Corporate Data Science – Angela Bassa

Tags:

Presenter: Angela Bassa, Director of Data Science at iRobot
Title: Corporate Data Science
Level: All, Beginner, Intermediate, Advanced,
Type: Premium Training, Non-technical
Length: 47 minutes

Overview: In this talk, Angela Bassa discusses strategies of building and maintaining successful data science teams in a corporate setting. Bassa focuses on common mistakes and errors that companies making when setting out to build data science teams


Learning outcomes:

  • Teach how to deliver creative and novel solutions.
  • Dispel myths such as “Unicorns” and “Super-chickens” that hurt companies and their data science teams.
  • Help those working in the industry who want to achieve a better ROI from data science, especially those in charge of hiring data professionals.

 

Talk Timeline

 

Introduction and Overview 0:00 – 2:00

Data Science-ing In Industry 2:47 – 11:14 

  • The commercial spectrum
  • Bespoke questions <-> Production software
  • Where does data science fit?
  • Pigeonholing Unicorns
  • Blended Objectives

Building Effective Data Science Teams 11:14 – 26:36 

  • Unicorns Don’t Really Exist. You want to have a well balanced team.
  • Dynasties and Intellectual Inbreeding
  • Mirror-tocracies & Survivorship Bias
  • Building a Large Reality
  • There’s More Than One Way to Skin a Cat
  • Mistakes Might Have Been Made
  • Common Blind Spots
  • Multiple-Objective Decision Analysis
  • How To Interview For The Best Intersections

Proper Care and Maintenance of Data Teams 26:36 – 37:47 

  • You Have an Awesome Team in Place, Now What?!
  • Data Team’s Responsibilities
  • Why Do You Have To Care?
  • “Any sufficiently advanced negligence is indistinguishable from malice.” – Deb Chachra
  • It’s Not That No One Cares
  • When Should You Start
  • What You Expect
  • What Actually Exists: A System Optimized For Not-Analysis
  • How to Care
  • Documentation is How You “Program” A Business
  • What Should You Document
  • What Will You Want to Know About the Data?
  • Having Junior Team Members is a Great Forcing Function!

Q&A Portion 37:47 – 47:08 

 

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