Decision Analytic Principles For Data Scientists | Data Science Predictive Modeling | Simplilearn

Today, the most valuable companies are those that successfully monetize data. As the global store of data dramatically grows, pressure increases for data scientists to improve at delivering value. Data scientists with this skill will enjoy an advantage over those who lack it.

Because predictive models derive their value from the decisions they inform, data scientists should apply decision analytic principles (e.g., the importance of framing, taking a value focus, pursuing the elements of a high-quality decision) as naturally as they apply data science principles. This talk will demonstrate decision analytic principles on examples of decisions that data science teams encounter, including those made by the customers of their models.

About Brad Powley:
Brad is an engineering lead at Salesforce, where he conceptualizes, designs, and builds data products that deliver insights to customers through machine learning and distributed data processing. Before joining Salesforce, he was a strategy consultant, helping clients make good decisions in industries including energy, life sciences, real estate, IT, and manufacturing.

His engineering experience spans the disciplines of mechanical design, manufacturing, systems, and software development. His Ph.D. research focused on Decision Analysis. He is particularly interested in the intersection of artificial intelligence and human decision making.

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