Mix-n-Match R and Python for Data Science

Description:

Python's fast growing as the de-facto language for doing data science, and over the years, many have adopted it, especially newcomers to the data science space. R has been one of the primary languages for statisticians and data scientists over the years, and has an impressive array of data analysis and mathematical modeling libraries that come with it, that make it formidable.

While a lot of people see R and Python as competitors in the data science space, pragmatic Pythonistas will want to use the best of R within Python, and vice versa with experienced R programmers who are statisticians or data scientists, who would want to benefit from Python. This talk takes us through the differentiated projects and libraries that are there within R and Python, and how to enable the interoperability of these capabilities, specifically in a data analysis context.

Prerequisites:

Those exposed to R and Python for data science ought to benefit from this talk. Practicing data scientists will be especially benefited, because they get to learn about packages within R and Python that are unique to their respective ecosystems, options for interoperability, and so on.

Speaker Info:

Experienced data science and advanced analytics professional with practical experience in statistical analysis, large scale data science and engineering and machine learning including deep learning. Solved interesting problems in industries such as banking, financial services, manufacturing, telecommunications and the energy sector, in the spaces of computer vision, time series modeling and helped envision award-winning AI and IoT solutions. Past experience in statistical problem solving, quality/reliability engineering, engineering design and process optimization, in diverse industries.

Speaker Links:

1. Website: http://rexplorations.wordpress.com
2. Github: http://github.com/rexplorations
3. Twitter: http://twitter.com/rexplorations