 So, right now I am working as creative lead in UX practice in Photon infrastructure. Before that I have worked in Infosys for 4 years now as user experience designer. So, that was my background. So, I have I mean experienced quite a quite times that in order to design those interfaces which involved a lot of data is a challenge or is a kind of challenge for us. So, how many of us experienced that? Raise hand. So, if you are having I mean a lot of data in that screen because if we talk about design it should be I mean mixed up of content, form, behavior everything right. So, what if we are going to have only the content right. So, let's deal with that problem and move ahead. So, this if it is I mean not working. So, yeah. So, this is a certainly a screen talk of 15 minutes. So, in that we will start with an introduction short introduction and how do I see. So, how does it matter is that if we look at some object. So, there is some process going on in our brain which includes our eyes as well. So, we will see that that how do we see then how to design data visualization, what the design is known is what process we should follow. And because if we are talking about data visualization and if we are not talking about tufte that would be a crime. So, we will see the thumb rules. Tufte is a very known name in data visualization field. So, he is considered to be a kind of father in data visualization. He has done a lot of experience to his life. So, we will talk about that. And lastly we will talk about innovation because I believe that following the principles can help us in finding a solution. But, setting a new direction will always need innovation right. So, we will talk about innovation or what innovation we can bring in in data visualization. So, guys you are able to hear me or is there any issue whether I can move forward. That is not a problem ok. So, I mean I would like to start with that there has been a belief that everything is made up of items and it is still a belief that everything is made up of items. Gradually that belief move I mean people tried or have started to change their opinions and not only thinking about items, but we have started thinking of in the form of bits right. That is everything is made up of 0 and 1 right. So, bit has resulted data that is information data. There is slight difference between information data, but yes we are in data edge. So, moving ahead that we started on manufacturing aid and we have come up to data edge. So, now let us welcome data edge and let us welcome ourselves in data edge. Yes, so this is considered to be one of the oldest data visualization which was done by Vinad. So, Vinad is also a kind of very known name and he has done number of experiments throughout his life about data visualization. So, what is this diagram shows? Can you even guess? There is nothing to read in this. So, having a smaller points is not a problem. I think it shows the army movement. Yes, correct. So, it is actually Napoleon's march towards Moscow. So, his army started from there and reached towards Moscow and it has shown that during the retreat the size of the army is decreasing which is shown in the black lines. Okay, below that we are having dates and temperatures. So, the reason of decrease in the numbers of the army is fall in the temperature, sudden fall in the temperature. So, this is shown to just tell a story. Okay, so the primary purpose of data visualization is telling a story. So, using this particular map, he had shown numerous things. The size of the army, then it actually transfers to the map which shows that how it went to Moscow and how it came back where some of the troops were not returning back actually. And along with that temperature and the dates like on what date, what is the size of the army. So, there are numerous dimensions of data we had shown here. So, we will talk more about this example later. If you just have disappeared because of magic or something. Okay, so right now what do we have is that we are having pool of data. I mean we are all surrounded with numerous data on Facebook, Twitter, blah, blah, everything. There are numerous data. So, what is the need is that we should be able to somehow filter that data which is very relevant for us. Or it clearly depends on the user. So, whatever user wants, only that needs to be shown. So, from that pool of data, some process should happen. We will talk about that. So, that it will show us or some kind of representation would be given for the user. So, that we can make the decisions which can make the user happier. So, this is the type of steps. So, this is the way in which we see it actually happens in three stages. So, first stage is rapid pilot processing. And this is combination of two stages. So, in rapid pilot processing what happens is that we actually when we pass our glance through the object. You have any question here? So, whenever we pass our glance on any object. So, what happens is that we look for orientation, color, texture and motion. So, these are certain attributes on which I mean we look for these attributes instantly without any attention to our brain. See, this happens even before paying some attention in order to look at an object. And pop out the hits are again part of, you can say, emotion and edges and reasons to talk about that. So, that is in detail. And thereafter I mean it is slow signal bone directed processing. This happens in our brains. So, after that this is I mean purely attentive process that goes on in brain. So, in order to see some object. So, take away from this slide is that orientation, color, texture and motion are few attributes we look for without any attention from our brain. So, I have shown here that color, size, position. So, size and position is combined forming orientation. And the other one is motion it is not flickering against. So, it is flickering yes. So, if let us say that we are going to have 50 best squares, black squares here. I mean on the slide only 50 black squares on the slide. And one of them is flickering like this. So, will it grab our attention? Of course, right. So, this is pop out effect. So, this is also part of motion. So, if some element is having motion. So, it grabs our attention immediately without any attention from our brain. So, these are examples of those three attentive attributes. How to design data visualization. So, what are these? First one this is part of poster of movie right. So, it is volume industry. This is the reader in which this is a very famous movie. This is Chinese movie. And coincidentally we are having all these three terms which are basically the three points which we must know this in order to design for data visualization. So, what are goals? So, there are two kinds of goals. First is explanatory and second is explanatory. I will just give one example for that. How many of us have opened our bank account page? Just to see our balance. And number of times we log into our account and see our balance and be happy. It is too much. It is not like that right. We have some purpose in order to see our account transactions or some purpose we are having. Then we log into our bank accounts right. Yes. So, that is kind of explanatory because we are having questions. Let us say that we can have a question that on certain day how much amount I have taken out right. How much amount I have withdrawn from my account. So, that can be question in our mind and then we log into our account and see that this much amount was there. And this was the transaction reason. So, the question is there in our minds even before seeing that data. So, that is called explanatory. When we call explorative that is we are not having any question in our mind. We are looking at data. We are creating questions in our minds and then getting answers for that. So, there are basically two kinds of goals. In explorative we explore the data. We for example if we see applied factor across the globe between cities. So, that is a kind of explorative data. We do not have any question for that right. So, these are the goals we should identify for which we are going to design. And then the reader who is the user who is using that data that also matters. And even in reader context and identity these two attributes of the reader should be noticed. So, what is context? Okay. I will first complete it because this is a screen talk. Yeah. So, data. Now how many dimensions of data do we have which we need to visualize that also goes that will also go into the master. First example which we show that was minutes map. It was having six dimensions of data that date, temperature, size of the army, size of the army will be achieved. Then the reason for the death was blah blah. So, these are dimensions. So, in order to show the data visualization correctly we will have to fix on that how many dimensions we are going to show. And it is going to show I mean maximum number of or a minimum or an optimal number of dimensions of data in a certain visualization. So, this is a cheat sheet which was designed by Andrea Baila. Okay. So, this is very helpful in order to determine which particular data visualization we should use. Okay. So, this is having that what would you like to show. Whether it is a relationship because we may show a relationship between two variables which is also visualizations. Whether it is a distribution, composition, comparison. So, a number of data visualization and it is types. This is very useful chart which was designed by Avela which we can use for data visualization. Now we are casually moving towards Tuftes principles. This is first principle of Edward Tuftes. So, the principle says that the ratio of data and the ink should be very close to one. So, what does that mean is that we are having data. So, in order to show that data we will be using inks. Inks can be used to show the data, to decorate the data, to show the related pictorial views which are also corrected with the data. So, how it will be put to show as much as data as you can through that ink. So, the uses of ink and visualization of data, this ratio should be very close to one. So, this will be an indication of good data visualization. This is the second concept which says that number of data matrices. These are the number of data matrices and it is getting close. So, I mean this is just the numbers that it depends on how many numbers of data you can show in a certain area. That is a good indication of the data visualization again. And this is an image which shows that this guy is sitting in the middle of the sea. So, here you cannot imagine that this is a sea in which that guy is sitting. So, with the loss of frame of reference we can lie to someone. We can easily lie to someone. So, if they are going to show some data, we should always have a frame of reference. So, this was the example and interactions are the very important part of data visualization. And this is the last one, this whole discussion. Thank you very much.