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Seeing the future

Predictive modeling uses the statistics of data science to predict or foresee a result (actually, a probable result). This may sound a lot like fortune telling, but it is more about putting to use cognitive reasoning to interpret information (mined from data) to draw a conclusion. In the way that a scientist might be described as someone who acts in a methodical way, attempting to obtain knowledge or to learn, a data scientist might be thought of as trying to make predictions, using statistics and (machine) learning.

When we talk about predicting a result, it's really all about the probability of seeing a certain result. Probability deals with predicting the likelihood of future events, while statistics involves the analysis of the frequency of past events.

If you are a data developer who has perhaps worked on projects serving an organization's office of finance, you may understand why a business leader would find it of value to not just report on its financial results (even the most accurate of results are really still historical events) but also to be able to make educated assumptions on future performance.

Perhaps you can understand that if you have a background in and are responsible for financial reporting, you can now take the step towards providing statistical predictions to those reports!

Statistical modeling techniques can also be applied to any type of unknown event, regardless of when it occurred, such as in the case of crime detection and suspect identification.

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