Launching A Data Science Project: Cleaning Is Half The Battle

This month we are going to dip into data science a bit. The same issues we deal with loading a data warehouse exist when starting a data science projects. We'll have pizza and drinks around 6pm and kick things off around 6:30pm. Hope to see you there!

Abstract:
There's an old adage in software development: Garbage In, Garbage Out. This adage certainly applies to data science projects: if you simply throw raw data at models, you will end up with garbage results. In this session, we will build an understanding of just what it takes to implement a data science project whose results are not garbage. We will the Microsoft Team Data Science Process as our model for project implementation, learning what each step of the process entails. To motivate this walkthrough, we will see what we can learn from a survey of data professionals' salaries.

Speaker:
Kevin Feasel is a Microsoft Data Platform MVP and CTO at Envizage, where he specializes in data analytics with T-SQL and R, forcing Spark clusters to do his bidding, fighting with Kafka, and pulling rabbits out of hats on demand. He is the lead contributor to Curated SQL (https://curatedsql.com) and author of PolyBase Revealed (forthcoming). A resident of Durham, North Carolina, he can be found cycling the trails along the triangle whenever the weather's nice enough.