Foundational Methodology for Data Science

In 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.

There 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.

As data analytics capabilities become more accessible and prevalent, data scientists need a foundational methodology capable of providing a guiding strategy, regardless of the technologies, data volumes or approaches involved.