Faculty Spotlight: Maytal Saar-Tsechansky

Maytal Saar-Tsechansky

Having received her Master of Science at Ben-Gurion University in Israel and her Ph.D. at the Leonard N. Stern School of Business, Dr. Saar-Tsechansky considers herself a computer scientist in the business industry. Her primary focus concentrates on machine learning and data mining methods for data-driven intelligence and decision making. “There’s so much innovation, so much excitement around this,” she said. “It’s not just hype; it’s a real fundamental change in how things are being done.”

Throughout her career, Dr. Saar-Tsechansky has been at the forefront of this data-driven revolution. During her time in Europe, she researched how predictive models could help power grids become more reliant on renewable energy. While in Israel, she worked with local startups to use the algorithms she developed in her own research to help find solutions to the challenges faced by the cyber security industry.

This global experience has helped Dr. Saar-Tsechansky demonstrate the applicability of predictive modeling throughout a variety of industries. In her current class, Predictive Analytics, she uses her own innovative case studies to provide executives with a better understanding of the broad landscape of predictive modeling. She hopes participants leave her class with a stronger grasp on the context in which predictive models are used to inform decision-making.

For 21st century executives, predictive modeling is a fundamental and crucial method for decision making. Dr. Saar-Tsechansky firmly believes that all executives would greatly benefit from this technology. “There is almost no industry that I can think of today, all the way from entertainment and sports to military, where these techniques do not apply to improve things or even transform the way things are done.”

Predictive Analytics – November 2-3, May 10-11

Evaluate data-driven business intelligence challenges and tools, such as data mining and machine learning techniques. Apply data-driven intelligence to improve decisions and estimate the expected impact on performance. Discuss data-driven business intelligence challenges and tools like data mining and machine-learning techniques.

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