IT professionals who are looking to transition I think these a da does provide a very good and Siri career path because there is a fair amount of technology involved in all of them and since people coming through and tech background have that background they have to learn new skills nevertheless but it is still easier to think of a transition from IT to analytics or Big Data then let us say from IT to marketing or IT to operations and so on but that doesn't mean that analytics is an opportunity this is only for IT professional you know today analytics is used across all industries from the food you know that is manufacturing calls having its global analytic center in Chennai in India to banks that are using analytics heavily to e-commerce companies which even the entire recommendation systems or marketing or you know CRM is completely business - telecom companies you know companies do they still have huge analytics teams or manufacturing and energy company so the more.

I think about it the you know the more companies that you heard or you in practice with as customers our clients have either already have invested in analytics or are investing and analytics is the backbone of their business operations and we see that you know even in our own candidate mix you know our own candidate profiles through our programs and you see that there's always a blend between people from IT sector to banking to analytics to consulting to manufacturing and and more healthcare in fact I've not mentioned else yet here but health care is a major sector that uses analytics right so analytics today point being that analytics cuts across centers it does not is not specific to an industry like ITR or a roll circus technology it cuts across industries across sectors and provided you can learn this well and you can pro you know you can kind of crew up your competence or demonstrate your skill sets irrespective of the industry that you are in but you know you will have opportunities to grow and build a career so talking about correct you know careers in data science which is what the topic was about there are broadly full different career tracks you know that exist in did the science.

So data science itself is a is a large and fuzzy term which is an intersection of analytics technology big data technology you know and computational technology and mathematics / statistics but broadly the way companies define data science is the whole field or domain of you know converting data to the lesson sites so within data sounds tracks or career tracks there are four broad tracks at you see one is business intelligence and reporting which is all about working with historical data and providing dashboards and visualization reporting it the other is on big data which is working with large volumes of data and building a technical architecture to finally have to first of all manage this data that is in just a clinic model it and then to implement analytical solutions on those large volumes of data the third is machine learning machine learning is a move with specialized field which is highly computational and highly algorithm driven and it is nascent but it is something that has massive potential and that we also grew and the fourth is analytics which is all about converting data to decisions or you make all inferring decisions or insights from data right.

So they're essentially what happens is that people working in the analytics part of companies they're actually working on are they using the data layer that is there and when to build models and force fit this data into these models and try to improve accuracy and thereby applied business context and Dragon cell needs so broadly you know at a at a superficial level if you look at a these are the four broad career tracks that exist VI in reporting those intelligence reporting something that has been around for a while in about two decades and it's it's a stable it's a stable track where there is growth but it has been at that stage for the past many years big data is analytics and big data of both extremely high growth opportunities where a lot of industry is moving to a lot of jobs are moving to a lot of high pings of someone going to and these are where most professions are trying to obscure them themselves machine learning is a nascent field right now but it has a potential for the future and machine learning even as it comes up it will still be very very computation which means it's primarily computer science driven with a lot of algorithms.

If you are extremely good at you to know computer science and computational methods then machine learning is a track that you can locate for the sake of this discussion I would be taking up analytics and big data as the two promising principles clearly these are the two biggest career tracks that exist and over the next few slides what I'll try and do is dissect each you then try and understand you know what kind of a very attraction you pursue what does each of these career tracks mean and you know what is the best way to go about it so let's take a closer look it's business analytics versus big data so the left hand on the left-hand side of your screen you see business analytics and on the right hand of your screen using big data.

I will take them point by point I'll try and help you understand at a broad level what does be single takes me and what is potato mean you know what kind of skill sets do people need to have what kind of roles exist and what do people really do in these shows right so let's begin with the comparison so business analytics as I mentioned is about deriving business insights from data right so here your primary skill is you know working with data to infer positions to infer the business decisions that are that are that can better applicable or that can be executed right whereas in big data it is about implementing analytics algorithms on large volumes of data you know so basically being able to transact on large volumes of data which means it is primarily a technology-oriented role right where you're building technical architecture to work with large volumes of data and implement analytical solutions on large volumes you take business analytics needs a mix of business understanding domain knowledge.

You know these are very important because ultimately you know what what I have seen is that most people kind of lose sight of the fact that analytics is mostly a service like your at the end of the day you were still applying analytics to a business problem and what is going to come out of it is also a business solution right so you cannot apply analytics in isolation you know and separating business so hence to be good at business analytics or to build a career in business analytics mean it has an understanding of the business of how businesses work you need to understand the domain what domain means is that you know if you are trying to you know to solve the problem in pricing or solve the problem and segmentation or solve the problem in churn you need to understand marketing as has a domain you need to understand that because that is where that problem is going to be solved.

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