I think I'd miss a person upfront when somebody had asked about placement assistance with the paving program for somebody with ATS experience I'm not I don't want to dwell on that question too much I the short answer is that you know there is no such thing as placement assistance in kind of the mid carriers in this the mid carrier level anybody who tells you otherwise is you know either has some secret obviously or is misleading the reality is people in the mid carrier level with 8 plus years experience.

I don't hire based on placement the placement process if you think about it is largely for a lot of people who are either fresher so straight out of business school who need to be hired in large numbers to do something that's you know that high volume at the mid-career level these are specific roles that come up at specific times either because somebody has left or because they're in need has come up and they want experts in a particular area so there is no such thing as a placement in the broader industry but if you have specific questions about you know the kind of career support things or activities that happen within any of our programs I'd suggest that you please do reach out to our admissions team or you know just go and great learning that I am and ask that question another question I'm working in Big Data projects where I'm using spark and Hadoop or what can I focus on I'm not entirely clear about the question.

I think your questions around the choice between sparking are open and the short answer is looking a lot of companies are moving from going to this part because it's faster for a certain class of problems that are inevitably going to the other companies that are going to stay on the resulting ecosystem honestly I the question is kind of there is not like that it's a little bit like saying I'm going to start running now should I buy Nike shoes I read at shoes or fumer shoes like that is it's a little bit like that the reality is you know focus on you know learning how to run focus on the mechanics of you know being able to run longer distances a faster without injury focus on hydration focus on all of these those are the base kids shoes come shoes go similarly tech black comes from technical code the tools keep going in and out.

I mean I could tell you an answer which is you know spark seems to be faster for a larger class of problems and there's a lot of companies are migrating to spark compared to a loop but the short answer is it doesn't matter pick one and become really really good at it because ultimately you need to be able to solve real tangible problems with this and you know really good you need to focus on that on the margin spot seems more approachable than Hadoop I think because it's got you know you can write Python code on it to the top coat so so what plants programming language you already know is no longer a barrier if you are already very proficient in Java and or Scala.

We can or you know or if you already work on MapReduce you know to write MapReduce code on reduc then great go ahead with that but really you know if you you know I pick the one that you think is most intuitive to you and go with but whichever one you pick kind of become really really good at yeah so the person who wanted to learn who's finding it difficult to learn in the beginning said it isn't a program okay that qualifies things okay another question where is the scope in big data analytics and how would be the growth.
So this is a very tough question to answer which is in brief but I'll try not they bickered analytics as well, to be honest, is an amalgamation of different areas right if you think about analytics as okay so if you think about what analytics means you know people have data okay.

Let me take you a step further back people have a problem to solve they you know need to collect vast amounts of data too because they think that data will help them answer the question at hand whether its customer churn issues whether it's understanding fraud rates whether it's understanding you know why somebody's not buying your product or whether it's understanding why you know a drug is not being used if you're a pharmaceutical company whatever the problem is you think that you need a set of data you go and try to find a set of data once.

You find it you need to kind of manipulate it and do a set of analyses on it right now obviously that analysis can you know can lead to various answers you can either prove or disprove your answer or your initial hypothesis of what you were trying to test and that's given you know either your entire answer or some part of the answer now you can think about all of this there is a data collection data processing data manipulation issue that is to go back to the other question and infrastructure question around infrastructure software choices configuration and so on there is obviously the analytics question which is around how what kind of analysis do I do and then there is a business interpretation question of okay why am I what is the business and problem I'm trying to solve and what should I be doing for it and there is an affiliated area which is the visualization problem of okay.

So how exactly do I think about you know making this data transparent or making my analysis transparent and so each of these whether it's data engineering its data analysis there is the configuration and use of big data tools there is the manipulation of data on the big data tools in a distributed way there are machine learning algorithms that are statistical algorithms that are you know visualization tools and then they're visualization principles all of that together is vacant are taken in the context of how we think about it and how and hence that's how we offer the program's every one of these areas is pertinent it's relevant you will become over the course of your career an expert in one or more of these areas you will not become an expert on every one of these areas in fact this is everything that.

I've told you in the long run will typically be done by an entire analytics team but what what what it does help is for you to know what all these different areas are and how they interact with each other so it's like you know if you think about you know becoming a you know a software developer or an app developer or relative you know you need to understand you know a little bit of the product side you need to understand input as UI UX you need to understand you know app development you need to understand the API it's sometimes you need to understand hardware will be first and databases and all of that together becomes you know makes you a good app developed it understanding the business and so on now in very large teams or large implementations every one of these areas becomes a specialty there is a specialty UI percent is a specialty hardware person and so on and it's similar in big data analytics in very very small teams if you are the analysis person in a five member startup you will be in charge of everything from you know understanding what data to bring to what kind of analysis you should be doing.

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