 In this video, we will continue the descriptive analytics. In last class, I mentioned that you have to use data to represent in visualization formats, graphs or plots. But can we represent the data using tables? So, have you used tables in your study to represent your data? Or have you used the tables for your day to day purpose? Have you used the data in the table format? Have you seen it? Please list down where you have seen this data or which data can be reported in tables. After you listing it down, let us in the video to continue. So, these are examples of tables. There are a lot of that more. I am just giving few examples. I might have seen report cards. It is better represented in tables because we are not showing a trend or we are not comparing with someone. It is just a report card of one student or one particular work, one particular task. Report cards, we use a lot of tables. And if you want to compare two different values, say product A and product B and they use tables. Tables is more easier way because we can compare these two values. And in tables, we can also write text, numerics, different values in a single table. And for decision tables, like for logics to take a decision, what is the nest table? You can represent that in the table. And you saw tables in form of confusion matrix representing our results in a table. So, we mentioned that tables can be used for data representation. Still why we are talking about using graphics or visuals to represent data. So, the graphs, the main purpose is to represent large data. Table data can be shown, say 50 students data or someone can control the data, but you have say 50 students data over the last five years. This data is overwhelming, right? The too much data and how to represent this data, we are not making any inference from the data. Instead, you can represent the large data in terms of statistical values like a mean average in a graph. So, the user or the viewer can understand what the data means. So, to get a sense of the data on easy to comprehend. So, you can say this particular data trend, 2015 is doing group compared to 2016 something like that. You can talk about the data easily instead of in a table format. Also, it was a sense of data. And you can show the trend in the data changes over time. You can compare the data in correlation, like what is the correlation between attendance and performance. Can we see any comparison between these two variables? Or you can use these graphs to make inferences. Table data is good, you can make inferences, but graphical representation is easy to make representations. Also, the distribution value of the data can be shown easily in the graph or the plots or the visualizations. And do not forget that these words like a picture is worth a thousand words. So, you can have a long table, give all the information in table the user wants, but just a picture is enough to talk about all the data. So, what is this visualization? It is not started now. The first attempt to visualize session was 6000 years ago with cartography by scratching a map on the stone. So, the aim of visualization is to communicate, accurate and complicated data in a format where the user can understand easily. If you have a complicated data in table that of comparisons, lot of inferences within the table, it is tough to communicate with the end user. The aim is to communicate this complicated data, but in a easier representation for the user can get the inference easily. The idea of data visualization should revolve around three things. So, when you talk about I want to represent data in the graph formats, what are the three important things you have to see? The first is what you want to communicate. That is basically on your research question, what you want to highlight in this particular topic. The next is the audience. You may say the audience may have their own bias or the audience may have one perception. So, you have to consider the data should remove all the audience bias. This should have all the values included, all the outlays has been removed. You have to represent all the information in the graph itself. And most importantly, the data. Yes, the data based on the depends on the data, the data visualization will change. To summarize, in this video we saw what is tables and what are the visualizations. And this video is for motivating that we have to use visualizations, what to consider when you do visualizations. Thank you.