Abstract: The past few years has seen broad cross-industry adoption of data-driven decision making. Concepts like big data, artificial intelligence, and data science have taken center stage. Business Intelligence companies like Tableau command multi-billion dollar valuations. How does this evolution fit into the life of a decision maker? The data science process is similar to the decision analysis process in many ways. This workshop is designed for the decision maker who wants to understand data science and learn how to incorporate it into their daily work. Participants will get an introductory understanding of the data science philosophy and how to integrate data focused techniques into decision models. Using popular tools like R and Python applications will focus on the hands-on creation of decision models informed with data science methods. Participants can expect to work from raw data and deploy a simple decision support dashboard. No previous experience in Data Science is required.
Bio: Alejandro is a Ph.D. Candidate at Stanford University and Professor Ron Howard’s last doctoral student. His research focus is on approximation of multi-attribute utility functions with the use of thresholds and the implications that this approach has on the creation of better decision support automation. He is passionate about operationalizing decision making within organizations in a way that creates an “alive process”. Before coming to Stanford, he was an Operations consultant and an Operations Manager for several manufacturing businesses in his native Venezuela. He has led large teams in delivering results in operations rich fields such as food and heavy metal manufacturing.