Should a data scientist know big data hadoop?by Atul Jaiswal Blogger
A data scientist could do well to understand the way the MapReduce programming paradigm functions. This permits you to do exactly the exact same job in less time, or even considerably more work at precisely the exact same moment.
The part of interest in Hadoop will be MapReduce
If you are visiting the sphere of big data and Hadoop today you are lucky because there currently exist several off the shelf implementations of these calculations that you want as a information scientist who are composed for Hadoop along with other programs such as Apache Spark. It's possible to use one of many data science libraries on the market written for these programs.
Though you ought to be able to receive some excellent answers from those tools without digging too much to the implementations, then you might still wish to find out a little about how they operate since from the sphere of large data, execution is more significant than everbefore. And not only the platforms however also the algorithms beneath techniques such as SVMs, and k-means.
When it doesn't make a difference
In the event that you were choosing between two different algorithms assembled into something such as R in your notebook, state, naive bayes versus k-nearest neighbors, then it may not make much difference concerning time to train and confirm your model along with the opportunity to use it to new information. However, in the sphere of big information a little difference in calculations' runtime can mean the difference between obtaining and response in a couple of minutes versus hours or maybe days.
Another cause of paying attention to the advancements in large numbers is not only is it information science essential to develop a page of amounts that may not make sense when only considering them into a clear model from which you are able to acquire helpful predictions; today it's the situation that you may have the type of information which, in the event that you just had a page value, could be understandable by simply looking at it, but you have a lot of it and also you need to turn into information science practices to summarize and make sense of everything.
Examine the case of a journalist if seeking to make sense of a trove of records which have only been published. Each is readable but there might be tens of thousands of these. Data science methods in the context of large data are the sole tractable means to find a feeling for the information inside such scenarios.
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Created on Aug 9th 2018 08:16. Viewed 192 times.