My company, Inquidia Consulting, is currently engaged in/completing several predictive analytics and data science projects. While we distinguish PA from DS, there's often not a hard dividing line between the two with our customers. Indeed, though we demur, some now consider data science to be any application of statistical methods to business problems.
For Inquidia, both PA and DS generally involve statistics and machine learning of some sort, often “climaxing” with predictive models trained and validated on existing data. The ultimate goal is to deploy the models to make go-forward predictions in a business process.
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