Database Reference
In-Depth Information
Simulations
In addition to all the techniques we defined until now, there might be situations where
the data in context itself has a lot of uncertainty. For example, a project manager is
given a task and she can estimate with her prior knowledge that the team can per-
form the task in 2-4 hours.
Thecostofinputmaterialmayvarybetween$100-150andthenumberofemployees
who come to work on any given day may be between six and nine. An analyst then
estimates how much time the project might take. Solving such problems requires
simulating a vast amount of alternatives.
Typically, in forecasting, classification, and unsupervised learning, we are given data
and we really do not know how the data is interconnected. There is no equation to
describe one variable as a function of others. In optimization, we have the relation
well defined and we also have access to data. In simulations, we do have a well-
defined relation. But, the input data itself is uncertain.
Essentially, data scientists combine one or more of the above techniques to solve
challenging problems.
• Web search and information extraction
• Drug design
• Predicting capital market behavior
• Understanding customer behavior
• Designing robots
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