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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/31553
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| Title: | Data Science and Prediction |
| Authors: | Dhar, Vasant |
| Issue Date: | 29-May-2012 |
| Series/Report no.: | CeDER-12-01 |
| Abstract: | The world's data is growing more than 40% annually. Coupled with
exponentially growing computing horsepower, this provides us with
unprecedented basis for 'learning' useful things from the data through
statistical induction without material human intervention and acting on
them. Philosophers have long debated the merits and demerits of
induction as a scientific method, the latter being that conclusions are
not guaranteed to be certain and that multiple and numerous models can
be conjured to explain the observed data. I propose that 'big data'
brings a new and important perspective to these problems in that it
greatly ameliorates historical concerns about induction, especially if
our primary objective is prediction as opposed to causal model
identification. Equally significantly, it propels us into an era of
automated decision making, where computers will make the bulk of
decisions because it is infeasible or more costly for humans to do so.
In this paper, I describe how scale, integration and most importantly,
prediction will be distinguishing hallmarks in this coming era of Data
Science.' In this brief monograph, I define this newly emerging field
from business and research perspectives. |
| URI: | http://hdl.handle.net/2451/31553 |
| Appears in Collections: | CeDER Working Papers
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