Myths About Leadership You Shouldn’t Believe
In today’s episode, I talk with Gary about three myths leadership. We point out why they’re false and give some counterexamples.
Knowledge
Three Things to Learn About Yourself That Will Make a Huge Difference
In this 300th episode, I discuss the three things about yourself that could make a significant difference in your career and personal development. While many self-help resources exist, this episode provides practical insights into understanding yourself better.
Taking out the pain of paperwork – How sponsors and small businesses and individual experts work smoothly together
Managing paperwork can be an excruciating process for both companies and individuals.
But what if there was a way to make it easier for sponsors, small businesses, and individual experts to work together seamlessly?
Framework for estimating policy estimands
In this episode, let’s dive into the details of this innovative framework for estimating policy estimands.
Interpretable Machine Learning
In this episode, we have the privilege of interviewing Serg Masis who authored the book “Interpretable Machine Learning with Python: Learn to build interpretable high-performance models with hands-on real-world examples”.
Simulations – your most powerful study design tool
Together with Kim Hacquoil and Jamie Inshaw in this episode, we discuss the benefits of using simulations as a study design tool.
PSI Book Club – dare to lead review
In this podcast episode, Marius and I discuss Brené Brown’s “Dare to Lead”, a book that dives into the importance of vulnerability-based leadership.
Introduction to Clivoffective
In this episode, we explore what Clivoffective has to offer and how it benefits both statisticians and hiring managers.
Killer statistician
With a PhD in statistics from UC Riverside, Debarshi believes that to be a “killer statistician,” one must adopt the roles of a detective, lawyer, and storyteller.
5 Reasons for Career Failure
Today’s episode is inspired by a German podcast that delved into five key reasons for career failure and I fine-tuned this to fit statisticians.
