17:08] take the analytics I'll get column also
[01:17:12] row grand total if I keep grand total
[01:17:14] you get row grand totals okay again if I
[01:17:17] go and see column grand total you can
[01:17:20] see I'm getting column grand total
[01:17:22] Okay. If I want to add uh sub totals,
[01:17:25] you can go to totals and drop it on
[01:17:28] subtitles. So these are your sub totals.
[01:17:31] These are your sub totals.
[01:17:38] Clear.
[01:17:40] Totals is clear.
[01:17:47] Should be clear.
[01:17:54] I need some more response.
[01:18:06] Okay. Now let's go to the model section.
[01:18:10] Okay. We'll go to model section. Before
[01:18:13] talking about model section, I want to
[01:18:14] understand from you guys what do you
[01:18:16] mean by confidence interval?
[01:18:19] What do you mean by confidence interval?
[01:18:21] Please tell me
[01:18:35] what is confidence interval?
[01:18:41] What is confidence interval?
[01:18:51] Uh it's more over a Google definition
[01:18:53] but uh but correct but it's correct.
[01:18:57] Okay. See guys confidence interval is
[01:19:01] nothing but it's a range of values
[01:19:04] between which your actual value lies.
[01:19:07] Okay. Confidence interval is a range of
[01:19:10] values between which your actual value
[01:19:13] is going to lie.
[01:19:15] Okay, you understood what is confidence
[01:19:17] interval? It's a range of values between
[01:19:20] which your actual value will lie. Okay,
[01:19:24] let me give you example. For example, uh
[01:19:27] let's suppose you want to uh know what
[01:19:31] is the average height of all the
[01:19:32] students in your school. Okay, school or
[01:19:35] college what it whatever it be you want
[01:19:37] to understand what is the average height
[01:19:40] of all the students which are there in
[01:19:42] your college. Okay. So let's suppose
[01:19:45] there are 2,000 students in your
[01:19:46] college. Okay. Let's suppose there are
[01:19:48] 2,000 students in your college. So
[01:19:50] instead of measuring all the 2,000
[01:19:52] students, what you can do? You can just
[01:19:54] measure 100 students. Okay. You can
[01:19:57] measure the height of 100 students and
[01:19:59] from those 100 students you got that the
[01:20:03] average height of those 100 student is
[01:20:05] 160 m 160 cm. Okay. You got that after
[01:20:09] measuring 100 students height you got
[01:20:11] that the average height of all those 100
[01:20:13] student is 160 cm. Okay. So this is just
[01:20:17] one sample that you have taken right.
[01:20:19] This is just one sample that you have
[01:20:21] taken. It doesn't mean that all the
[01:20:23] students has to be exactly 1 c 160 cm
[01:20:27] right. So your confidence interval would
[01:20:31] be like
[01:20:33] in between uh this 160 range. Okay. It
[01:20:36] might be from 157 cm to 163 or 150 to
[01:20:41] 165 like that. Okay. So this range is
[01:20:43] nothing but your confidence interval
[01:20:46] between which your actual value is going
[01:20:49] to lie. Okay. So
[01:20:53] you can say that uh you are 95%
[01:20:56] confident that uh the average height of
[01:20:58] all the students will be between 157 to
[01:21:02] 163. Okay. Clear?
[01:21:06] clear what is confidence interval?
[01:21:10] What is confidence interval? Okay. So
[01:21:14] here confidence interval with confidence
[01:21:16] interval uh we have one thing called
[01:21:19] average with 95% CI. CI is nothing but
[01:21:23] the short form of confidence interval.
[01:21:25] Okay. CI is nothing but uh full for
[01:21:28] short form of confidence interval. Okay.
[01:21:32] So average with 95% confidence interval
[01:21:36] it means that this function will show
[01:21:38] you the average with an expected range.
[01:21:41] Okay, it will give you an average with
[01:21:43] an expected range. Okay, average with an
[01:21:48] expected range. For example, let me take
[01:21:50] the same example of uh
[01:21:54] uh order date.
[01:21:56] Okay, I'll convert this to months and
[01:22:00] we'll take
[01:22:02] profit this time.
[01:22:05] Okay. Uh I think so profit will not be a
[01:22:07] good option. Let us try. Let me stop it.
[01:22:11] Okay. So this is one of the graph where
[01:22:14] you can see uh we have profit by month.
[01:22:17] Okay. Month wise profit we have. Now in
[01:22:20] this I want to uh create average with
[01:22:23] 95% confidence interval. So I'll say
[01:22:26] I'll take this and I will drop it on the
[01:22:28] table. Okay. Now from this you can
[01:22:32] understand that this is the average
[01:22:34] profit and this is the confidence
[01:22:38] interval between which your average
[01:23:04] It's fast.
[01:23:24] Okay. Is my voice audible now?
[01:23:31] All right. Now, if you see here, I have
[01:23:34] set the content intervals in 95. You can
[01:23:36] change it to 99
[01:23:38] but 95 consider as an ideal value. Okay.
[01:23:41] 50 80 90 anything. If it is 50, this
[01:23:45] band will be more big. Okay. If it is
[01:23:48] 99.9 this is going
[01:23:52] to be so small
[01:23:56] uh
[01:23:58] 3% should be big right
[01:24:01] that uh it should be
[01:24:05] very big
[01:24:08] okay no worries I'll check this out okay
[01:24:11] so ideally uh we should have a 95%
[01:24:14] confidence interval between which
[01:24:17] we have to give the function value.
[01:24:19] Okay, for example, in this uh the
[01:24:22] average value is 24,000 something and
[01:24:24] from that we are getting this values.
[01:24:30] Okay, label let's give values. So you
[01:24:32] can see
[01:24:43] let's see.
[01:25:06] Just keep you can
[01:25:10] so this value is your average value and
[01:25:13] this is value range and the lower range
[01:25:16] 95% confidence. Okay.
[01:25:20] This is 95% confidence intera
[01:25:45] with 25%.
[01:25:47] Let's suppose I have region I have
[01:25:50] sales. Okay, this is my region my sales.
[01:25:54] So for this particular thing I want to
[01:25:56] find out median with% profit.
[01:26:01] So this value is the med of sales
[01:26:07] and
[01:26:09] from that this is the
[01:26:13] range of of range and this is the lower.
[01:26:16] Okay.
[01:26:41] Singap
[01:26:53] the upper range and the lower range
[01:26:55] between these average values will be
[01:26:56] false. Similarly, median value will have
[01:26:58] the upper range and the lower range
[01:27:00] between which it has to fall. Okay.
[01:27:06] Clear.
[01:27:09] Yes or no?
[01:27:24] that is here. We'll go to next that is
[01:27:27] line.
[01:27:29] We go to the next one that is
[01:27:32] what is
[01:27:35] trend
[01:27:39] line is going to show you the direction
[01:27:41] of the grow
[01:27:49] to have
[01:27:51] okay let's suppose I have
[01:27:57] sales
[01:28:02] It makes sense.
[01:28:22] Excuse me.
[01:28:26] Please
[01:28:32] model
[01:28:54] it's showing me
[01:28:56] increasing.
[01:28:58] Okay, this means it's growing. It's
[01:29:01] growing
[01:29:03] declining. Maybe it was for a particular
[01:29:05] month. So, for that, but here uh this is
[01:29:08] how you create the trend line. Okay.
[01:29:11] Now, uh not only this, you can create
[01:29:13] another form of line also. You can
[01:29:16] create exponential line like this. Okay.
[01:29:18] You can create line if you want like
[01:29:21] this. Okay. Generally we go with
[01:29:25] the linear line option. Okay. So from
[01:29:29] this particular line we estimate that
[01:29:32] whether my sales is increasing or
[01:29:34] decreasing. Okay. From the trend line we
[01:29:37] estimate that whether my trend whether
[01:29:40] my sales is increasing or decreasing.
[01:29:56] So it's going upward direction. It's
[01:29:59] going in upward direction.
[01:30:03] Uh guys, is my voice clear?
[01:30:09] Is there any disturbance in my voice? Is
[01:30:11] it not clear? Give me thumbs up or
[01:30:13] thumbs down.
[01:30:16] Please confirm
[01:30:22] it's clear.
[01:30:26] So I hope you understood about the trend
[01:30:28] line. Basically trend line will tell us
[01:30:31] whether my particular uh category is
[01:30:35] increasing or declining. Okay, whether
[01:30:38] it is growing or declining. Okay,
[01:30:42] I hope you got the point.
[01:30:46] I hope you got the point.
[01:31:00] Now let's go to the next one that is
[01:31:02] forecast.
[01:31:05] Let's go to the next one that is
[01:31:06] forecast.
[01:31:08] Okay.
[01:31:10] So I'll take the same option once again.
[01:31:12] I'll take the order date here. What does
[01:31:14] forecast will do? Focus will predict
[01:31:17] your future values. Okay. Forecast will
[01:31:19] predict the future values. I'll take the
[01:31:21] order date again. I will take this month
[01:31:23] from here and I will take the sales.
[01:31:27] Okay. So we can see the sales month
[01:31:30] sales. Okay, we can see the month sales.
[01:31:33] Now I want to forecast my sales for the
[01:31:35] next coming months or years. Okay. So go
[01:31:39] to analytics. I go to forecast.
[01:31:43] Now you can see the one which you can
[01:31:46] see in the dark color is your active
[01:31:48] values and the one which you can see
[01:31:50] light blue color is your estimated.
[01:31:53] Okay, this is your estimated sales. This
[01:31:57] is your estimated
[01:31:59] uh right click here.
[01:32:15] This is if you want to change the
[01:32:17] colors, you can change the colors also.
[01:32:20] You can change the colors also.
[01:32:26] And this is if you're not in
[01:32:35] you go to edit options you can see to
[01:32:39] which month you want to forecast. Okay.
[01:32:42] For example it is uh if you want to
[01:32:44] forecast for exactly 2 years you can see
[01:32:47] it's forecasting for 2 years. Take 3
[01:32:50] years 5 years you can predict. Okay.
[01:32:55] before.
[01:32:58] How do you want to aggregate the data?
[01:33:00] Monthly, yearly, quarterly, you can do
[01:33:03] it. Okay. Yearly, this is for no
[01:33:06] purpose. Uh let's assume matter. Okay.
[01:33:12] Ignore the last one period. It's
[01:33:14] ignoring. Okay.
[01:33:20] How many months you want to know? You
[01:33:22] can set it from here also. Okay. What
[01:33:24] should be the forecasting model? You can
[01:33:27] select automatic without seasonality
[01:33:31] or custom.
[01:33:33] We keep automatic. Okay. So, you can see
[01:33:37] some graded lines uh some uh some line
[01:33:41] here, right? Some dark some light color
[01:33:43] line. Okay? That is nothing but the
[01:33:45] prediction interval. We have set it to
[01:33:48] 95%. If you keep it off, you won't get
[01:33:50] it. You won't get it. This is the
[01:33:54] prediction interval means it might fall
[01:33:56] between this particular interval. Okay.
[01:33:59] It might this value might fall
[01:34:02] this. Okay.
[01:34:05] Clear.
[01:34:06] Okay.
[01:34:09] Clear. Yes or no?
[01:34:27] Okay, if that is clear, let's go to the
[01:34:29] next one uh to see the
[01:34:32] uh cluster.
[01:34:34] Okay, cluster. So, as you have already
[01:34:36] mentioned, what is cluster? Cluster
[01:34:38] basically allows you to form group of
[01:34:40] similar data.
[01:34:42] Okay, cluster allows you to form a group
[01:34:45] of similar data points. Okay, how do we
[01:34:47] create a cluster? Let's suppose I have
[01:34:53] profit. Okay. Now let me add customer
[01:34:57] names into the details.
[01:35:02] Okay. You can see lot of customers are
[01:35:05] now let's suppose I want to create a
[01:35:07] cluster of this
[01:35:09] customers. Okay. I want to segregate
[01:35:12] them into different groups. So tap 2
[01:35:15] will automatically create the groups for
[01:35:17] you. Okay, you need to actually just
[01:35:19] pass how many groups you want to create.
[01:35:22] By default, it can create groups. For
[01:35:24] example, if you see at a cluster and pop
[01:35:28] it on here, you can see by default it is
[01:35:30] creating two groups. One with blue color
[01:35:33] and one with
[01:35:36] blue is cluster one, orange is cluster
[01:35:38] two. If suppose you want to create just
[01:35:41] pass here and hit enter. See clusters
[01:35:45] will get created. Okay. Multiply four
[01:35:49] gets created.
[01:35:52] 10
[01:35:54] 10 clusters are getting created. Okay.
[01:35:58] 10 clusters not
[01:36:02] clear.
[01:36:08] What the default number of clusters have
[01:36:10] created?
[01:36:20] By default, how many clusters Tableau
[01:36:22] create
[01:36:31] participants in the YouTube?
[01:36:43] Uh YouTube students any issues?
[01:36:52] YouTube students any issues
[01:37:02] guys for better experience try to attend
[01:37:04] this session only through laptops.
[01:37:07] Okay.
[01:37:14] Yes, two. The default yours is two.
[01:37:19] Okay.
[01:37:27] Uh Vish, uh you're talking about live
[01:37:30] session. You can just join the YouTube
[01:37:32] video. Okay.
[01:37:35] Uh one second. Let me share the YouTube.
[01:37:46] enjoy this. This was shared in the
[01:37:49] beginning of the session.
[01:38:05] Guys, I hope I'm clearly able to mention
[01:38:19] Okay.
[01:38:22] So, that was all about the models.
[01:38:27] Okay. So, that was all about the models.
[01:38:27] That was all about the models. Okay.
[01:38:29] That was all about the models.
[01:38:35] All right.
[01:38:47] Now let's jump to the next one.
[01:38:51] Let's jump to the next one. Uh which is
[01:39:04] sharp. I think you are not able to
[01:39:37] Let's go to the next one that is
[01:39:39] summarize section.
[01:39:41] In summarize section also we have
[01:39:43] certain uh
[01:39:46] option. First is the reference line.
[01:39:48] First is reference.
[01:39:50] So I think so uh this option I have
[01:39:53] already changing using parameters also.
[01:39:56] Let's just see one of the
[01:40:00] for example we have
[01:40:04] okay
[01:40:07] or else let's keep and see
[01:40:19] now for this uh original sales I want to
[01:40:22] set a reference a reference
[01:40:26] Okay. So I'll take the reference line
[01:40:28] and I drop it on the C section. Okay.
[01:40:31] Now what you want?
[01:40:34] So you can keep the reference as a sum
[01:40:37] as a constant value maximum but average
[01:40:40] med. Okay. For instance I'll keep the
[01:40:46] average I keep it average value as a
[01:40:50] reference reference point. Okay. average
[01:40:53] value at a reference point.
[01:40:56] Okay. So values
[01:41:01] labels you can set values.
[01:41:05] Okay. So you'll get values instead of
[01:41:09] itation.
[01:41:13] Let's select val
[01:41:18] custom option gives you different
[01:41:20] options how you want to see
[01:41:23] differentation if you want to give name
[01:41:27] the name like that. Okay. But for now we
[01:41:30] will go with option.
[01:41:34] Again this line can be colored with some
[01:41:36] other colored
[01:41:40] line.
[01:41:42] Okay.
[01:41:43] And let's suppose you want to highlight
[01:41:46] all the data points which are falling
[01:41:49] below this particular reference lines.
[01:41:51] You can set them with this. Let's go set
[01:41:54] them with red color. Okay. And the
[01:41:58] values above this line. Let me set it
[01:42:00] with green color. Okay. Since these are
[01:42:04] the more positive side.
[01:42:10] Okay.
[01:42:12] This is my reference line. I have filled
[01:42:15] the area
[01:42:18] above the reference line. Okay.
[01:42:24] Understand
[01:42:26] Okay. Understand
[01:42:26] how the organization is performing with
[01:42:29] respect to any specific target or
[01:42:31] reference. Okay. Clear.
[01:42:44] Yes.
[01:42:56] Okay. Let me go to the next one that is
[01:42:58] reference band. So what is band?
[01:43:01] Reference band just highlights a range.
[01:43:04] Okay. It will highlight one range. Okay.
[01:43:07] For example, uh let's suppose we have
[01:43:12] and we have
[01:43:21] you have category and you have let's
[01:43:24] assume that I'll keep a profit uh
[01:43:28] between some range. Okay. I am expecting
[01:43:32] as a business owner I'm expecting my
[01:43:34] profit uh accepted a range of my profits
[01:43:37] from certain values. Okay. Let's suppose
[01:43:43] one
[01:43:52] second.
[01:43:59] Uh, I'm audible now.
[01:44:03] Yes, sir.
[01:44:14] I'm not audible everyone
[01:44:16] YouTube participants.
[01:44:22] Okay. So what I was saying
[01:44:24] reference band
[01:44:27] helps us to highlight a particular
[01:44:29] range. Okay, reference band will help us
[01:44:31] to highlight a particular range. Now how
[01:44:34] are you going to highlight that range?
[01:44:35] You need to have some benchmark value
[01:44:38] across which you are going to highlight
[01:44:39] it. Right? So that is what we'll give
[01:44:41] it. Okay. So there are multiple of ways.
[01:44:44] Let us see reference line will take you
[01:44:47] to the table. Okay. Now you can see here
[01:44:50] uh sorry reference band reference band
[01:44:54] into the table. Now here you have
[01:44:58] multiple options. Okay where you want
[01:45:00] the value to start from. Okay the
[01:45:04] reference point. This is the value which
[01:45:06] is getting started and this is the value
[01:45:07] till it goes. Okay. So you can modify
[01:45:10] this value not starting from 20th. We
[01:45:13] can change it. For example, if you see
[01:45:15] here, it's starting from minimum value
[01:45:17] to maximum value. Okay, minimum value to
[01:45:20] maximum value. Minimum value means the
[01:45:21] lowest most value in my graph. So the
[01:45:24] maximum value means highest value in my
[01:45:27] will change it to constant. Okay. Now
[01:45:30] what value I want to start it? I want to
[01:45:32] start it from let's suppose 100.
[01:45:37] Okay.
[01:45:38] Okay.
[01:45:41] And when it goes it should go to again
[01:45:45] constant let's make it uh
[01:45:51] one just
[01:45:57] this is the range which I want which
[01:45:59] color you want to fill let's go
[01:46:02] silver.
[01:46:21] Now you get the
[01:46:25] reference. Okay. So this kind of
[01:46:28] reference plan we will set it so that we
[01:46:32] can understand this is the acceptable
[01:46:34] profit range. I'm expecting the profit
[01:46:36] for all my categories to be from 100 to
[01:46:39] one. Okay. Anything falling outside will
[01:46:44] uh it will take my attention right. It
[01:46:46] will help me to understand this is the
[01:46:48] improvement area I have of
[01:46:52] this shaded area shows the acceptable
[01:46:55] profit range.
[01:46:59] Clear
[01:47:08] that is your reference plan. Okay. Next
[01:47:11] is uh distribution map. Next is
[01:47:14] distribution map. What is distribution
[01:47:17] map? As I mentioned earlier also it just
[01:47:20] shows you the spread of the data around
[01:47:23] your average value. Okay. distribution
[01:47:25] map shows the spread of data around in
[01:47:28] arrangement.
[01:47:32] Okay,
[01:47:35] one second. Let me see if I can have
[01:47:38] some more better example to you.
[01:47:52] Let me take
[01:48:00] subcategory
[01:48:02] and take
[01:48:17] distribution.
[01:48:19] So we select cross. If you select paint,
[01:48:23] it will be different for each B. You can
[01:48:24] see we have three B. One is for
[01:48:26] furniture, one is office device and one
[01:48:28] is for technology. Okay.
[01:48:32] Let me go distribution.
[01:48:36] Okay. Now as I mentioned your
[01:48:39] distribution is it shows through the
[01:48:41] spread of uh values cross your average
[01:48:46] spread of the values across your average
[01:48:48] okay now it's up to you what you want to
[01:48:50] set suppose you want to set 60 80% okay
[01:48:55] you want to set 70 90%
[01:48:59] okay you can set it what you want to see
[01:49:03] percent of profit
[01:49:06] total which Total value. This is
[01:49:08] constant.
[01:49:09] This is average. Okay. Average almost
[01:49:13] suits here. Okay.
[01:49:16] This is average most value. Standard
[01:49:18] deviation also you can set minus one to
[01:49:20] one. Whereas mostly we go with
[01:49:23] percentages form and we keep the target
[01:49:25] of 68. Okay. It means my data should
[01:49:29] show me the distribution of other values
[01:49:32] between 60 to 80% of my uh average
[01:49:37] value. Okay. 60 to 80.
[01:49:41] Okay.
[01:49:42] Color you want to change. Let the color
[01:49:44] be green color.
[01:49:47] Okay. And any value you want to fill
[01:49:50] above or below you can.
[01:49:53] Okay.
[01:49:58] Let me close this. Now you can see this
[01:50:00] is the distribution.
[01:50:03] This is the distribution.
[01:50:07] Okay, that is the
[01:50:20] data. Yes, my name is
[01:50:26] sales
[01:50:37] now.
[01:50:46] So you can select uh 60 to 80% of
[01:50:49] activation.
[01:50:55] change it.
[01:51:04] Okay,
[01:51:09] this
[01:51:11] uh
[01:51:13] let me change the seat.
[01:51:35] If you want to see parts also we can
[01:51:37] give you
[01:51:40] the variable.
[01:51:44] Okay. So what it is doing it is
[01:51:45] measuring the variability in the
[01:51:47] customers.
[01:51:48] Okay.
[01:51:50] So what distribution bank does it just
[01:51:53] gives you the spread of data around the
[01:51:55] average value. Okay.
[01:51:58] Uh
[01:51:59] let me this
[01:52:02] region
[01:52:04] says
[01:52:06] now let us try
[01:52:15] 80% of the value
[01:52:19] 60% of the
[01:52:22] okay this is your average.
[01:52:29] Okay.
[01:52:32] Yes or no?
[01:52:43] Box plot is visible because uh of the
[01:52:46] not data. Okay. But it works in the same
[01:52:49] way. Okay. So here also you get uh in
[01:52:52] list you got only single option to drag
[01:52:54] and drop it right but here this box
[01:52:56] allows you to create table
[01:53:00] or
[01:53:02] tell like
[01:53:08] it but usually box we create in
[01:53:11] visualization
[01:53:16] are we clear with visualics Say
[01:53:23] yes or no.
[01:53:35] That's all about
[01:53:39] that's all about the visual.
[01:54:06] the topic.
[01:54:14] Okay.
[01:54:19] Sales
[01:54:23] by category.
[01:54:31] Eating
[01:54:44] Okay.
[01:55:06] So why boxard mostly box used to
[01:55:09] Okay. So why boxard mostly box used to
[01:55:09] understand the outliers and the
[01:55:12] distribution of the
[01:55:17] Okay.
[01:55:27] All right.
[01:55:29] I hope our visual art experience still
[01:55:51] So
[01:56:05] we had
[01:56:08] seen understand
[01:56:12] we are seeing our face.
[01:56:15] We are seeing friends
[01:56:36] from all
[01:56:39] Okay.
[01:57:04] What was next?
[01:57:08] Cluster
[01:57:11] cluster
[01:57:12] forecast
[01:57:23] are
[01:57:25] these are the
[01:57:40] Vision.
[01:57:48] Can I guess in terms please?
[01:57:54] Is that clear?
[01:57:56] Only two people. What about rest? Why
[01:57:59] are you not responding?
[01:58:07] Vijr you have joined session late.
[01:58:11] Those who are present in the session
[01:58:12] they have already received it.
[01:58:21] Okay.
[01:58:28] All right. Uh let's move to the next
[01:58:30] one.
[01:58:31] I want to discuss one more thing. Okay.
[01:58:34] Uh
[01:58:36] in this vocabul interface we have
[01:58:38] something called folders. Okay. Tell me
[01:58:41] guys why do we create folders?
[01:58:44] Why do we create folders?
[01:59:14] Yes. Keep collection of single files.
[01:59:18] Keep a collection of single files.
[01:59:20] Looking into the data set, can you guys
[01:59:22] tell me what are the similar files for
[01:59:24] which we can create folders?
[01:59:28] Looking into the data set, can you tell
[01:59:30] me what are the similar files for which
[01:59:32] we can create folders?
[01:59:48] Can you guys tell me to the folders what
[01:59:51] are the files which we can create?
[01:59:56] See guys, it's very simple. U you can
[02:00:00] see that category, subcategory, product
[02:00:02] names, we can keep them into one folder,
[02:00:05] right? Yes or no? See in your you have
[02:00:09] different document downloads, videos
[02:00:14] or images the folder size. So here also
[02:00:19] we can do that. Okay. So we can keep the
[02:00:22] similar items in one. Okay. To create
[02:00:25] folder you can go to create
[02:00:30] group by folder. And now you can see
[02:00:36] your folders option create folder. Okay.
[02:00:40] Just in our product
[02:00:50] hit enter. Now in this product category
[02:00:53] I also want to store my subcategory.
[02:00:56] Okay. I will drag and drop. I want to
[02:00:58] store my product name.
[02:01:01] Okay.
[02:01:04] product name. Okay,
[02:01:07] you can see how the folders have been
[02:01:10] created. Okay, how the folder has been
[02:01:13] created. We just stoing all the similar
[02:01:16] elements into one your folder.
[02:01:19] Okay,
[02:01:22] that's folders.
[02:01:26] Folders.
[02:01:28] Can you tell me which is the next one?
[02:01:31] Next items which we can keep in folders
[02:01:52] guys
[02:01:57] what is the next one which we can keep
[02:01:58] in folders.
[02:02:02] We can create a folder for it. We have
[02:02:05] seen location right? Yes or no?
[02:02:08] Location.
[02:02:11] Location also we have seen right. We can
[02:02:13] create a folder for region, city,
[02:02:15] country,
[02:02:17] state etc. What?
[02:02:21] Okay. What is the difference between
[02:02:24] folder and hierarchy? Can somebody tell
[02:02:26] me what will be the difference between
[02:02:27] folder and hierarchy? We have seen
[02:02:29] hierarchy before, right? We're looking
[02:02:32] into the folders now. What is the
[02:02:33] difference between hierarchy and
[02:02:34] folders? Can you tell me?
[02:02:48] What is the difference between folder
[02:02:50] and
[02:02:53] see guys? Folder is just a kind of a uh
[02:02:56] storage where you can keep the similar
[02:02:58] items. Okay, hierarchy is used to drill
[02:03:01] down your values. Okay, you cannot pass
[02:03:04] uh you cannot pass
[02:03:08] folders into this particular field rows
[02:03:11] or columns. Okay, you cannot pass it
[02:03:14] will not take it. Okay, folder is just a
[02:03:17] directory where you storing the similar
[02:03:19] elements. Okay, what hierarchy can be
[02:03:22] passed into the graph to fill down the
[02:03:24] values? If you want to remove the
[02:03:26] folders, right click the folder and
[02:03:27] click on remove folder. Folder will get
[02:03:29] removed. Okay, folder will get removed.
[02:03:34] All right.
[02:03:37] Next is groups. Next is groups. For
[02:03:40] example, uh what is groups? Groups is
[02:03:44] basically is used to keep similar
[02:03:46] elements together. Okay, keep similar
[02:03:49] elements together. For example, if you
[02:03:50] take products,
[02:03:53] there is a huge list of products, right?
[02:03:56] There is a huge list of products. You
[02:03:57] can see for Xerox, we have multiple
[02:04:00] products. Yes or no? For Xerox, we have
[02:04:03] multiple products. Correct? Similarly,
[02:04:06] for Zebra, we have multiple products
[02:04:07] which are falls in the same category.
[02:04:10] Right? So instead of analyzer
[02:04:15] okay how to create group right click on
[02:04:17] product claim I'll go to create and okay
[02:04:21] now you can see I'm getting different
[02:04:23] groups I'm getting the entire list where
[02:04:26] I can make the groups for example I'll
[02:04:27] make of stocks okay see so one second
[02:04:33] how will I create the group I'll select
[02:04:35] all the zerox
[02:04:38] okay I'll select all the zerox
[02:04:46] blocks and then speed.
[02:04:53] Okay.
[02:04:56] Similarly, if I'll go here in the top uh
[02:05:00] I'll make one more
[02:05:05] let
[02:05:07] all
[02:05:14] please find this and click on
[02:05:22] okay. Now you can see here uh we have
[02:05:27] created
[02:05:29] okay uh let me take
[02:05:32] let me clean this let me take names
[02:05:37] now you can see here if I go bottom
[02:05:40] you'll find zerox
[02:05:44] okay you find the loop similarly you
[02:05:47] find for we created
[02:05:51] that
[02:05:54] group.
[02:05:56] Okay. So, so why we do this? Uh why we
[02:06:00] create groups? So that we can focus on
[02:06:04] the particular groups at a overview
[02:06:06] level. Okay. We can focus on the
[02:06:08] particular group at a overview level.
[02:06:10] Okay. If you want to analyze you have
[02:06:13] the option, you have one more option of
[02:06:15] product names. You can use that. But if
[02:06:17] you want to see from over level you can
[02:06:20] form the rooms and on that you can
[02:06:22] analyze
[02:06:29] here
[02:06:32] and folders de
[02:06:36] people in the YouTube
[02:06:45] comes
[02:07:03] I believe that is clear to everyone. Uh
[02:07:05] now I want to show you how to create uh
[02:07:14] this first session will be on dboard
[02:07:15] story and publishing. So before that you
[02:07:19] need to understand how to create KPIs.
[02:07:22] Okay. How to create KPIs? KPIs are
[02:07:24] nothing but key performance indicator.
[02:07:26] Okay. So from your data set can you tell
[02:07:29] me what are the parameters we can keep
[02:07:31] as a key performance indicator.
[02:07:34] From my data set can you tell me what
[02:07:36] should be the KPIs?
[02:07:39] What should be the KPIs which I can keep
[02:07:41] in my dashboard?
[02:07:43] What should be the KPIs which I can keep
[02:07:45] in my dashboard?
[02:07:50] looking into the data.
[02:08:14] Please tell me
[02:08:30] I think I have deleted some of file.
[02:08:41] Please tell me what should be the thing
[02:08:45] which I should keep in KPIs.
[02:08:54] I need some response
[02:09:03] guys.
[02:09:09] I believe I have deleted some temporary
[02:09:11] because of that
[02:09:15] liquid type.
[02:09:59] not getting any response guys you won't
[02:10:01] respond me how I can help you
[02:10:06] I'm asking you what should be the values
[02:10:08] which I can keep in my dashboard
[02:10:13] the KPIs which should I keep in my
[02:10:15] platform which
[02:10:20] didn't know that.
[02:10:44] This is getting some
[02:10:47] because of which it's not generating.
[02:12:01] Yes, your project session will start
[02:12:03] from earlier tomorrow onwards. Okay. Uh
[02:12:07] I am asking you guys one question. What
[02:12:09] is API and what KPI should I keep on my
[02:12:12] dashboard? See if you see your sample
[02:12:15] dashboard image.
[02:12:19] They are your three performance
[02:12:21] indicator right in this dashboard you
[02:12:23] can see some values here right this is
[02:12:24] the values this is the value so looking
[02:12:27] into the data set what all values I can
[02:12:30] keep in my
[02:12:33] work
[02:12:35] which values I can keep in
[02:12:42] which I can keep in my national
[02:13:23] This was a temporary file because of
[02:13:25] which I guess.
[02:13:30] Okay.
[02:14:04] Can I get some response from you guys?
[02:14:07] Meanwhile, I'm fixing.
[02:14:12] What is KPI and what KPIs we should keep
[02:14:15] in dashboard? Please tell me
[02:14:21] what is KPI and which KPIs I should keep
[02:14:24] in the dashboard.
[02:14:30] See guys uh understand one thing in Sab
[02:14:34] you have these things right.
[02:14:47] your data set you have sales of
[02:14:49] reactions right those case
[02:14:57] I know I'm asking you what things we can
[02:15:00] keep we can keep in our
[02:15:05] projects
[02:15:33] Let me go to our paint.
[02:15:37] Okay.
[02:15:39] In this page,
[02:15:41] I'll show I'll explain. Okay, let's
[02:15:43] suppose this is my interface of my
[02:15:45] dashboard. Okay, in my dashboard, I'll
[02:15:48] keep uh some visualization here. I'll
[02:15:50] keep some visualization here. Okay, I'll
[02:15:53] give some title here. Right, I'll keep
[02:15:55] some filters here. I'll give some more
[02:15:57] visualizations here. Here I want to give
[02:16:00] KPIs. Okay, I want to give KPIs key
[02:16:03] performance indicator. So what values I
[02:16:06] can take from my data set and I can keep
[02:16:08] it there. That is what I'm asking you
[02:16:10] guys. So if you careully see we have
[02:16:12] this measures right in my dashboard I
[02:16:16] can showcase total profit can showcase
[02:16:19] total quantity
[02:16:22] total revenue total sales. Yes or no? I
[02:16:25] can showcase this right.
[02:16:28] Are you able to follow me? Yes or no?
[02:16:32] Can I keep that? That is what I'm asking
[02:16:35] you. Now your task is to think logically
[02:16:38] and tell me in tomorrow's session what
[02:16:40] KPIs we should keep in the dashboard.
[02:16:43] Okay, tomorrow is going to be the last
[02:16:44] training session. So today we just have
[02:16:47] seen uh the visual analytics. Tomorrow
[02:16:51] we'll see how to create a dashboard, how
[02:16:53] to create a story and how to publish it.
[02:16:55] Okay, these three things will be the
[02:16:58] target for tomorrow. All right. So with
[02:17:00] that all for today. So if you guys have
[02:17:03] any queries you can ask. If no worries
[02:17:07] you can
[02:17:13] somebody holds the
[02:17:20] YouTube participants don't worry
[02:17:22] everything will be shar
[02:17:28] link is also shar session l
[02:17:33] then there's no point anyway if you're
[02:17:35] marking attendance link also like
[02:17:37] attending session from office.
[02:17:50] See Mohamad there is no nothing such
[02:17:52] kind of homework kind of thing. There is
[02:17:54] a project development. Okay, understand
[02:17:56] there is a project development. Okay,
[02:17:58] means whatever you have learned so far
[02:18:00] on that you will be working on a
[02:18:02] particular project. Okay, titles we will
[02:18:05] be giving you. You have to select one
[02:18:07] particular title and work on that. Okay,
[02:18:10] that is how you go ahead in this.
[02:18:14] Now I believe it's clear. What about
[02:18:16] others? Any queries students in the
[02:18:19] YouTube? Any queries do you have?
[02:18:34] Uh Kuman have you joined the session
[02:18:36] just now because we have shared
[02:18:38] attendance link multiple times.
[02:18:41] We have shared the attendance link
[02:18:42] multiple times.
[02:18:45] Are you joining session just for the
[02:18:46] sake of attendance
[02:18:55] please drop your email.
[02:18:58] Mohammad Ali I'm telling you you have to
[02:19:01] work on one project okay
[02:19:05] you have to work on one project title of
[02:19:08] the project will be given by us to you
[02:19:11] need to work on that
[02:19:14] sessions are getting recorded we cannot
[02:19:15] speak in Hindi in this session
[02:19:21] okay understanding
[02:19:24] session will get completed by tomorrow
[02:19:26] means tomorrow we'll complete our 30
[02:19:29] session after that we'll be working on
[02:19:32] the projects projects using whatever you
[02:19:35] have learned
[02:19:38] okay
[02:19:48] can you raise your uh what is the this
[02:19:51] time there's a issue in the telegram
[02:19:53] going on from the back end side itself
[02:19:55] so can you please post concern here. I
[02:19:58] will share it with the concern person.
[02:20:10] Attendance link is not working
[02:20:12] guys. Is it so?
[02:20:19] Is it so attendance link not working?
[02:20:39] It's working guys.
[02:20:42] Attendance link is working. If you are
[02:20:44] trying to phone then we are not
[02:20:46] responsible.
[02:20:49] Okay.
[02:21:02] I check myself. It's working.
[02:21:07] If you click on this link, it will take
[02:21:09] you to this page.
[02:21:11] It's working. Here the response.
[02:21:15] Okay.
[02:21:26] Participants, those whoever have
[02:21:28] registered with us have received the
[02:21:29] login credentials. Okay,
[02:21:34] you would have received the login
[02:21:35] credentials in your registered email ID.
[02:21:36] Please cross check in your registered
[02:21:38] email ID only. Okay, if you are checking
[02:21:41] in some other email id then we cannot
[02:21:44] help you. Please remember what email id
[02:21:47] you have used while registration. check
[02:21:49] in that of okay.
[02:22:03] Yes, Suman Sharma. It's okay. It's okay.
[02:22:21] Please drop a proper email.
[02:22:24] Please drop a proper email.
[02:22:51] Anybody
[02:22:56] else need the concern?
[02:23:04] But alam ra just understand one thing.
[02:23:07] How come I'll know what email ID you
[02:23:09] have used for registration. Right.
[02:23:12] You have to drop the email to the team
[02:23:15] to get your query resolved. Right?
[02:23:18] Or draft the email here. I'll send it to
[02:23:20] them.
[02:23:23] Okay.
[02:23:26] You have concern which I can send it to.
[02:23:46] Okay. Let's do one thing. I'll get a
[02:23:48] Google phone. Okay, I'll create a Google
[02:23:50] form here. Uh
[02:23:54] let me quickly create a Google form to
[02:23:56] understand what email ID and whether you
[02:23:59] registered with us or not. Okay, I will
[02:24:01] create it.
[02:24:20] uh take this matter seriously. Okay. If
[02:24:23] you wor details of other persons in the
[02:24:25] sheet, we will not be able to help
[02:24:27] anyone. Okay. This is some initiative
[02:24:29] that I'm taking.
[02:24:43] Let me share my
[02:24:49] I hope my screen is Google right
[02:24:54] if you have got serious with market.
[02:24:58] Okay.
[02:25:00] I think I will share it with my team.
[02:25:09] Sorry.
[02:25:13] Okay. really start to indicate
[02:25:17] college
[02:25:18] issue.
[02:25:20] Okay,
[02:25:24] let me share
[02:25:34] access.
[02:25:40] I'll share it with you guys. Okay,
[02:25:43] I'll share on the YouTube also.
[02:25:46] Yes, uh there will be a deadline also
[02:25:48] for the guide. See guys, I have shared
[02:25:51] you this. Okay, those who are facing any
[02:25:54] difficulty related to platforms,
[02:25:57] please raise your concerns here.
[02:26:05] name registered email
[02:26:09] and
[02:26:11] okay
[02:26:13] like login not received something like
[02:26:15] that. Okay.
[02:26:24] Yeah. We'll try to fix this as soon as
[02:26:27] possible.
[02:26:31] I hope it's clear to everyone. I'll
[02:26:34] share this with my team also.
[02:26:38] Okay.
[02:26:46] so that we can know what is that you can
[02:26:57] please give your registered email ID
[02:26:59] only here
[02:27:02] I
[02:27:05] have and the same thing
[02:27:08] okay
[02:27:11] and one important thing don't give fake
[02:27:14] issue secret okay write down only your
[02:27:16] general queries
[02:27:18] only operation related queries you have
[02:27:20] to write it down. Okay.
[02:27:25] All right guys so that's all from my
[02:27:27] side today. Thank you for joining. Bye
[02:27:30] all. Bye bye.