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- The post previews a double header meetup session pairing a practitioner talk with an engineering deep dive into distributed algorithms.
- The post states Netflix analytical leader Chris Pouliot presented on building a large data science team and the lifecycle of data science in practice.
- The post states 0xdata's Tomas Nykodym and Cliff Click presented on building a Distributed Generalized Linear Model covering logistic and Poisson regression.
- The post highlights Stephen Boyd's ADMM and Generalized Gradients as the two mathematical approaches compared in a live demo on big data applications.
In this double header we present a practitioners close view of the science and an engineer’s close view of design and implementation of distributed algorithm.
Day in the Life of a Data Scientist – Chris Pouliot In this session, Netflix analytical leader Chris Pouliot shares his experience building a large team of data scientists at Netflix and what a typical day in life of a Data Scientist looks like. From extracting and exploring data to posing good questions around them and matching them with the right algorithms, Chris goes through the lifecycle of data science in practice. Chris built a central, horizontal team for the company that spans across all business verticals. Chris shares insights and stories, covering pitfalls and successes and impact they have at Netflix. Distributed Generalized Linear Modeling (GLM) – Tomas Nykodym In this session, 0xdata engineers, Tomas Nykodym & Cliff Click explain how to build a Distributed GLM (Logistic, Poisson Regression .) Generalized_linear_model is the most popular tool at the hand of a good datascientist. A couple of very powerful mathematical approaches such as Stephen Boyd’s ADMM and Generalized Gradients are analyzed along with implementation choices. Live Demo and performance comparisons between the two approaches and on applications on Big Data will be presented.