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Foundational Methodology for Data Science

White PaperIBM AnalyticsFoundational Methodology for Data Science2 Foundational Methodology for Data ScienceIn the domain of data Science , solving problems and answering questions through data analysis is standard practice. Often, data scientists construct a model to predict outcomes or discover underlying patterns, with the goal of gaining insights. Organizations can then use these insights to take actions that ideally improve future are numerous rapidly evolving technologies for analyzing data and building models. In a remarkably short time, they have progressed from desktops to massively parallel warehouses with huge data volumes and in-database analytic functionality in relational databases and Apache Hadoop. Text analytics on unstructured or semi-structured data is becoming increasingly important as a way to incorporate sentiment and other useful information from text into predictive models, often leading to significant improvements in model quality and accuracy.

A methodology is a general strategy that guides the processes and activities within a given domain. Methodology does not depend on particular technologies or tools, nor is it a set of techniques or recipes. Rather, a methodology provides the data scientist with a framework for how to proceed with whatever methods, processes and heuristics will be

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