You can build your own models: Why you don’t need to be scared of doing your own data science

There are two ends of the “AI” spectrum that are often presented. On one end, AI is going to solve the world’s problems one slide deck at a time. On the other, a PhD physicist will give you a “quick” run-through of a four-hour deep learning with tensorflow in Python tutorial. In this session, we aim to land right in the middle of those two and provide a layman’s view to getting started with data science and machine learning. Almost everyone has data and problems, but many don’t have the expertise in technologies like Python or R to feel confident in getting started with machine learning.

In this session, we will aim to help you better understand the concepts used in machine learning, how to set up problems, how to analyze and interpret your data, and finally, how to build models that can drive business value without ever needing to know Python or R.

Speaker Bio

Keith Moore

Keith Moore is responsible for the development of the Darwin automated model-building product. He specializes in applying advanced data science and natural language processing algorithms to complex data sets. Moore has multiple patents in the space of data science automation software, and has been recognized by Hart Energy as an influencer in the energy innovation space. Moore previously worked for National Instruments as a data acquisition and vibration software product manager. Prior to that, he developed client software solutions for major oil and gas, aerospace, and semiconductor organizations. Moore has served as a board member of Pi Kappa Phi fraternity, and still serves volunteers on the alumni engagement committee. He graduated from the University of Tennessee with a B.S. in mechanical engineering, and serves on the alumni board of advisors for the Austin, Texas area.