Jana Schaich Borg

 

 


 

I use behavioral economics and high-dimensional neural recordings in both human and animal participants to understand how and why we make social decisions, especially decisions that have consequences for others. Current projects also aim to incorporate social media data and performance on experimentally designed online social games to better classify and diagnose social deficits that influence disruptive behaviors. I use the insights gleaned from this research to develop interventions to decrease violence and improve social functioning in both psychiatric and community populations.

Outside of my research, I am interested in designing systems that create incentive structures to effectively bring together science, business, and public policy to implement innovative technology- and science-based solutions for societal problems. Towards that end, I participate in initiatives aiming to revise academic research models to better support cutting-edge collaboration, and am an advocate for interdisciplinary training programs that teach scientists how to apply their research to world issues, and education programs that teach entrepreneurs and philanthropists how to support structures that foster disruptive innovation in biomedical science.

Jana obtained her PhD in Neurobiology from Stanford University.

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Dec 12th 2016

In business, data and algorithms create economic value when they reduce uncertainty about financially important outcomes. This course teaches the concepts and mathematical methods behind the most powerful and universal metrics used by Data Scientists to evaluate the uncertainty-reduction – or information gain - predictive models provide. We focus on the two most common types of predictive model - binary classification and linear regression - and you will learn metrics to quantify for yourself the exact reduction in uncertainty each can offer. These metrics are applicable to any form of model that uses new information to improve predictions cast in the form of a known probability distribution – the standard way of representing forecasts in data science.

Average: 10 (1 vote)
Dec 12th 2016

In this course, you will learn best practices for how to use data analytics to make any company more competitive and more profitable. You will be able to recognize the most critical business metrics and distinguish them from mere data. You’ll get a clear picture of the vital but different roles business analysts, business data analysts, and data scientists each play in various types of companies. And you’ll know exactly what skills are required to be hired for, and succeed at, these high-demand jobs.

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Dec 12th 2016

One of the skills that characterizes great business data analysts is the ability to communicate practical implications of quantitative analyses to any kind of audience member. Even the most sophisticated statistical analyses are not useful to a business if they do not lead to actionable advice, or if the answers to those business questions are not conveyed in a way that non-technical people can understand. In this course you will learn how to become a master at communicating business-relevant implications of data analyses.

Average: 6.7 (3 votes)
Dec 5th 2016

This course is an introduction to how to use relational databases in business analysis. You will learn how relational databases work, and how to use entity-relationship diagrams to display the structure of the data held within them. This knowledge will help you understand how data needs to be collected in business contexts, and help you identify features you want to consider if you are involved in implementing new data collection efforts.

Average: 5.3 (12 votes)