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Tips for the Data Scientist: Top 5 Reasons Why Your Data Science Project Didn’t Make It and How to Get It Right the First Time (304107)*Irina Kukuyeva, Ph.D., Kukuyeva Consulting
Keywords: Data products, production, collaboration
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 the predictive model - 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.