Abstract: Feature engineering is more than simply missing value imputation, handling outlier and categorical variables and scaling numerical variables. It is an opportunity to allow a data scientist's creativity to shine and as Andrew Ngs stated, Applied machine learning is basically feature engineering. In this talk, we will show how to aggregate time series data and calculate moving averages in pandas, directly on the data warehouse using SQL and leveraging Rasgo to calculate and publish those features on Snowflake.
Bio: Andrew Engel is the Chief Data Scientist at Rasgo. He has been working as a data scientist and leading teams of data scientists for over ten years in a wide variety of domains from fraud prediction to marketing analytics. Andrew received his Ph.D. in Systems and Industrial Engineering with a focus on optimization and stochastic modeling. He has worked for Towson University, SAS Institute, the US Navy, Websense (now ForcePoint), Stics, HP and led DataRobot's efforts in Entertainment, Sports and Gaming before joining Rasgo in August of 2020.