Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK

Executive Briefing: Lessons learned managing data science projects - Adopting a team data science process

Danielle Dean (Microsoft)
14:0514:45 Wednesday, 23 May 2018
Executive Briefing, Strata Business Summit
Location: Capital Suite 17 Level: Beginner

Who is this presentation for?

Program Manager, Product Manager, Data Scientist, Data Scientist Manager

Prerequisite knowledge

Basics of data science

What you'll learn

Best practices for managing and creating value from data science projects

Description

Data science has tremendous potential to extend our capabilities, and empower organizations to accelerate their digital transformation by infusing apps and experiences with AI. This presentation covers the basics of managing data science and AI projects, including the data science lifecycle and overview of one example approach that is adopted internally at Microsoft within many data science teams which we call the “Team Data Science Process” (TDSP). Learn more about the typical priorities of data science teams and the keys to success on engaging and creating value with data science.

The Team Data Science Process (TDSP) is an agile, iterative data science methodology to deliver predictive analytics solutions and intelligent applications efficiently. TDSP helps improve team collaboration and learning. It contains a distillation of the best practices and structures from Microsoft and others in the industry that facilitate the successful implementation of data science initiatives. The goal is to help companies fully realize the benefits of their analytics program.

Photo of Danielle Dean

Danielle Dean

Microsoft

Danielle Dean, PhD is a Principal Data Scientist Lead at Microsoft Corp. in the Algorithms and Data Science Group within the Artificial Intelligence & Research division. She currently leads an international team of data scientists and engineers to build predictive analytics and machine learning solutions with external companies utilizing Microsoft’s Cloud AI Platform. Before working at Microsoft, Danielle was a data scientist at Nokia, where she produced business value and insights from big data, through data mining & statistical modeling on data-driven projects that impacted a range of businesses, products and initiatives.

Danielle completed her Ph.D. in quantitative psychology with a concentration in biostatistics at the University of North Carolina at Chapel Hill, where she studied the application of multi-level event history models to understand the timing and processes leading to events between dyads within social networks.

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