Presented at O'Reilly Strata, San Jose, 2017

The goal of RCSA's Scialog conferences is to foster collaboration between scientists with different specialties and approaches, and, working with Datascope, the company has been doing so in a quantitative way for the last six years. Brian Lange discusses how, explaining simulated annealing and also how the project has evolved over time.

Given at PyData Chicago, 2016

As organizations increasingly make use of data and machine learning methods, people must build a basic "data literacy". Data scientist & instructor Brian Lange provides simple, visual & equation-free explanations for a variety of classification algorithms geared towards helping understand them. He shows how the concepts explained can be pulled off using Python library Scikit Learn in a few lines.

Given at the inaugural JupyterDay Chicago, 2016

Over the past year at Datascope our team has used Jupyter Notebook to do data science work for clients, present findings, generate figures for blog posts, and teach data science classes, to varying levels of success. In this talk I'll summarize what worked, what didn't, and how we think we'll use it moving forward.

At the end, I even get a little philosophical about tools.

This talk was given at the January 22, 2013 meeting of the Northwestern Machine Learning Meetup.

I talk about about why data-first problem solving sometimes misses the mark, how machine learning and the design process go hand in hand, and what that means for changing the ways machine learning is applied. The presentation will also feature some demonstrations of Datascope Analytics projects where the harmonious pair of data and design resulted in some pretty cool tools- one for tracking food trucks, and the other for helping lawyers find digital evidence.