This tutorial is a primer on crafting well-conceived data science projects on course toward uncovering valuable business insights. Using case studies and hands-on skills development, we will teach techniques that are essential for a variety of audiences invested in effecting real business change: (1) academics looking to transition to roles applying the scientific method in a business environment, (2) business professionals looking to expand their analytical skillsets, and (3) business non-analysts working with data science teams. We will start our discussion with case studies demonstrating advanced analytics entry points (the initial impetus for the project). Our case studies were chosen to demonstrate how a project’s entry point impacts its scope and approach, and how that can diverge from the critical business drivers that ultimately measure successful data science projects. We will also show you how to avoid missteps that can lead to less than stellar results or wasted effort, with a checklist to follow to get started on the right path from the beginning! The next portion of the session will outline a framework to help you define, refine and assess value for business questions that are candidates for data science projects. Many organizations struggle with identifying and prioritizing these questions, but this step is critical to ensure your project teams are focused on the right work! Finally, we will demonstrate a pragmatic approach to frame your data driven decision making projects in an agile project methodology. An agile approach lets the project team quickly adapt, based on findings, as the project progresses. This framework helps to manage uncertainty while ensuring the project is focused on constant progress toward a stated goal.

Link to tutorial: http://www.t4g.com/kdd2017/