Data ProDUCT vs Project
Top 5 Reasons Why your Data Science Project Didn't Make it and How to Get It Right the First Time
Strategic Plan you Need to get your Data Product into Production
Abstract:
Every Data Scientist is hired to bring value to the business and is expected to develop and iterate on data products that help the company grow. But not every data analytics project is a data product. This talk, based on 25+ collaborations with companies of all industries and sizes, will cover 5 of the most common reasons for what’s necessary to upgrade your data analytics project into a data product. You will learn:
How to better collaborate with your stakeholders
What to ask before the project begins
What to watch out for as you’re developing data products
What software requirements you should be aware of
What resources you need to have
By the end of the session, the audience will have a better understanding of the technical and organizational considerations for iterating on data initiatives, and walk away with practical advice for how to help your company get return on data investment and make it more data-driven.
Speaker:
(v 2.1.1) ASA Consulting Statistical Practice (Feb 2021)
(v 1.3.1) IDEAS Conference 2019 (Oct 2019)
(v 1.2.1) LA Springboard Community Meet-up (Oct 2019)
(v 1.1.1) Customer Data Science LA (Sept 2019, video)
(v 0.3.1) SoCal PyData meetup (Apr 2018)
(v 0.2.1) SoCal Python meetup (Jan 2018)
(v 0.1.1) Data Con LA (formerly "Big Data Day LA", Aug 2017)
Keywords:
Data products
Machine Learning in Production
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