 students welcome to the next module of introduction to data science course in this module we will see what is the current perspective of data science overall it is a data science ecosystem overall it is an ecosystem of different technologies and industries we can take advantages from it and benefit from it all these things are opportunities for individuals, business, education, telecom, manufacturing overall it is a landscape of data science the most important thing is that as we discussed in the last module data is more than 5% of social media users we know that it is more than 5% of internet users in the world it means that the growth of data is exponentially the data produced in the world is very much and the analysis of it is very little the data we store for a very long time that is just a very very small fraction or percentage of overall data that is being produced till now we have established one thing that data is very very important but to use that data, to store it, to analyze it the effort that is being made is not available yet this is the major perspective of the challenge for the world today and the data that you are growing at this time that is from 2025 till 180 zeta byte the size of it will be gone in the world which is beyond imagination 15-20 years ago even after the internet came nobody would have imagined that the data will increase with this speed but this all happened after social media and the internet of things and industrial automation after these things, the data that you will see the speed of the data being generated is so much now basically you will say that this is a rat race in which every company of the world infrastructure companies, IT companies retail companies, e-commerce companies they are all in this race that they can do their infrastructure so well so what I have just said that if the whole data in the world is 100% then in that it is only maximum 5% of the data is being stored rest of the data is being wasted and we have also seen that the world is saying that the data is new oil it means it has huge value but that value is not able to be capitalized due to the single fact that how much storage you need to store not storage but electricity because the server the storage of the server consumes a lot of electricity when the power is consumed it means that the carbon footprint is more it produces as much heat as you can it is a give and take and many companies the data center is in Europe in Siberia in such places they have installed where they need electricity for cooling because the data center if we take it at the requirement of data science it means that we need a lot of infrastructure which is not available at the moment so this is another for you as a student for your career as a data scientist this is good for you because the world is not analyzing 5% of the data so if in the coming times you will see that as the data storage capacity will increase analytical needs will increase the role of data scientist will continue to increase along with the time in this we have also seen that the expertise we need is a landscape in which there are different perspectives we have talked about the quantity of data about the capacity about storage but again overall what we need we have discussed statistics is one of the main thing because without statistics data science is nothing in fact because its base is on it if you have inferential statistics or you have statistical skills only then you can do data analysis or predictive models the data engineering we have also seen how to store the data how to clean it noisy data how to do different ready for analytics how to manage the types of data and one more important thing in the landscape of data science is domain knowledge domain knowledge means which industry you are working for suppose we are only talking about social media that is maybe common for many many industries but if you go to telecom or banking or insurance then when you have to work there then you have to have specific knowledge about that industry what we are talking about basically what you are seeing is when we are talking about any specific industry what is our business problem what is the strategy of business what is expectations of business from a data scientist or the data we have the most important thing is to understand the data before analyzing it to understand the source of the data where is the data coming from from which format which problem we have to solve which analytics model we have to present which graph we will use which dashboard we will use what kind of reports we will have which KPIs we should have basically when we combine all these things then overall the landscape of your data science we can understand it by some extent