 So, hello everyone, this is Shreyas Bhargave, I'm part of CABGEMINI, India, working with the group industrialization and automation, focusing on intelligent and cognitive automation. Let's talk about why it is important to study data science and why it makes sense to be in the field of data science today, especially for students who are probably graduating and getting ready to come into the industry very soon. So, one of the main reasons is today data is everywhere. It's like the new currency, it's like, as they say, the new oil and everything is data-driven, especially with digital revolution and everything is getting digitalized. So, it's a very opportune time and to get into this particular field and at the same time it's also very important to be able to use the available data, analyze it and leverage it or bring it to the benefit of whatever different use cases we need to find. And there is a larger angle to it that this is also the third wave or the third coming of artificial intelligence. So, the first two waves in the early 50s, late 80s and all, as compared to that, this third wave of AI is the most promising and that is thanks to the easy availability of data, the liquidity of data, availability of data that we see today is kind of one of the major fueling factors for AI again coming into prominence and also the scalable compute power thanks to the cloud and other environments that is making the data crunching, data analysis very much possible into the hands of the individuals. There is also another factor that the algorithms and tools that kind of facilitate the analysis of data, visualization of data, all that is probably getting advanced at a very rapid space. The maturity level, the ease of availability of these algorithms into the hands of the data analysts or software developers and IT professional, it's becoming very easy. So, it kind of creates a very conducive environment to someone who's getting into this area to be able to kind of go deep and come out successful out of it, use the data and the tools to the maximum effect. No wonder that is the reason this is the single hottest career field today, if I can say so. So, definitely there is tremendous demand. At the same time, there is a big shortfall of the available skills as well. So, definitely for students who are getting into their final year, graduation years, coming into the industry will find it very lucrative that this is an area where definitely a lot of opportunities, good remuneration, good pay scale available if you're definitely going to make a success in this particular area. And then it's not just companies. You have from companies to governments, get from sports to even politics, medical sciences to finance, retail, every sector, for that matter, every sector is seeing tremendous amount of opportunities. And they really want to capitalize on data. And for that, there is definitely a need and the current birth of good data science people. So, while when we say data science, it need not be restricted to like only machine learning or deep learning or whatever. Yes, that is one area. There are other aligned areas also, which if you find it to your liking, you can make it big in that particular area also. And that does come under the ambit of data science, artificial intelligence. So, be it advanced data analytics, maybe something to do with computer vision, something to do with speech as in voice, the general text mining, big data analysis, as well as visualization. So, there's also a big focus coming on how good you're able to visualize and present the data. And that is also a niche specialized area in itself. So, these are different areas where you would probably find a lot of scope going ahead and see that the demand keeps rising in these areas as well. And to top it all, there are very focused specialized streams, be it education, professional courses, platforms, including self-study platforms and the MOOC platforms and all. There is so much of choice available today that you can easily probably hang on to one or two of this. And if you're especially good with numbers, have a liking for maths, have a liking for problem solving, I think there is no stopping. In fact, sky is the limit in this particular area. And you don't really have to come from only an engineering background. You come from sciences, you come from commerce, you can come from economics and still kind of get introduced to these different domains under the data science, artificial intelligence and you can definitely find a very great scope for it going ahead. As far as the various roles or opportunities that are considered in this particular space, it could start from being something like a junior data analyst, senior data analyst. And when we say data analyst, it probably means that, okay, you're able to read data, understand data, you're able to kind of query the data and kind of bring out certain results that could be of interest. So also the process of data cleaning and like the basic visualization stuff. So being good with something like even SQL queries to be able to query data and bring out the right results is also a good starting point. And then we can get into the more data engineering role or data scientist role. So data engineer typically would probably be a little more focused towards software development rather than pure number crunching or data analysis. So you may kind of build skills with, say, R or Python and some of these very popular data science languages. Or you could also get into more advanced SQL skills or using tools such as Apache Spark or Splunk, which kind of gives you ability to work with a large quantity of streaming data as well. And then if you kind of think about the data science role, it is more about being very capable and confident on the data, what I can say, creating data models, strong on machine learning, advanced statistics, finding trends and patterns. So definitely, you need to be good on the languages such as R and Python, but you may also want to kind of focus on the various different algorithms that are available, which algorithm is suitable for which scenario and having a good understanding about that. And then also be able to leverage and use the cloud services. So while we keep saying that R and Python and some of these toolkits are a good starting point, we cannot avoid what the cloud platforms are providing because it becomes a very easy environment for deploying the large scale data science solutions. So when we say cloud platforms, there are Google, Microsoft, Amazon, all these platforms not only provide the cloud infrastructure, but they're also providing large scale compute power and very advanced or mature, stable platform-based services. So those platform services in the area of data science, the algorithms and all are very easily available at a large scale deployment, for a large scale deployment. So the cloud platforms become a very key aspect as well. And then so while you can look at from data analysts to a data scientist, there will be very different specialized roles as well. You could become like a machine learning specialist or an engineer, there could be areas such as data warehouse architect and data warehouse design, which are allied areas under the whole ambit of data science. And then always the reporting visualization of the business intelligence side of it, where you could possibly look at tools such as Power BI, Tableau and some of those tools. And going ahead, there is a different career path that is now getting formed. And as you go ahead, there would be security, data security and those kinds of angles coming in. So you would probably have organizations having a data security officer or a chief data officer who knows. I mean, there are organizations today who have started having a post called Chief Data Officer, just like Chief Executive Officer, Chief Finance Officer, you have a CDO. So right from a junior analyst to a straightaway up to the Chief Data Officer, there are n number of roles that are going to open up or opening up in this whole stream. So clearly, like I said, sky is the limit. Thank you.