Presented By O'Reilly and Cloudera
Make Data Work
September 25–26, 2017: Training
September 26–28, 2017: Tutorials & Conference
New York, NY
Matthew Rocklin

Matthew Rocklin
Computational Scientist , Anaconda

Website

Matthew Rocklin is an open source software developer at Anaconda focusing on efficient computation and parallel computing, primarily within the Python ecosystem. He has contributed to many of the PyData libraries and today works on Dask, a framework for parallel computing. Matthew holds a PhD in computer science from the University of Chicago, where he focused on numerical linear algebra, task scheduling, and computer algebra.

Sessions

9:00am12:30pm Tuesday, September 26, 2017
Data science & advanced analytics
Location: 1E 15/16 Level: Intermediate
Matthew Rocklin (Anaconda), Ben Zaitlen (Anaconda)
Average rating: *****
(5.00, 1 rating)
The Python data science stack, which includes NumPy, pandas, and scikit-learn, is efficient and intuitive but only for in-memory data and a single core. Matthew Rocklin and Ben Zaitlen demonstrate how to parallelize and scale your Python workloads to multicore machines and multimachine clusters. Read more.
2:55pm3:35pm Wednesday, September 27, 2017
Data science & advanced analytics, Machine Learning & Data Science
Location: 1A 08/10 Level: Intermediate
Secondary topics:  Pydata
Matthew Rocklin (Anaconda)
Average rating: ****.
(4.67, 3 ratings)
Dask parallelizes Python libraries like NumPy, pandas, and scikit-learn, bringing a popular data science stack to the world of distributed computing. Matthew Rocklin discusses the architecture and current applications of Dask used in the wild and explores computational task scheduling and parallel computing within Python generally. Read more.
11:20am12:00pm Thursday, September 28, 2017
Location: O'Reilly booth (Table A)
Matthew Rocklin (Anaconda)
Want to know how to parallelize Python code with Dask? Talk to Matthew. Read more.