Why Analytics Is Key for Product Managers by Airbnb Data Scientist
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 Published On Sep 9, 2017

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Understanding how to access and interpret data is critical for the modern Product Manager. In this session, a data scientist at Airbnb taught the basics in data-informed thinking as well as resources and tools for SQL, python and R. She discussed how you can up-level your skills in interpreting A/B tests, incrementally, and when and why to use Machine Learning techniques.

Dr. Theresa Johnson is a data scientist on Supply Growth at Airbnb, a two-sided marketplace for travel. She also steps in to product manage for her team, and recently shipped Paid Photography globally. Dr. Johnson came to data science with a PhD in Aeronautics and Astronautics from Stanford University.

She currently serves as a founding board member for Street Code Academy, a non-profit dedicated to connecting culture, community and technology in East Palo Alto. She also serves on the special panel on data science interviewing and is a co-founding member of Stddev (Standard Deviation), Airbnb's employee resource group for Underrepresented Minorities in technical fields. Theresa is passionate about extending technology access for everyone and finding mission driven companies that can have an outsized impact on the world.

Chapter 1 1:25 What is data science?
Chapter 2 2:20 What characteristics should you expect from a data scientist?
Chapter 3 5:00 Data informed, not data driven
Chapter 4 6:55 Challenges that a Product Manager faces with data scientists
Chapter 5 8:10 Who are the data scientists?
Chapter 6 10:05 The data stack levels
Chapter 7 14:25 Centralized, embedded and hybrid model
Chapter 8 19:15 Airbnb principles for decision-making
Chapter 9 20:25 Cross-functional teams
Chapter 10 21:40 Leveraging data insights
Chapter 11 24:05 Machine learning vision at Airbnb
Chapter 12 30:20 Experimentation at Airbnb
Chapter 13 33:50 Unique characteristics about experimentation at Airbnb
Chapter 14 38:35 Launching professional photography at Airbnb
Chapter 15 57:10 Questions from the audience

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