Hardware for Data Science: A CRUG|ChiPy joint production

Whatever your language, at some point the code is processed on Silica and metal. Considering ever growing data sets, what are the implications for working with the standard issue 8gb laptop, and what attributes should you consider in your next computer? Join us for a discussion on the latest in hardware for Data Science to find out!

Hosted at Metis with pizza and drinks provided by the venerable IBM!
Doors open at 5:00 pm, talks start at 5:30!

Parfait Gasana will kick off the meetup by analyzing hardware benchmarking results from R and Python data analytics simulations. He will show how processing time and memory usage varies with hardware specs: available RAM, virtual memory, 32/64-bit architecture, OS type and version, number of cores, and core speed.

Brian Peterson of Heymeyer trading + investments will crack open a desktop built for modeling high-frequency time series data and discuss its components. As someone that routinely process greater amounts of data than can be contained in RAM, he offers the practitioners point of view on hardware choices.

Seth Carpenter of FHLBC, will discuss how and when to use Amazon Web Services (renting someone else’s hardware) and how to do so using Python.

Justin Shea will discuss his recent custom build containing an AMD Threadripper 1950x processor. This newer chip contains 16 cores for parallel processing, offering greater performance than its Intel counterpart, at a fraction of the cost.