Data Science 101

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: What are the differences between Big Data and Data Science – and why aren’t they the same thing? What distinguishes descriptive, predictive, and prescriptive analytics? 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 are some common pitfalls that turn good ideas into bad science? 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 end-user applications, with an emphasis on data and programming. 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 is a regular contributor to the community of analytics and technology user groups in the Boston area, writes and teaches on many topics, and looks forward to the next time he can strap on a dive mask and get wet. Todd is a Data Science Evangelist at DataRobot.

Open Data Science Conference