Decision Science for Data Scientists
September 17, 2019
9/17/19 9:00 am PDT to 9/17/19 9:45 am PDT
The past decade has seen the integration of big data, artificial intelligence, and data science into business operations. The value of the data, models, and visualizations is realized in better decisions; and yet, decisions are about the future, which can be highly uncertain.
The field of decision science has evolved powerful tools, models and practices that guide decision makers through their choices. This webinar will provide data scientists with an introductory understanding of the decision sciences and a vision of how, by integrating data and decision sciences, you can earn a seat at the table where important decisions are made.
You will learn:
- The most important thing data scientists should understand about decision makers
- Techniques to measure the value of data science initiatives and prioritize among them
- How to frame the business question such that you gain the most value from data models and insights
- Where to go to learn more
Carl Spetzler, a decision professional, has 40+ years of experience helping top management create innovative new strategies that deal with the complexities of uncertainty and risk over long time horizons. The author of Decision Quality: Value Creation from Better Business Decisions, he frequently leads senior executive retreats, briefings, and courses on decision making and risk management. His widely recognized for his contributions to the fields of decision science and decision quality. He is the CEO of management consulting firm Strategic Decisions Group.
Alejandro Martinez, a data scientist, has 10+ years of experience in operations and manufacturing. From early in his career he has been using analytic techniques to improve business results and achieve operational excellence. He has a Stanford PhD in decision analysis under Prof. Ron Howard; his research focused on which decision problems can be automated and developed a mathematical simplification that reduces the complexity of preference elicitation. He is the co-founder and CEO of MatrixDS, a company that lowers the bar to entry for people and companies into data science.