Introduction to Python Data Wrangling with Python datatable Series 1/3
This meetup was recorded in San Francisco on January 29th, 2020.
Description:
H2O datatable is a Python package for manipulating 2-dimensional tabular data structures, aka data frames. It is close in spirit to pandas, however, we put specific emphasis on speed and big data support. As the name suggests, the package is closely related to R's data.table and attempts to mimic its core algorithms and API. H2O datatable started in 2017 as a toolkit for performing big data operations on a single-node machine, at the maximum speed possible. Such requirements are dictated by modern machine-learning applications, such as H2O Driverless AI, which need to process large volumes of data and generate many features in order to achieve the best model accuracy.
This talk will be the first of a series of talks regarding Python datatable. We will provide an introduction of H2O’s datatable focusing on what datatable is, what is not, when to use it, high-level library overview, where to ask questions, how to contribute, a short demo followed by the Q/A session.
Prerequisites:
Knowledge of Python
Jupyter Lab Installed
Python datatable installed on your local machine or use cloud
Datatable can be installed by the following: https://datatable.readthedocs.io/en/l...
Bios:
Pasha is a Hacker Scientist at H2O.ai. He holds an MS in Applied Physics and Mathematics from Moscow Institute of Physics and Technology, an MA in Economics from New Economic School (Moscow), and a PhD in Economics (econometrics) from Stanford University. During his education, he obtained knowledge in Computer Science, Machine Learning, Statistics, and Econometrics.
Prior to coming to H2O.ai, Pasha was working at a stealth-level machine learning startup Machinify.com as a data scientist/frontend engineer; before that as an engineer at Facebook; and before as a senior quantitative analyst at a business consulting company Keystone Strategy, working on big data analysis.
Oleksiy Kononenko: Oleksiy is a maker scientist and hacker at H2O.ai, focusing on highly optimized algorithms for machine learning and data analysis. He holds M.S., summa cum laude, and Ph.D. degrees in applied mathematics from National University of Kharkiv, Ukraine. In 2009, Oleksiy was selected as a research fellow by CERN and contributed to R&D for Large Hadron Collider and the next generation of high energy particle accelerators. In 2013 he joined SLAC and Stanford University to develop high- performance simulation suite for 3D multi-physics modeling. Oleksiy authored more than 60 scientific papers, was an invited speaker at major international conferences, prominent institutions, and companies worldwide.
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