Abstract: 

Curious about Data Science? Self-taught on some aspects, but missing the big picture? Well, you’ve got to start somewhere and this session is the place to do it. This session will cover, at a layman’s level, some of the basic concepts of data science.

In a conversational format, we will discuss: Why do people look to Data Science to help? What purpose do predictive models serve in a practical context? What kinds of models are there and what do they tell us? What is the difference between supervised and unsupervised learning? What should we consider when we frame our problems? How do we think about the data we’re using? What are some common pitfalls that turn good ideas into bad science? What do we need to think about after our work goes into the production pipeline? During this session, attendees will learn the difference between k-nearest neighbor and k-means clustering, understand the reasons why we do normalize and don’t overfit, and grasp the meaning of No Free Lunch.

Bio: 

For more than 20 years, Todd has been highly respected as both a technologist and a trainer. As a tech, he has seen that world from many perspectives: “data guy” and developer; architect, analyst, and consultant. As a trainer, he has designed and covered subject matter from operating systems to databases to machine learning / AI to end-user applications, with an emphasis on data, programming, and results that matter.

As a strong advocate for knowledge sharing, he combines his experience in technology and education to impart real-world use cases to students and users of analytics solutions across multiple industries. He has been a regular contributor to the community of analytics and technology user groups in the Boston area and beyond, writes and teaches on many topics, and looks forward to the next time he can strap on a dive mask and get wet.