Variance is all around us. It impacts every decision and outcome; but unless we go out of our way to envision, it often passes by unnoticed until it’s too late. In this post, Data Scientist Tony Yiu explores how failing to recognize the role of variance in outcomes often blinds us to the true state of the world.
Michael Galvin, Executive Director of Analytics at Kaplan, wrote an article for Training Industry Magazine on the most important data and analytics capabilities for today's companies. What are the top 3? Find out here.
Over the past few months, Burtch Works has teamed up with the International Institute for Analytics (IIA) to survey quantitative professionals about the ongoing impacts of the COVID-19 pandemic on analytics teams across the country. The most recent wave of results was published this week in a post titled, Have COVID-19 Analytics Impacts Peaked? Read about it here.
Here at Metis, we have a long legacy of live online training instruction. We're proud to be part of Kaplan, a pioneer in online learning, and we're also proud to have launched our live online Corporate Training offerings back in 2017. In this post, read how online corporate learning enables increased flexibility, accessibility, and more.
During his Demystifying Data Science talk, Atif Kureishy (Global Vice President of AI & Deep Learning Products at Teradata) discussed how to use AI to merge offline and online activity in order to better serve customers while staying cost-efficient in the retail space.
Models provide necessary simplifications to a complex world. They reduce real-world phenomena into a set of key features and relationships that allow us to explain, analyze, and sometimes even predict. But there is a cost to these powerful benefits. In this post, Data Scientist Tony Yiu walks us through understanding the limitations of our models and their assumptions.
We recently hosted a webinar on Exploring the Adoption of Python in the Workplace, during which our team broke down Python for data science and analytics, explaining what drives adoption and how companies are reacting to the shift.
Too often in the business world, we think deterministically. We plan our finances, inventories, etc. for the base case – and it usually works out alright. (After all, the base case is the scenario that is most likely to unfold.) A better way to forecast and think about the future is probabilistically - and an intuitive way to do this is via Monte Carlo simulations. Read why here.