 So let's start looking at one of the projects that has come out of this hack night. It's an interesting visualization project about drugs and deaths caused by those drugs. I mean, a lot of other aspects also, but death is definitely one common theme. So we'll just talk to the team about it. So hi guys, can you just run through what you've done? Firstly, the data set is provided by USFD authorities. So the data set is basically divided into some parts. So they're provided on quarterly basis and they've been provided for like almost say for two years. So what we did was we sampled the data for say quarter by quarter basis and we took it for quarter 1, 2012. So the data is basically divided into demographics, the outcome of drugs, how many people died, and then whether they were hospitalized or it caused any disability and things like that. So what we tried to do was we tried to correlate the demographic data with the data that we had from outcomes and events and things like that. So we basically took a sample data and it was pretty huge, but still we managed to import it into a SQLite database. So here is a bit of visualization and I think if you actually see this graph would represent the number of cases which were reported to FDA. And these are the cases which say you take a drug and it has an adverse effect on you. So it's only those cases. United States is reporting maximum which is about 162,000. And then if you see world over there is no reporting and then say every country is reporting below 30,000. So there is a stark difference between the reporting of United States and the rest of the world. If you come down this visualization actually shows the number of deaths which has happened in various age groups. So if you see from 2 to 70 there is a linear rise in deaths and then it falls off. This is an interesting one. So these are the various drug which is causing maximum death in the world which has an adverse effect and it's causing maximum death. Aspirin which is very common in India is one of the high risk medicines in the world. Actually there are some very interesting things like for one particular age group we found that girls between the age of 20 to 30 about weight 40 were drying out of drinking overdose of benedict which is a cough syrup and that was actually happening across Germany. So there are some pretty neat data. So one of the things that we actually were trying to see if people were actually dying which we were taking a combination of drugs. For example in the next visualization that we will show you we will go to the next visualization. So there will be people who report deaths out of taking vitamin A, vitamin B, vitamin C. So it's likely that they would have also been taking something else as well. So this visualization will actually show you the drugs that have actually caused death in case of a headache. So I think again aspirin is one of the leading causes of death. So we were curious to understand whether one gender death is more skewed to the other one. And we found that male and female both are dying almost by equal proportion due to the effects of drug. So what sort of tools do you end up using in this project? Many range of tools. So for visualization we have considered using R, GNU plot. So this one that we are using is actually from IBM. Okay. It's called as many eyes. They have done a lot of work on visualizing data. And they have built tools which can actually help you to represent data in really good ways. For actual manging data we used very heavy SQL. A lot of nested queries, joins, group buys and we actually tried using a lot of heavy databases. But then we after like say 2-3 hours of which is a lot of analysis on that also. We tried using MySQL and stuff like that. So basically to mine the data we used SQLite. So we imported the data to SQLite and mined data from it. And for visualization we have used many eyes by IBM. So obviously we did evaluate a lot of tools as you already said. We evaluated R and others. Okay. Great. Thank you. Thank you. Can you tell us what you've done? Yeah, I tried to visualize the marks of 10th and 12th students in Bangalore. So it's like almost like I tried to visualize like one lakh students data. I mean the result whether they have passed or failed. If we can see this each of the circle represents a school and red represents students who have failed and white represents students who have passed. So the larger the circle the more number of students. So that each school has been chosen randomly. And like I would like to give an artistic perspective. Like the colors that have chosen like white and red. I visualized it like an egg which has a potential and pass or fail are just labels and they got nothing to do with their talent or like their ability to excel. So it's like one lakh students and like more than 100 schools. So it's going to take quite some time to finish the stuff. So what's the pink color? No, it's like I added an opacity for the both red and white. So like say for example the white is bigger than the red. It generally kind of whatever hides of the red. So I didn't want that to happen. Okay. So like whenever there is a red thing rather than white it means like there are more number of failures than whatever passed students. Okay. So this dataset is for like all schools in Bangalore. North and sort of Bangalore. Okay. So that's yeah. And I use the processing to visualize the data and I got all of the data sorted school wise in a CSV. Okay. Cool. Thanks. Cool. Yeah.