Speaker "Hari Titan" Details Back



Data Science needs Data Scientists, Data Engineers and Data Architects


There are a lot of software products and online services that import clean data and produce descent models in record time. However to get great models, obtaining clean and relevant data and deriving powerful "features" will occupy the majority of the time in data science. Data quality begins with good software engineering with descent QA, good data architecture and warehousing that requires minimal data engineering. How can we get great data from companies working in niche industries?


Nearly 20 years in predictive modeling including fraud and risk modeling on a wide array of financial instruments. These products included credit cards, HELOC, online deposits, business lines and loans, warranty claim obligations and property casualty insurance premiums. Inventor of U.S. Patent #US5745654 used to provide English reasons for a neural network model fraud prevention system.