HomeUpcoming webinars

How You & Your Team Can Learn Data Skills More Effectively

Key Takeaways:
  • Learn techniques backed by neuroscience for how to learn data and math in a more effective way
  • Learn how to overcome fear of technical subjects.
  • Learn about the power of “ish” for learning and for life.
Wednesday May 8, 11AM ET
View More Webinars

Register for the webinar

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Description

Education can be the silver bullet for increasing productivity and personal satisfaction. Unfortunately, bad education is at best a waste of time and money, and at worst is demotivating. Fortunately, decades of research mean that we know a lot about how to teach well and how to learn well.

In this session Jo Boaler, the Nomellini & Olivier Professor of Mathematics Education at Stanford University, explains what we know about how to learn quantitative subjects well. You'll learn about the power of mindset on learning, the impact of physical movement and communication on understanding, and other techniques from neuroscience on how to learn in a more effective way. The techniques discussed originate from research in neuroscience and the learning sciences, but are also applicable to the world of data. Whether you are a teacher or a learner, you'll discover ways to improve your training program.

Presenter Bio

Jo Boaler Headshot
Jo BoalerNomellini & Olivier Professor of Mathematics Education at Stanford University

Jo is one of the world's leading scientists in mathematics education and is co-leading a K-12 Data Science initiative. Her research shares the best ways to learn and lead, whether it is in school or in life. She is the co-founder of youcubed, a resource reaching over 250 million students, and Struggly, an online game platform. Jo has written nineteen books on learning and life, including Limitless Minds. Her latest book is called: Math-ish. Bringing Creativity, Diversity and Meaning to Mathematics.

View More Webinars