Let's say if you were you know working on healthcare and I'll teach you to understand health care as a domain that is all French learn if you need to understand basic [ __ ] finances domain so business analytics Snee is a mix of business understanding domain knowledge a strong analytical knowledge whereas when it comes to Big Data you need to do well in big data rules and to proceed or progress in between two careers you need a solid understanding of Big Data technology start and the analytical techniques that you need but Big Data technology stack because that is the architecture that you want to build or you want to manage or you go green hands the is going to talk to and work with these large volumes of data you need analytical attend you need to understand analytical techniques is ultimately what you're doing is you're implementing you know the model solutions on these large volumes of data so you can't do that without understanding these algorithms on these techniques.

So hence Big Data career-track needs you to understand or need you to have a good understanding of both these areas the typical skill sets that companies look at for the analytics includes data mining you know predator modeling machine learning in terms of tools it's our SAS Tableau visualization is a key skillset and of course domain applications you know how well have you solved or how much have you solved problems that exist in the domain when it comes to Big Data the kind of skill sets that people look for primarily the Big Data technology stack which is her do big high spark Python are right so how well were to do with these tools and technologies how much have you work with them how what kind of data sets have you large volumes of it I have your work drawn how many solutions are you module that so on beyond that machine learning and multi little things it also and also needed when we come to roles you know what is important to understand is that career opportunity exists on both these sites you know.

So it is not as if one has more opportunity than the other if these technology companies will have a lot of opportunities and big data and also opportunities in personality analytics this is analytic they said is not just restricted to take to take companies those roles exist across the board so if you look from a career you know from a career opportunity perspective both of them are replete with opportunities and you can look at the kind of roles that people have moved to and I've taken these examples from our own programs you know what are the kind of rules that our alumni have moved to by doing these programs so depending upon your experience in business analytics you may start off as a business analyst or the senior business analyst or data analyst and then you can move on to more functional management tools which is an analytics manager or a consultant or a data scientist this data central again catching a lot of traction right now or even leadership roles just see and so on in P data it's basically you are primarily a part of the engineering team so you begin off by being a big data engineer or the data engineers received senior software engineer or a big data technology specialist then as you grow you become a Technical Architect or order data architect.

We also had our alumni transition to senior roles VP technologies so I hope you know this kind of gives you an understanding of the distinction between the two in terms of what does each area mean what kind of a prerequisite do you need to have in terms of understanding or what should your intonation be what kind of skill sets you want to build you know if you're going down either of these tracks and finally what kind of roles can you a spy for the question is you know which is the right career track for you and I think that completely depends upon your background and what is it that you really want to do right because a career doesn't spend a few years the career spans a few decades so you need to be sure in terms of what is your interest right what do you want to stay in the technology track right or do you want to go into the functional track or what are your skill sets and strengths and on the basis of that is what you can make this decision so when you look at business that all it takes you know business analytics primarily is a good fit for people who aspire to be in managerial roles in analytics or techno functional roles and attics techno functional roles are route system analyst and so on where you know this there is some amount of Technology and tools that you need to know especially R or SAS or tableau but in the same time you need to have a core competence in analytics as well the thing about business analytics is that these opportunities exist across industries and companies.

So you won't be tied down to one industry and you know you need to have if you want to do well in this field you need to have an interest in the business side of analytics so you have to be extremely strong on inference and analytical capabilities to Big Data this is primarily suited for professionals from a technology background you know so it is hard to build a career in Big Data engineering if you're not from a tech background so I think that is a plea that is an important prerequisite secondly you should have a strong interest in programming that if you are not already very good at it then you should scale yourself to become connected and again to sit is Python whether it is Python or whether it is our you know these are not hard programming languages to master but at the same time one needs to have the interest in inclination to learn these things just like in business analytics you need to have an interest in the business side of analytics for big data engineering or big data analytics track you need to be interested in the technical track within analytics companies because that is the kind of track that you will pursue opportunities here largely exist in tech companies and technology rows within I'll take this company so an interesting anecdote or an interesting observation.

I can share is that I'm sure all of you or most of you would have come across these you know big numbers from companies citizen forces or called the central easy as where they talk about fleeing of certain people or certain thousands of people what people don't completely realize is that parallel you know they're also recruiting and if you look at their recruitment most of the recruitments are actually happening on the analogies and big data track but the massive number of opportunities that exists within you know within a cognizant of PML next in charge, not Infosys where they are looking to build their big data technology teams right so it is a question of how well-poised are you or how well-positioned are you who makes the transition by upscaling Russell's to these opportunities with the companies along the sides so the same companies that are firing people are also the same are hiring people in large numbers it's just that they are hiring for different skills.

So now that you know over the the last two slides the focus was in understanding these two different areas and then by you know mentally you can make a distinction of all these different areas and career tracks are then probably have a slightly better understanding or which is the right track for you the question is you know how do you go about getting into this track right and I think the first thing that you need to understand is that you have to learn right without learning nothing is going to change it right you want to continue in the stay in same status quo getting into either business takes all big data means learning new skills and learning new skills is a heart that's the first thing I try and tell all the candidates who come to me or come to any of admissions touch points across programs is that don't come in with the expectation that learning these things whether it is Big Data stack or you know analytics is or machine learning is going to be easy these are hard things to learn and because they're hard things to learn is why companies put such a premium on those people who get to learn it and do well write it.

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