 My name is Dr. Prasad Ramnathan, I lead the AI ML COE within the cloud and infrastructure services business unit of Capgemini. I have a significant amount of interest, not only in data science and machine learning, but in general, MIS and the complete data transformation, that is the digital transformation that is happening across the industry is what I provide my assistance to within our organization and even outside. So regarding the query of how data science is important and why students should take it up as an area of active study, I would like to discuss this in three sections. First I would like to give my perspective on what is data science. I would like to indicate some of the key reasons why it has gained the importance, especially in the last few years and then talk it off with a few examples of how it can be applied across various industry sectors. So first of all, what is data science? The goal of data science in my opinion is to convert data into actionable information. This act of converting data into actionable information essentially follows certain steps. First we need to convert this data into information, information into knowledge and knowledge is then transformed into some kind of a decision on which actions can be taken in order to improve the business outcome. So in effect, the data scientist role sits at the intersection of three areas. One is obviously the data crunching capabilities which requires a lot of mathematical and statistical skills. In addition to that, while applying any kind of data crunching capabilities, it is important to apply computer science techniques in order to do this crunching in a faster and better manner. And ultimately all of this is going to need some kind of a decision for which domain knowledge is also important, that particular industry sectors domain knowledge is also important. Thus, the role of a data scientist sits at the intersection of mathematics statistics plus computer science plus domain knowledge and thus a successful data scientist is actually a unique blend of all of these skills. Now coming to the second section of why data science has gained importance recently. While there are many reasons, I would like to focus on three reasons and those three reasons will obviously make it an automatic choice of why data science has to be focused on as a field of study today. So first and foremost, as the name implies, data means that is the activity or that is the input on which the data scientist is going to work on. And the availability of data itself, especially in the last five to seven years is one of the key reasons why data science has bloomed in the recent years. And why has this data become available? That is because of the availability of cheap, low cost sensors. And where are these sensors coming up and how are these becoming available in a low cost manner? It is because primarily of the mobile applications or the mobile devices itself. All of our smartphones have at least a dozen sensors like your gyroscope, your GPS, your accelerometer, your touch sensor and so on and so forth. Not to mention the biggest sensor of all, which is the camera itself, which is actually creating a lot of unstructured data on which a significant amount of analysis can be done. So the data availability and the advances in big data as a field itself are one of the key areas why data or the area of data science has flourished. The second area of importance of why data science has flourished is the availability of compute power at an affordable cost. And this has been enabled by the advances in cloud platforms. So in the computer science area, when we learn about cloud, the ability to access a large amount of compute power at an affordable cost itself is a key enabler. And the third area is the democratization of the machine learning and analytics algorithms. What I mean by that is that once upon a time it required a PhD in computer science or AI to actually apply many of these algorithms and today many of these algorithms are available for citizen data scientists, which means with a relatively lesser amount of training, many of these complex algorithms can be applied and therefore there is a significant surge in the ability to apply these algorithms for industrial purposes. Thus the data scientist is in a unique position to work at the bleeding edge of such technology enablers like big data, cloud, machine learning and so on and so forth. Now how is this applied in various industry sectors? I just want to top it off by talking about a couple of examples. If you just take the area of financial services, clearly algorithmic trading, high frequency algorithms are now enabling people to actually do trading without necessarily having to apply their gut feel or instinct or making it a huge lottery based approach. So the data driven transformation of the financial services industry itself is a significant enabler that we can talk about in the context of data science. Retail industry specifically online retail is something that all of us have seen in the recent past and how data is used in order to make recommendations of purchases and how that in turn is driving the consumer behavior is practically known to all of us. While these are a couple of sectors that I touched upon, even areas like sports where whether it is cricket, football, baseball, each of these sports have now got analytics people who are analyzing every move of every player and their opponent to discover what are your strengths, what are your weaknesses and how players can actually predict ahead of time what the opponent's move will be in order to actually take action and make a winning move. So whether it is traditional industry sectors or even sports, the application of data science cannot be understated and therefore all of these factors will enable us to create a new breed of data scientists who can actually transform the complete industry sector going forward. Thank you.