Introduction to Data Visualization and Analytics | CS Viz | Skillshare

Introduction to Data Visualization and Analytics

CS Viz

Introduction to Data Visualization and Analytics

CS Viz

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26 Lessons (1h 34m)
    • 1. 1

      3:09
    • 2. 2

      2:17
    • 3. 3

      3:49
    • 4. 4

      3:37
    • 5. 5

      3:11
    • 6. 6

      3:35
    • 7. 7

      3:33
    • 8. 8

      3:08
    • 9. 9

      4:00
    • 10. 10

      3:06
    • 11. 11

      7:49
    • 12. 12

      2:38
    • 13. 13

      5:17
    • 14. 14

      2:14
    • 15. 15

      6:13
    • 16. 16

      2:14
    • 17. 17

      2:33
    • 18. 18

      3:08
    • 19. 19

      1:25
    • 20. 20

      5:32
    • 21. 21

      5:04
    • 22. 22

      2:29
    • 23. 23

      4:54
    • 24. 24

      3:32
    • 25. 25

      2:37
    • 26. 26

      2:51
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About This Class

In this course you will learn basics of data visualization, we will start with how storytelling can be used in data visualization with examples, DIKW hierarchy used in data visualization, we will look into Gestalt principles and how it can be used in data visualization and at the end we will learn which chart types to be selected in different cases.

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Transcripts

1. 1: hello and welcome to data Visualization course in this section, we are going to talk about the importance off data. No, the first question, which we need to ask for learning data visualization. Is it worth the amount off time, money and effort we're going to spend on data and presenting it in a nice farm so that can be consumed by our user? To answer this specific question, I would like to point out to some off the discussion, which has been done in the past on one of the very famous analogy to date AWAS comparison of data toe oil on Day one of the court, which I really liked in this comparison is from Michael Palmer now Michael Palmer, exactly told that data can be as much valuable as oil, but it is actually in the crude form. Just text numbers on and the forms off zero and one, which is stored in your computer in a digital format but really find information meaning out of the date on and make it off certain use. You need to actually do two main things. The first is to break it down, and secondly, analyze the data them now I would like to give you one analogy to our current circumstances . For example, if you have a database, it can be huge number of records sitting in a computer in any location in any part of the world. To really get some benefit out of the data what we need to do. For example, if you have the raw material for cooking your food, you can consume this raw material, but it would not be very appetizing to really make it more valuable are more beatable. You need to cook the food. Once you cook the food, you will be able to prepare dishes on. You can serve these dishes to people, and then they will be more happy to consume the dishes over the raw materials if they are not on a strict diet. The same case happens with the Dyersville. If you want to present the data, which is very easily consumed by the user, and then they can get much more value out of it. Compared toa the date on which exiting the database table with just zero and one, you need to actually perform the operation off, breaking it down on analyzing the data and preparing the data for the use two yard users. So in this course we are going to drill down into some of the timeless principles which we can apply in our analyzes process and preparing the data to be consumed by the users. On in the next sections, we will be seen water the time, those principles which we can apply and we see step by step, the process off really preparing the data for the consumption off your users. 2. 2: So, after understanding the importance of data visualisation, we're going toe. See why we need multimedia to represent our data. In our visualization. A multimedia is the form off images. Maybe it doesn't form off colors or different phones may be different geometrical shapes or even music on a few off. The modern visualization also contained background music's in it. So why we co such led to make our visualization stand out or look more nice? Is it just to make it more nicer or make the customer or user feel that you have put effort so you can justify the price they're paying? The answer to this question can be better explained by brain science. If you see the human rain, the human brain is mawr directed toe visual cues. The images actually bypass your language center and could directly to your visual cortex. And the visual cortex is having the ability to recognize a pattern and also make sense off the image 10 times faster than you are able to make sense. Often text because if you read, it takes, you have toe anyway, decipher it inside your mind. You get the meaning out of the text and then you start to understand it. But when you see an image directly, that image is sent to your visual cortex and you can easily find batons in the images. So this is not something which is proven just recently by rain signs on CAT scan reports and many other experiments. But if you see our ancestors, they learn to understand and communicate through images first, and then the scripts came. So we are in a more wild to understand images. So in a visualization as well, we use this facts and findings to make user understand the information much more quickly, using multimedia and colors. 3. 3: before moving into techniques and more of the details of the regionalization. I would like to first define visualization. Now, if you Google the term with allies are visualization, you will probably get this definition. The definition says that visualization are visualize is to form a mental vision image or picture off something on visible or present to the site are often abstraction to make visible to the mind or imagination. So basically, the important words here is we are creating a picture in the mind off our user. It can be imaginative reap, picture. Or it can be a very abstracting something very new to give you an example. If you see this study data visualization Now, this is a very old graph. They're in the Y axis. You can see the planets on the X axis. You can see the time. So this is actually showing the moment of the planet over the time. So this is actually 1/10 century graph which stills how different planet usedto circle on What is their motion pop? So this is creating an picture in the mind off the user who are very open to the idea that we can have different planets revolving around sun or something else or something other than Earth. Because in the olden times, people used to believe that Earth is the center off universe and everything else is revolving around it. So it is creating an imagination in idea in mind off the users. If they're open to this idea, then they will actually think about it on DA. If you consider maps to be very innovative and reliable, then let me remind you that all old in war maps used in 17th century, this is one off them. You might have seen them in movies or in some TV series where the military general or some Lieutenant day plan course of action on top of a map, they say. Okay, this is my army, where we have to attack, which are the positions, which is the enemy tens. If you have bean to the war, that's well and good, because you have seen the actual planning. So in the olden days, they have toe brain strong in the top of the map, so visualization techniques and the maps like Google maps and mystery maps on or something very new they have bean used throughout the time in history, in different farms and in different ways and different times. Now, if you see the modern concept off data, we have the big data concepts we have. The four properties are Vidar volume, velocity, variety and veracity. We are going to make this course as a big data oriented court, but it's a well known fact that we have to deal with such amount of data, which can be complex. We have bean also in our arsenal toe work with some off the tools. Some of them are very easier to work where you have to just import and export data like W information builder Onda. There are some other tools which are there, where you have to really spend some time in programming, including, like D tree. In this course, we're not going to go deep into our to handle such specific data like Big Data Technologies on discuss about that. But we will be seeing the principles off how the front end visualization should be created for such complex data scenarios. Very user can be benefited for reaching their goals or getting spied. But findings 4. 4: when we talk about D I queda blue individualization. It's something very important to understand that What is the Ankita blue? Many off you might have heard this and might also have experience. If you are from a data background than you might know, what is the I could understand for D I k w stand for data, information, knowledge and wisdom. Now, this is actually an bottom to top approach. So basically, you have the data and you find the relationship among the data Onda that give you information. Now, on information, you find some of the patterns you compared. If informations, you get more patterns on that something which you call knowledge Onda. From our timeless principle off acquired knowledge, you can get into a stage where you can collect wisdom. Now the goal off this section is to tell you that data in itself sometimes is not that powerful until it can help find some problem and conclusions to make meaningful actions for some results. Toe, Explain this. I will tell one story Now This is the real story which happened in 18 54 now in 18 54 in London, there was a cholera outbreak is people were getting sick and other people they're trying to know What is the cause of the problem? They were seeing this for the first time on dumb until Jon Snow came. He depicted that the people who are affected by cholera, they are mostly living near one particular water pump. Andi does. He was able to convince that the cholera is a waterborne disease and the Palm Beach is shown in the orange color is being affected. Now, this is an example off our D I k e w because the information which Jon Snow had all the data you can see the data points here is the people who are getting infected with cholera, where they live on the map off the London. Now, in this case, he also have information about position of the water pumps. Now, if you use the map and you map out all the victims of the cholera which you can see the black dots there in the maps and also the water pump, then you can really understand that the majority off the victims are near the water pump which is being affected. So now you can really say that there's a correlation between the disease spreading and people using the same water pump which is infected. Now. This is something where we step into from information into knowledge. So if you talk about the visit, um, aspect of the story, the wisdom aspect is that cholera is a waterborne disease now in future, if there's an area where people are getting sick of the cholera Andi, there are specific regions where many people are getting sick. Then the first thing we should go is that we should check the source off for supply and see if there's some water supply which is getting infected from the germ which spread cholera. So this is the I K W. And how it really helps off a mystery. It is also very helpful in finding some off the meaningful information which can be legendary. 5. 5: so there's also a very interesting discussions about the user experience. Involvement in data visualization. How the user experience off your data visualization looks like. Is it something which the user is comfortable to use? Or is it something which is very complicated Now, before moving into the user experience aspect of the visualization, we should first ask why we're going to focus on your experience and the answer discussion can be your user is going to work on the graph you built off the dashboard you created while using it. They're going to feel in certain way on. It can be either negative or a positive feeling they're either very comfortable in using it are they find it very difficult. They always go through health document you bills to see how to use your visualization, Austin individualization and you made the controllers very un intuitive. And it's very difficult to never get true. And also it can happen that you might have regions of realizations or some features which the custom marble them. Burfeind is really heartbreaking for any developer or designer, it'll say, and ah, there are some studies and tested principle from the domain off user experience which can help you or I will say guide you in the design process and the developing process to meet the user expectations. So why not use them? For example, if I show you this chant, this is a very common chant use in stock market. Now I will tell you what is wrong with this chance. You might look into it and then you miss the scales. It's not right. The data is very small. There are two chance I'm not able to see the bar graph. Clearly, the distinction is not right and many of the things. But if you see from a user experience point a few, there are many other problems which you could find which you might never find from a point of view off. A Data engineer audit analyst, for example, one off very odd phenomenon which is related to human eyesight is that if you are a red color blind person, then you will be probably seeing this chant like this. You can see that I'm not able to differentiate clearly between those red and green in the top one. I can really see where the greens are and where the reds are and that's probably giving me one more dimension to this bar shot. But the below one, they are almost on distinguishable. So I'm losing one dimension and I'm losing. One. Specific information is the coloring is defecting. That can be a crucial information. This example. Waas Not that intuitive in common. You cannot really say that how the color blind person will look into this graph. But there has been some instances where people have these issues, and as a developer and designer, it's a responsibility to make the solution as much stable and better as we can. 6. 6: user experience is a very last topic in the context of data visualisation, which is our subject of interest. We will be covering few key points, which should be always followed. When you try to incorporate your design according to user experience principles. For example, the first rule says that new your user view should be always aware about your user. Who is going to use your data visualization. Now you have to do a little more research about the user. You have to know one of the personal attributes like age, the culture, the background from which environment they belong. And what is the expertise? Are the subject matter expert or are they very new to the subject? So let me give you a few examples off how these attributes play a major role in deciding what kind of design thinking we should be incorporating in our data visualization, for example, here you can see in the left Inside, there are certain phone points, which you can read. There are six points. Phone, which is the smallest on the largest, is 24.8 points. Now, when we speak about the user, who is going to use your visualization. If they are young, then they might have no issue reading a phone size off 10 or 12 or 14. But if your target audience is little bit older, then they would probably like to have a phone size, which is very comfortable to read. For example, 24 or 20. For example, if you are creating a visualization for a game which is popular in certain countries than people are well familiar with the names and jargon used, other people may have issues understanding their key points. For example, in this picture, I'm showing nations which are playing cricket. This is international career Council I C. C. So if you take some person from Egypt and you talk about the offside, the leg side and the straight drive the cover drive, this spines may not be that clear to someone who is from a cultural background who don't have that much exposer to that game. For example, if I take the same person from Australia or England, the mighty Ville aware about the terminologies which are used in a crooked so you don't have to really make them understand what long drive what color drives on what street Dr Stand for and one of the most important aspect, which, as a data visualisation designer you have to consider is understand how much expertise your target audience holds. Is he or she a expert in the field? So you don't have to really make them understand all the things which you are showing or is he a newbie? So you have to do a lot of hand holding to understand what the data and the data points represent, and maybe also helped them to understand what you're tryingto say in the data points as well. About from that, there's one more factor which many people overlook, even some of the user experience. Designers also don't look into this matter, But this is actually one of the key points how people are going to use it. Are they going to use it on a mobile device? Are they going to use it while travelling? Are sitting quietly in an office? So this airfield, the design keep bonds, which you should always remember while designing your visualization 7. 7: the previous section, we saw the key principles related to user experience while designing your data visualization. The most important fact is that you should know your customer or user. Now in this section, we are going to discuss fuel user experience principles. These principles are timeless and can be used in any situation. May that be data visualization or kidding your website or creating a mobile application? So these are the five user experience principles. The 1st 1 is digestibility. A good design should be easy to digest. The brain should have toe spend a ton off energy to figure out what's going on and what they're looking for. The main point should be visible very clearly. The second key principle is clarity. Clarity is similar to having simply city in your design. Sometimes you are providing some value proposition, or you're placing a free trials, but or maybe in some data, points off your data visualization. You want your user to perform certain action at that point of time, you should be very clear on present ing what to do and why they're doing it for, So this information will help you toe make your user understand what they are required to do. The third key principle is having trust. Being honest and clear in explanation builds trust at each step. But as your user starts to trust your website or applications, then you don't have to spend more resources in building that trust, and it will be very easier and more enjoyable user experience. The fourth key principle is familiarity Now. This is very important because once the user is familiar with your design, then they don't have to spend much time in learning what to do and what not. For example, if you see the sign of form, then they are almost similar in most of the website, which you use. They have the same name, last name, your email idea, your password and a summit button. So that's pretty much similar. So you don't have to spend much of the effort explaining what to do, and your user will be less confused and the yard user experience will be much better of the final user experience. Principle is delight. It's been said. That idea isn't enough anymore. That execution is what wins the war. One of the best example can be when you see someone using Xbox. They are so much immensely engaged with the game itself that they might forget that they are actually interacting with the software. The ultimate delight is when someone forget your product is a product where it's useful that it doesn't even read as a product anymore, just simply as some useful things in a person's life. Now these are the five key principle digestibility, clarity, trust, family lt and delight. Now, whenever you design next time an interface it can be a mobile application website already dead visualization. If you pay attention to this five key principles are they following this five key principles, then you will be far better off for the design as but the user experience guidelines. 8. 8: So, after understanding the user experience key points and using expedience principles, let's come back to the capitalization. Now when you are present ing something, inform off charts, diagrams and text. Then anyway, you are communicating to your user. It can be about a story, or it can be about some findings you want to represent. Allow your user to find the finding themselves. Now when you are present ing this to your users than there are overall three different vase , your user will be perceiving this information now. The 1st 1 is the scanner category. These are the people who are looking for something different, something exciting. But they are looking for something which is new so they can be hooked to your data visualization and the second category of the peoples of the people to have lots of question in the mind. They are more interested than the category one who are just the scanners. They may want to know more about what you are trying to say. They may be having some questions regarding that credibility off your radar sources or what you're trying to say. They might approve it so they might disapprove this with their questions. But over all these other people who are more interested than the scanners, then there are the third categories of the peoples who are more interested in the second categories. So these other people who are most interested in this three categories of the people they are looking into your data, their understanding, what you're saying, and they would like to know more about it. And they are interested in finding similar data visualization and understand more about the top. So when you present your data visualization, you have to keep in mind the rich audience. I'm preparing married civilization. So the 1st 1 who are actually the scanners you need to present your data in a form that's grab attention. So, for example, if you are designing infographics on, you are showing an overview off. What you trying to say? So this is something which the scanners might be interested in. And if you are trying to communicate to a person who has a lot of questions who are more interested in scanner, then you have to tell a story to them. You have to tell a story with your data, so they are interested in your data visualization, and then they get a complete picture off what you are trying to say. But if you are trying to communicate to someone which is most interested of this tree categories, then apart from providing a good story to them, you need to also provided deep findings summary to them and also other sources where they can do research. So these are three general categories off your users on they will be expecting different things. So when you're designing your data visualization, you have to understand which category your users falls in. 9. 9: So after understanding the de visualization category based upon your user, now we will see the trivialization category based upon your requirement type. Now there are basically two types off Rita visualization. Based upon your requirement, the most one is explain a tree. The 2nd 1 is exploratory. Now, when we talk about explanatory visualization, they actually direct your user on a definite BART on the explain some of the details they are meant for someone who wants to understand something or who has a question in their mind are who want to confirm their decisions. Bulk off your business stash boats are off. Explain a tree types, for example. If your user have a question, how much sales did we meet? So this visualization, where you can see the sales report off all the countries off your company, you can see that United States is killing it. The Russia is just next to it, and the South African Division is in the third position. Now it's actually answering a question that how much sales did we made? Now you can also support a decision. For example, if you are deciding to invest some money on your Australian division because you think that Australian division can actually on more sales? And also, if you want to communicate some information for example, which division is most profitable? Least profitable? This kind of visualisation is for you. So if you see the pattern, the pattern is user. Have a question. They see the data visualization and they get down. Sir, that is the pattern which explain a tree types have now moving on to our exploratory type. Now here the data can have many dimension. This info graphics is the list off noble prize winners. So you can see the top. We have 19 0 won the 19 0 to 2 1925 Then if you come to the second row, we start from 1926 to 1950 similarly till 2013. Now this data is having many dimensions. For example, there is no two dimensionally. Damn, you are probably seeing here. The Nobel Prize winners are the male of the females and you're also seeing from which region they belong. So we are using colors were using date we Asian time. We're using Aikens and many more things. So this kind of data may be having much more dimension and can be much more complicated as well. The basic pattern off this kind of data is your user starts to familiarize themselves with the visualization. Then they identifies some idiot off interest, and suddenly they have a question on. Then they see again the data visualization, and probably they will have an answer. For example, if I looked through the data visualization and I want to see what is the percentage off female who won the Nobel Prize from 19012 1925 then I can probably see that in this 25 years there is only one female who won the Nobel Peace Prize now to summarize this to civilization. The explanation tree visualization is the one which answers the questions are supports a decision on the explore it 31 where the user posed new questions and explores and discovers new facts and figures to the data. So always understand, which is your requirement and which kind of civilization you are going into 10. 10: Now we're going to discuss about a very interesting and Baden topic, which is get stalled. Psychology Gestalt. Psychology is actually a theory off mine in school, off Berlin, off experimental psychology. Now, basically, to understand what we're dealing with, if you see this image can tell me what you are actually seeing an image to give you some pointers. Can you see in the image the old lady? This is her nose on DA. On the top, he has a scarf and probably wearing a black sweater. Now, if you will look this image very closely, then you can also see a different aspect of the image. You can also say that this pointer where the arrow is pointing is actually the cheek off a young, fashionable lady who is looking to right side. So this is what the real point waas. There may be some people who can see this image as an old lady who is looking down with very old fashioned wearing a sweater and a scarf. Or someone can look this image and say, this is a very young and fashionable lady who is looking to its right inside, so the same image can be actually seen by two different person in a different way. In the same way, if we talk about our data data is confusing by default. Now what gestalt psychology tries to do is gives you a few laws which will guide you through the process off how our mind actually thinks on what the mind will perceive on certain patterns or maybe certain informations. So when you are designing your data visualization, you will keep that in mind that I have toe alloy this because user can perceive my visualization in the wrong way. So basically, these are eight principles on this principle exist because the mind has a innate disposition to perceive baton. So this is actually because the theory of evolution off how we evolved are because how we are, there are certain images which we are wired to see, even if they are not the point of discussion on. There are certain images which we will always miss even if they are presented. And actually it will be very surprising to see the examples off these principles because you will also notice that your mind is thinking the patterns in the different way. Then they should be So this eight principles are proximity, similarity, closures, symmetry, common fate continue. T good gestalt on figure ground. So let's see what they are. 11. 11: So now we're going to discuss about it. Gestalt principles in details. Now I'm going to present these eight principles with a lot of examples, so it would be easier for you to understand what they are and what they mean. So this eight principles are proximity, similarity, closures, symmetry, common fade, continue. T good castaldo and fear ground. So let's start with proximity. For example, you can see here that all the dogs are having equidistant, so you think that this is actually a single object? But in the right hand side, you can see that these don't start divided into three parts. The first part is the first column, the second column of the Dogs, and then the second part is the third column and the fourth column of the dogs. On the third part is the fifth Column and a six column off the dog. So what re analyze from this fact that our mind perceives these objects, which are closer to each other as forming a group. So basically it may not be a group, but beating it as a group because off the proximity or the distance they are in in summary , the loft proximity states that when an individual perceives an object, the perceived object that are closer to each other forming group. Now let's move on to law. Off similarity. Now this law states that if you perceive an object which are similar to each other, then you think that they are actually trip together. For example, in this diagram you can see that there are and black circles and then they are white circles. You can see that there is a horizontal line forming from the black circles on a horizontal line forming from the white circles. You will not see the vertical lines because then you will have the pattern white, black, white, black which is not similar objects. But you can see all the white circles on the black circles as they are similar. They seems to form a group. So this is law off similarity moving on to the next law, which is law closure. So to understand the loft closure, if you see the image in right inside, you can see that there are three circles which seems to form a white triangle. But in reality there is no white triangle. This is something which we perceive because they are positioning suddenly, so our perception is actually filling the visual gap. And even if you see the triangle which is in the background, which we think as a triangle is not a triangle at all, is actually three arc, which are just position, like three robotics off the triangle on. We think that the White Triangle is actually on top off the background triangle, so we're not able to see Pantai Triangle. But in reality, we have only three arcs on day three circles in the picture on the left hand side. Also, you can see that there are no circle and rectangle drawn, actually, but we perceive that there is a circle. And then there is a rectangle, so to summarize, loft closer. It says that individual actually perceives object as shaped letter pictures except Tra as being hold when they are really not so moving on to los symmetry. Now what you see here is you are seeing a square bracket in the first, then a curly bracket group and then again, a square bracket. So we tend to observe three pairs off symmetrical brackets rather than six individual brackets. It is because we are seeing do brackets as a whole and not as in individual things or entities. So basically, the laugh symmetry states that the mind perceives object as being symmetrical on forming around a central point. It is, ah, perceptually pleasing to divide object into a even number off symmetrical pattern. So that's very interesting fact to know that our mind is actually very happy to divide the objects into your number off symmetrical parts. So now moving on to the loft common feed. Now, to understand the law of common fate, I have drawn few circles. Now, if I do a little animation here, can you see a few off the circles? Moved. Do the right inside. Now what you will think those circles which moved to the right inside are actually a group , but in reality it can be or it can be not. So let me do that again. So if I come back to the left and then I go back to write again. So the lof common fate tells us that if the object or multiple objects are moving in a single line or a single path, then we perceive that they are in a group moving on to the next. LOL off continue. T Laugh Continetti tells us that we perceive an object to be made a paff continuous object . Any point we see that the community is breaking, we don't consider that so here we see that the crosses made off two slashes instead of a grated than analyst in sign moving on. Do the law. Good guest old. So we see an image which is made up off a rectangle, a triangle and a circle. So basically what we're seeing is three different object and set off one single object the love good guest All say that elements often objects tend to be perceived are grouped together if they form a pattern that is regular simple or Audrey please. So, for example, if you are presenting a data and your data is showing e baton, which is actually shown in this image, no user will actually see the pattern in three different distinct, said the one that's rectangle and the other one is strangle on the 3rd 1 as a circle. This gives us a good idea about what the user will thing until of what we want to show them . So moving on to the last law, which is law off past experience. So the lof past experience implies there's under certain circumstances. Visual stimuli are capitalized according to past experience. So in this two images, you can see one of the left inside and one in the right. Inside, the elements used are similar, but because off their position, if I put this to filled circles outside off my main circle, then you will really don't see anything important here. But once I put them inside the biggest circle with this ark, then you will recognize from your past experience that this is forming a smiley. So to sum up the guest stars principles are this eight key points. This key principles provides you techniques to be used in your design or data visualization so you can make your data visualization easily perceivable by your user. They can clearly see what you're trying to convey. The message in the first glance are while they are interacting with the deregulation and also to avoid certain mistakes that your user might perceive in a different way than what you want to show that 12. 12: many off us who had previously dealt with data visualization might have this issue We always thinks about what should I use to show difference. For example, if I talk about a two dimensional visualization, you have your X and Y coordinates, so this is something which you can use to have points. But if you have more dimensions to show, for example, there may be other useful information or defense ation, which you want to show in your graph. Then what are the other key factors, which could include, for example, look at this graph where the user can use X and y as to dimension. They can also see the sizes off the circle that is the third dimension. Then they can see the colors of a circle. So this is something where you are able to showcase some of the interesting aspects off it . Eight or maybe a new dimension, or maybe some interesting points you want your user to consider. So we are going to talk about how to show this differentiation and numbers of ways you can show this. So basically there are very few ways to show differentiation, but actually there are many number of ways to executed to be very specific. There are five number of ways to show defenestration, so the 1st 1 is pollution like your X and Y coordinates on the co ordinates value shoes, one different station factor and the size, for example, the size and circle. The bigger circle can represent a bigger area. On the value will be more than we have the colors, the colors, for example, what we used as yellow. Why on purple they actually can show different dimension that it or, for example, if I want to showcase the data off different countries where each country's presented color , so I can use the colors as a differentiation factor. Now the next difference station factor is contrast. By using contrast, you can show differentiation at a similar toe color, but for example, you are having a monochromatic visualization. For example, you can only use gray and black and white, so in that case you cannot really use any colors like red and green, so you can show the differentiation using contrast. So the final differentiation factories shape. So now we're going to see this key five differentiation in details 13. 13: So let's see some off the examples off the differentiation which we discussed in the previous section. Now, in this following two dimensional graph, this is actually a bubble chant where in the X axis you have the power index and on the Y axis you have the fee fine decks. So all the countries are placed within the power and day from the FIE fund eggs and also the size off The bubbles represent the population on the color also represent which continent they belong. Toe The positioning is giving the basic to information for one particular country, like the power index and the X axis and the fee find X and the Y axis on the size of the bubble is telling you the population on the color, also telling you the country's so you can see this three differentiating Balham. Atriss playing its role Now if he want Oh, this map Here we are seeing the Internet usage off all the countries in 2007. In the left inside, you can see that we have a contrast, and by the use of the contrast as a filler we are separating. The country's who are having more Internet user than the countries who are not having. You don't need to actually use too much colors because sometimes it's overkill. So in sudden requirement where you want to show a variation among one panna meters, it's always better to use a contrast. So this website is very helpful if you want to create a color palette. Now, In the previous graph we saw, we have contrast pallets off blue on Dwight, and they were around 5 to 6 contrast palates. For example, if you want to generate more Paris like maybe 12 then you can enter the number at this text box and press this button toe. Make a palette. It will generate some pallets, and you can refined those pallets by changing the HCL value here. So basically, I would always prefer toe have single color variations like contrast, which you can do as well because if you provide too many colors to your user than your user might be confused now, basically they are generating some of the colors which the human eye can differentiate. You can also find the hex a little code for the colors and are typical for the color, which can be used in your CSS. Or maybe if you're designing a photo shop, then this can be used. You need to experiment with this to really come up with a nice color palette. But the I want aged with this tool is that it will generate the color palette, which the user can differentiate very well. Sometimes what happens that we need to fulfill a requirement for a customer who already have a color palette, So if they already have a color palette, then it's better toe. Go with that. Sometimes you can also create a color palette, which is having similar color to your customers, logo or phone color. This will really impress your customers because they will appreciate the fact that you have done some research and thoughtful thinking to come up with this color palette. Moving on. The other differentiation factor is ship. So here you can see that we have a upward arrow and downward arrow to show user that the value is increased are the value is decrease. It's very intuitive on very easy to follow, and user can release it information that overall sales has gone up by just looking at this shape now, one of the key point, which is always remain as the question is, which are the defense she ating factors which we need to sell it. There is no exact rule to be very specific, but the rule of the time is you need to make your graph our data visualization as simple as possible. Always have this question in mind that why shouldn't I go with some simple visualization? Why should I have to go with the bubble chart? Why cannot I use a bar chart? The more simpler your solution is, the easier it will be for your user toe work on Onda, the more happier they will be. I personally preferred this pattern. I will always prefer position as my first priority in selecting the differentiating perimeter. Then I will go with the colors and then I will go with size and then I will go with contrast and at the end I will select shape to show the differentiation. Now, this is not exact rule to say, but this is what has worked well for me and hopefully it will be working well for you as well 14. 14: So now we have certain bit off idea off how to use differentiation in our data visualization. There are few points which you should understand. The first point is that color should be used with care. Now there are chances that your audience for which you are creating your data visualization , there can be chance that 5% or 10% of the audience can be called blind. So whenever you use color, you should do a background. Check off how the different type off color blind people will perceive your colors. So this is also enforcing the idea that you should know your user. Now there's a website gold color blindness dot com, where you can actually calibrate color palette or even your data visualization to see how they are perceived by your user who are having certain type of colorblindness. Now you have tow upload an image off your civilization. Onda. Once you upload your image, you can select different type off colorblindness to see how the image is perceived. All the data visualization graph is perceived by your audience with different color disabilities. Now I have seen this multiple times that people want to make their visualization very jazzy . They want to include an extra dimension to just to impress their users off how technically good ones they are here, for example, I cannot really see the CDs four group to did. Um, it's actually hidden behind the series six bar charts because they are Moland here. And also, if I want to do a comparison, an exact comparison between CDs six, Group three and Group four I cannot do it because you cannot really tell if there's a minute difference or not. It seems to be equal, but are they really equal? I cannot really tell for sure. So these are some off the mistakes, which can be avoided if you are doing data visualization for your user. 15. 15: One of the very important and challenging decision for individualization is to select Vigen type to be used. Now. If we consider the chant tied, there are basically a few simple chart types, which you will be preferring if you want to show 123 dimension are variable off data. Now we're going to talk about each one of them. When I was a big earner individualization, I used to select those charts only which I can work with in programming now. Sometimes I would also see some of the charts similar to my requirement in some website or maybe some article, and I used to select those chance because I have seen them there. But this should not be the case. While designing individualization, you're chant, I floated. A visualization should be mostly dependent upon what is the requirement and who is your customer. And there are many of the factors which we already discussed, like user experience, principles and get stalled psychology. But now we are going to see some of the basic chart types and when to use them. And what are their at want ages? And they said, wanted this now the first chat types is bar charts. The bar charts are most prefer an effective and simple vague to represent your data. Whenever you're having any requirement for the D visualization, you should always ask this question that can I show this data using about shop? When you're presenting some data on the bar chart, your user will have no problem in understanding how to read the data. Because the barges are so common that everyone has experienced with the Bard shot, there are some variation you can try with the bar chart. The 1st 1 is multi variable bar charts. When you want to show categories with your data Israel and you want to present in the bar shot, then you can have multiple categories. For example, here we are having three different categories, which are compared side by side in a single chant on the categories are blue, green and red differentiated from their colors. Now the problem with multi variable bar shots is that if you are having Manica degrees, for example, if you are having 10 or 20 or more than that, then it will be very confusing on. It would be almost unreadable for your user to perceive the detail now. The other variation off Barton is called Stag Bart Shot. This can be an excellent way to compare within groups. For example, I want to compare between the first bar and the second bar on the corresponding categories of the bar. Then I can do it. I can even compare between the same bar and difference of categories. So the same problems lies with the stag barter as well. If there are many categories than the data becomes unreadable very quickly when you want to show something over, time on dure user is interested in looking for trends. Then the best chart is going to be your line shot. This line shot you can see here is showing a trends over the time. This is most preferred in showing how over the time my sales figures are doing My revenue reports is doing on its very easy and fast to get those data in our visual perception on getting feeling about the data. Now, one of the problem why bar chant should not be preferred while chewing trend, because whenever we see a bar chart, we always on our subconscious level, read are perceived the area off the bar as its value. For example, if I'm seeing the first bar here and the second last bar, I'm actually, in my subconscious mind calculating the visual area on thinking that the first bar is more than twice the size off the second last bar. So this is because we are actually seeing the area and subconscious mind Onda while showing the trend. The line doesn't show that they only show over the time the points which user is interested in. Sometimes there are also requirement from the user that they want to see some of the visuals clues related to time. For example, they need to see some events. Andi need to also have the events, description pictures than in that case. Your timeline is the best chart to go with. It's actually having content based stories with time. The points here, which are represented as red circle, can be also a infographic elements within your charts. It can also be interactive, so if the user clicks on any off the infographic elements, then they can see details about what has happened At the particular time are more information. If you would like to give to your user the next chart is the idiot shot. It gives you an idea about the Kardashian product. For example, if X axis is prize and why access is my sales, then the area off, which is covered within the chart, will give me actually how much money I made. Now there is one situation where using area chant can be a problem on. That is when you have multiple area chance and they are overlapping each other and you cannot see some of the points clearly so you can use area chart. But when there is a whole lot between the data, then it's very difficult to read on. You are also not able to see some points which are covered by your purple area. Chances will. So these are some of the basic chart types, which use position on coloring as their primary differentiation factor. 16. 16: We just saw bar charts, line charts and 88 charts. Now, when we talk about scattered plots and bubble Charles, scatter plots are excellent way to show correlation among data points. Now, in this chart, you can see we have reader points in the red color. On day, they are differentiated in position from X and Y coordinates. Now this particular correlation, where the Y value is increasing with X value, is called a positive correlation. Similarly, a negative correlation will be something like this fair increasing the X value. The Y value decreases now scatter plot is also ready. Good. In showing clusters, you can see there is a class reformation happening here, where some off the data points are grouped together on. We can say that there's some interesting things happening at that Data points now. Scatter plot also can be used to show outliers in the data because you can really point out that some of the points are not having any sort of pattern are they are really falling apart from how the other data points are behaving Now. If you want to show and add additional dimension, then we can go for bubble chance here. The data points representation, which was a red dot, is not uniform. There are some red dots which are bigger in area, and there are some which are smaller in area. Now. Here we are differentiating to the point in the new dimension with respect to their sizes. The sizes of the data points is not uniform. They are different, which is conveying the third new dimension. Now, if you want to add one more dimension and where you are going toe showcase four variables, then you can also add colors to your bubble child. Here you can see the colors red, green, yellow and white conveys one dimension on the size of the circle conveys another dimension , and the X and Y position coordinates off the points conveyed story I mentioned. So it's actually representing for dimensionally down. 17. 17: So now let's talk about the bike Shot by jobs are very popular. The individualization chance they are more and more used nowadays in the business dashboards. They're basically used to compare two or more values. For example, here there are £2 off the circle. There is this big part and the blue pop now here. The sector, which correspond to the pink pod, is actually the category on the second category is the one in the blue here, as we already discussed that our mind always perceives area in subconscious level, and we compare the area off to objects, and we understand which object is bigger than the other. So it's a very nice way toe show comparison between multiple variables, but there are some debates about usage off by chance. Some people indeed are visualization. Community believes that by chance is not the best way to show comparison. They strongly recommend not to use by chance. If there are more variables to compare with now, I will agree with them to certain extent, because if you see despite shot, this is almost unreadable. You cannot really have multiple categories who can be compared within a by chart because I cannot really see some of the points which are having one person or 2% off the entire circle shape. Now, the best rule to decide if you want to go with the pie chart or not is to ask your customer if they are previously used to see by chance for the same bitter points and they would like to see pie charts. Then you should give them by chance, because somehow, after all, decisions and rules is the customer who decides what is good for them and what is not. Because in the user experience principles, we came to know that the principal off familiarity or the past experience, we always want to use the things the way we are usedto. If you're showing comparison between categories which are not many, which are under five R seven, then you can go ahead with the by shot. But if there are many categories, then it might not be the best decision to go with a pie chart. 18. 18: scale one of the very important aspect off your data visualisation, which is often overlooked to tell you the importance off scale. Let me show you a little example. What will happen if you misuse the power off scale? For example, you went to the bank, you want to open its saving account and you are looking for a bank which can provide a high interest rate for your savings. And you meet with Bill. Bill is the salesman for your bank. To convince you, he shows you the interest rate of the bank and tells you that the interest rate has been growing from past few years. Andi, he tells you this is the best bank which every salesman's actually does. No. As you are smart, you go home and you do your research and what you find, you find that the interest rate is almost saying now this to graph looks very different. The one which Bill the sales person showed you and 2nd 1 is the one which you found in the Internet. Then the question comes why two graphs are looking so different than one is very convincing . The other one is not convincing to the sales pitch. Was the sales person lying to you? Now the sales person waas not actually lying to you. He was just changing the data and scale. So it looks more convincing to you that the interest rate is growing rapidly and will do in future as well. So it gives him more chance to convince new prospects to become customers. So that is one of the most important points regarding scales. By changing scales, you can drastically change the message picture data is sending across the table. So be aware. This is a small thing, but can have a big impact now how to select the right scale for your data visualization. So that may be the important question which we should understand. And the two rules, which you need toe understand, is that first, you should be very clear that your scale is actually presenting the message to your audience correctly the way you wanted on. Secondly, your visualization is buys free. It can happen that you are presenting some visualization, which gives a good news, a bad news and a neutral use. So if you try to make a bad news, good news are in neutral news good news or whites? Aversa. Then there is a business there. So a white business in your skill, our whenever you want toe, take a final decision. Just consult your customers because they are the one who will be using a solution and they are the one who are paying you to do what you're doing. 19. 19: legends. Legends are used to tell your user what they're looking for. For example, in this chance there are two colors used, one green and one blue, and your user will come to know by legend. One of the key important mistakes he normally do is we assume things. Sometimes it's very common for a developer designer. They assume that user will have no problem off understanding what they have done. But that's actually another case, because you are the one who has spent weeks off time designing and developing the solution to your user and the user. When they tries to use it, they're looking at for the first time, so they might not understand what you have done. So it's always better toe educate your user. Using legends on even interactivity will help while educating your user. Also, don't be this guy. You don't have to show your user that you can use all the colors, so I always recommend to use common sense. And there is a joke about comment since that common sense is not that much common. So show legends in the other day visualizations they are very helpful for your user on be reasonable in selecting dimension on be appropriate for selecting the colors 20. 20: in the section, we're going to see some advance chart types. The first advance job type, which we're going to see is maps. Maps are excellent fade toe show spatial data where you are showing the turbines off certain regions. Here you can see the animated UK Wind chance where the points actually show. What is the direction on Vince? Speed off the areas are, and you might also have used Google maps. Google maps are wonderful, and you can also find some of the custom maps online. Or you can also use the FBI off Google maps Now. After that, there's also a network diagram. Now this is a graphical representation off data where each note is representing a data point on each connection is showing that days a relationship between the two notes. Now each note can be one person can be some object Network diagrams are used when you have a lot off relationship among the data, and there is not a fixed pattern off how relationships are there and you need your user to actually explore how the relationships are. So basically, this is because they are more like your real life now, coming back to other forms off our data visualization. This is a typical heat maps. You can see this is a interactive heat map, but he capped is linked to bar charts by chance and also to a map as well. If you see the legends here, this heat map is showing how many peoples are actually looking for house in a week throughout the day. So the red color is when the traffic as more or it is more searched, and the purple is Venator's Lee search, so concede that at 4 a.m. people are generally asleep so they don't search. And around 9 p.m. They are very active, and they do lots of searching. This is very good way to show and present data to the customer when you have multiple dimensions, for example, you have days, you have hours and you have number of people's. So it's easily a three dimensional data, and you want your user toe be able to see all the data in a single glimpse. Andi compare in a single image. So this is very nice for comparison for the largest asset and finding batons quickly. No. Next we will see a tree map a tree map is some way similar to a heat map where you are differentiating with colors. Plus, you're adding one extra dimensions as the sizes off each boxes here, the size of the box is also very, and then they provide you one extra dimension. Moving on we have of the flow diagram and flow diagram is a very nice fate to show flow off money, people or maybe some objects. So here what we're seeing is the transfer off money from each football team to other for balding, for example, from Manchester United to Real Madrid. How much money has bean transferred? So it is around 1 31.5 million euro on DA. This is very nice to show inscrutable pattern if you have too much data and there is a inter relationship between the data's off flow diagram is the way to go. So the final example for at once chart type is a box plot. Now box plots are very commonly used and financial market. Now a box plot use simple graphics that summarizes a quantitative distribution with five standard statistics. So we have a smallest value. We have a lower quartile. This is the lower court trying value. We have a median, which is this value. Then we have a upper court trial, and we have a largest value. So we are actually seeing five value at a single instance. And people in financial markets and start market are used to seeing the values in a single glimpse. And they prefer box plot because it allows them toe easily, recognize differences between distribution on get the feeling of the data. In a single glance, there are actually a lot off other advanced visualisation you can goto de tree Dodge s. This is somewhat becoming a standard Andi. You can see a lot off examples as well. For example, if you go to this example section in DT judges, then you can see a long off advanced it at times. Most of the people who are experimenting with this visualization they come up with ideas, and they use D three mostly to implement those ideas so you can see many new on innovative ways to present data in de three Dodgers website on. It's something which always helps you. While thinking about selecting some of that one's chart type for higher dimension data 21. 21: the previous sections. We saw the technical aspect off data visualization, like one of the chart types to be selected. What is legends and how toe select the color palettes and designs for your data visualization in this section, we're going to talk about the creative aspect off the data visualization. One of the biggest challenges for some data visualization designers is to start the war. So we are going to talk about some tips and techniques to get started and designing off data visualization. Now, when you're talking to your customer and you are in a situation where you have got all your requirement, they have explained you what to do. What actually they're trying to visualize and might have also given you some on the direction, like what the think the final design should look like or what shot type to be selected. And this is the common phrase which almost all the customer use at the end of the discussion, which goes like this. Please feel free to know it and think outside the box. This is something very prominent, which I have seen almost with all the customers, which I have interacted with because you are the expert here, and they are coming to you for their solution off the requirements, which they have no to really start designing the data visualization. It's been challenging why it's challenging. It's not a very difficult job, but we always said expectation that this visualization will be the best. This will be the one which will be written in the golden letters in the history of the recapitalization, and we also spend a lot off time and thinking about requirement from the users, how that requirement can be creatively and innovatively map towards any design. But you have worked or what you are thinking about. Now here comes the tricky, but our left brain need to work on details and specific instructions like mathematical calculations on our right brain is responsible for creativity, generating new ideas, using our past experiences to figuring out things in a creative way. So whenever I talk about left brain and right brain, I always remember the old cartoon pinky and the brain. If you are familiar with this cartoon, then you will remember that left brain and right brain are similar to the character off the cartoon. The left brain is logical, orderly things in a leaner and rational way, but is actually slow and the right brain is playful, creative, emotional. Imagine ITRI and fast. Now we have to use both off this attribute to come up with some interesting and cools design, yet which is fulfilling the requirements off a customer. So that is a most challenging part which I personally feel Oh, come off it. Some creative ideas And I think it's takes a lot of time to really come up with some innovative idea which the customers will agree upon or which can be developed by the developer as well. So basically one I do to start the work, the first thing which I do is I put all the necessary inputs for a project which I have got into a white board with all the nodes and also sometimes I will also include timelines like how maney vics are days. I have to complete this face to complete this face on. Once I do it, it allows me to give a bigger picture off what I have toe do because it's always important toe keep the bigger picture in mind. So this gives me also control over the entire projects if I want to, because I can really get a summary off the entire progress of the project by just looking into this big board where I'm abating. All this contents and ideas which I have while thinking process goes in on it's always helps me or any stakeholders to show that I'm working on the projects and there has been some thinking and progress going on. Then I go back to pen and paper with nothing else, just to think in a notebook I create and Redrow stuff. This is where I have to use a lot off imagination, creativity when I'm expecting here is new ways and angles from my past experience to current problem. I think about how I would like to see the data visualization if I'm the customer on, if I have to do the exact same work which my customer is doing, so I will try to get into the shoes off my customer and think about my requirement. One thing which I have noticed is that I like to use blue sheets off paper instead off a notebook because you can see multiple sheets at a single point of time and the final outcome off this process is the design and the wild frame, which can be then used by the developer to develop a solution. 22. 22: So what is the importance off telling a story in your did out visualization? Now we are, in a way, wired for stories. Something within us compel us to be that kid again and to be immersed in the story on whenever we hear that once upon a time, we are literally hope to it. It is also the way we transfer knowledge from ancient time, maybe by religious text or maybe writing in gave Z and old scripts. But the challenge which comes here is that people may not use your visualization the way you want them to use, mainly in interactive ones. They will not look the data the same way you want toe. You need to grab the attention so they understand which part to be looked at first and which after to make a story, you have to bring a beginning a mid section, a climax and end. So these are the four main parts off a story, and you have to bring some restriction or some kind of control on your data visualization to make your user seethe story, which you're trying to tell now some of the techniques which you can use you might have experience it as well, but you install your new abs. There is an overly which exists on top off your app, which gives you an understanding off how to use your app. This is a nice way to quickly make your audience see how to use. What are the things to be pressed 1st 1 of the things which will help them or guide them apart from the legend. This kind of interactive overly when you have many things to train your user toe and finally, if you want to tell a story, but you don't want to invest too much on fancy visualization developmental design, then the best way is to use filters on the options. They make your user thing in a way you want them toe on. Day will actually remove many options, which your user might have in their mind, so it will not actually allow your user to follow a straight part. But it will actually remove many other part which the user could have gone, which you don't want them to go. So these are some of the ways you can very story that you're late office like station 23. 23: customers there. The life blood off the business, they're the people who pay you on. Your goal is to help them with what you do now. There are a lot of discussion about how the good customer are. Bad customer can make your life good or miserable and all sort of things. This course is not about that. Here. We're going to understand how to remain in the context of what you are doing. Basically, while you are collecting the requirements from the customers how to manage your customers, these are very important things, which every consultant or every people who are interacting with the customers should know. So we're going to talk about what if you need to get something done or get some information from the customer side, which will help you to proceed with your design or development. Now the first thing is, do not promise the world and amazing things which could really be impossible or take a lot of time. The expectation should be set to the right levels. You shouldn't promise them. Something which is too difficult to do are nearly impossible to do to just impress your customer that they come back to you the rial work shows at the end. And if you promise in the initial level, even if you come up with an excellent solution for data visualization or any design or development, then it will be still okay or average for them. The main idea is toe under promise and over deliver. Many times when you are working with enterprise industries, you are actually automating some business processes with your visualization and reporting tools so they know better than you regarding the process is because they have been doing for past many years, or maybe many months or weeks. Some people may very in this argument, and it is also correct that you should not actually listen the customer every time. And that's also true because you are the expert in your field. Your customer might not be as knowledgeable in your field as you are. Therefore, they're hiring you in the first place, so there should be a fine line between went to agree and went to disagree. And whenever you are in a dilemma about, should I listen to my customer? Or should I do my own stuff than always? Listen to the customer first because they are the one who are being, But sometimes when you are having questions like features, mainly interactive visualisation, then there might be a lot off details you need to know about a process or maybe data, and you are in the middle off development. But you have some questions regarding their visualization because there is some features or some change in the library's. So it's better to actually show and discuss those with your customers because they will be some changes in the final delivery as well. So feel free to approach your customers even in the middle of the development, if you have any major changes or even minor changes. One thing which is an exception to this case is if you frequently go to be a customer asking about questions, which we're not discussing the requirement gathering, then they will think that Do you are not expert? So the best way is to set some meetings. Some fix meetings with some fixing to belts may be, for example, weekly or monthly progress. Meeting a picture which we're seeing here is how things normally works. You first come up with an idea, and then you create a wire frame your customer will validate those wire frame. Those are just be PT's on some sketches after that. Once you decide the technology together with analysts and architect, then you go for the development. Basically, you will be walking on a POC. It is proof of concept off what you're going to build and wants. Your customer validates it. You will start your actual project, and once in the middle of the project, if you are doing the development, are doing the design, you will surely come off. It's on the questions, which we're not discussing the requirement face. It's always good to involve your customers and decision making processes, because the solution is actually something which the customers will be using. So whenever you feel that some decisions are important and stakes can be high, always involve your customers. And always keep that in mind that there may be some features or some questions which need to be answered by the customer in the middle of the development also and in the middle of design as well. So you need to have some preplant discussion meeting for that as well 24. 24: now coming back to some of the final questions, like which technology to select for your data visualization. Now the answer depends upon a few questions, which you need to ask to your customer. Or maybe you might be aware about those answers as well. But it's always advisable to consult your customer with this questions. Now let's see one by one what each questions being to the table, for example, how you're solution is going to be used. The main requirement, which you got is a one time requirement which will be used on the single time, like an excel are is going to be a constantly evolving requirement. Now the second question is. Who will use the solution? So this is something which we already discussed in the user experience, that you should understand your customer, who are day, what they do, what they know and where are they going toe use a solution or how they're going to use a solution. So your user technical landscape and current system configurations are also important because they decide what you can use. For example, if your user only uses I bad our iPhone or Apple devices, then it's probably best to not to go with flash. Now, if your user is actually a I E type guy now, all of the competence are Microsoft oriented competence. Then you have to be very careful and deciding the library of the framework, which will be used or deployed on top of it. Now you're uses technical capabilities is as important as the requirement because you have to understand what they can operate and what will be too much technical for them and also what is possible and within your reach. Do you have competency to work on the technology? Do you have resources? Are. If you are going to work on the technology, then can you work on the technology? Or do you need to learn because it can happen that you decided to go with a new technology which you have not much understanding about. It can take much more time than you really think to master the technology so that you can deliver some kind off productive solution. Do your customers on the final thing, which you need to consider. Is the future off your solution because in industry what happens when you give a solution which your customer is using. Then there will be someone who will be supporting the solution after it go lives. Now, most of the time it will be the same company who manages the solution after it has been made and is live but can be different company who supports the applications. So also make sure that the people who will be getting the knowledge transfer once the solution is developed from your side will be also able to do it. If they want to enhance the solution, then it will be also possible in future as well. At this point of time off selecting technology, it is always required to see how things will change and look into future. And in big industry, people are paid a lot toe. Just decide which technology to select, because sometimes you also have to see licensing and money factor from your side and your customer side off. Who is being the money for the software, which is used underneath as well 25. 25: to select the technology for your data visualization. We should also know about what are the options which is available to us. Before that. I have categorised the technologies into two main categories. One is excellent numbers and other ones applications. So, basically, if you are designing anything for your user, which is going to be used in Excel in Windows Machine on numbers in Mac Machine. So these are the one where the user is going to be opening the Excel files in their town alone computer. It don't require any server or any kind of infrastructure in place or database in place. You have to write the code in visual basics for Excel. And also, if you are going to automate things in Excel, then you have to also write macros. So these are some of the things which you need to take care while developing an excellent numbers. Now, the other main category, which is the point of focus for us now, is applications. Now, when we talk about the obligation, we need to consider many issues which we discussed previously. The additional issues which you need to consider is licensing costs, compatibility, technical visibility and your expertise. Now, if I see the application category, I think about two main sub categories inside it. For example, one is automatic Softwares, which is just out of the box software. There. You just need to get your data in, and they have some features. Are automatic tools toe output the visualization? So most of the time in automatic software's you don't need to write gold. You have to just estimize, and you should know how to use a tool. So customization is enough for using the tool, but there are certain cases where you may require toe development. Andi, this is where you can go on. Be more flexible now when we go for a custom development. If you are going toe, build some application like their application or an up then the use of JavaScript has been a very prominent. So there are some frameworks, like D tree on angle urges, even know Jess. They provide amazing visualization capabilities, and I think we already discussed the D tree. It is becoming a de facto standard for visualization 26. 26: This was a wonderful journey to understand data, visualization and all its part and how they all fit together to make a wonderful visualization which allows some of the people who are decision Maker to think clearly the beginning. We saw what is the importance of day Tom introduced ETA. Then we understood that why multimedia should be used. After that, we understood the hilarity off D I. K y, which is important to understand because it is knowledge and wisdom which the user is looking for. Daytime itself is not that religion. Sometimes after that we ready to use their experience, which plays a major role in data visualizations being destroyed, the key principles involving and user experience. And then we saw one of the key points to remember which we are going to use in our data visualization. Then we saw the important aid gestalt principles which will be always used in your data visualization. We saw in details how they are used and what they mean. Then we went inside the discussion off how to show differentiation in your data points. We saw examples and all key pawns to show differentiation. And after that we answer the big question that is which job type to be selected. We saw each important chart type in details and when to use them. We went through bar chance, multiple variable bar chart line chart, timelines, info graphics. The new went through scattered plots on Bubble Chance. He also saw pie chart on how to use it and then to avoid it. He also came to know some off the higher dimensional data charts as well. It will be used if the data sets is very large and having multiple variables, we understood the meaning off legends and charts and importance of scale. Then we understood how to work with your customers and what they expect from you when you are working in a data visualisation project. And at the end, we understood how to select the technology for your data visualization and all the options which are the liberty. And now we reach the end of the course. After taking this course, you are competent enough to take any challenge off data visualization. Maybe simple or complex. These principles and learning and understanding will help you and guide you through all the processes involved. Any project which involved state of and from our side the issue wonderful diamond it of realizing Thank you for watching this course