Abstract: 

R and Python are the primary tools of today’s data science workflows. R provides powerful statistics and quick visualizations. Python offers an intuitive syntax, abundant support, and is the choice interface for today’s major AI frameworks.

When building a data science team it is important to document and record work flows in both R and python. A data science department can use installable git controlled packages to form a foundation of code and best practices. In addition, libraries dramatically cut down the time and effort required for a team to bring work to production. Additionally package development is the first step for engineers and scientists aiming to contribute their unique ideas back to the community.

This workshop aims to teach the basics of package development in both R and python in 90 minutes. We will touch upon why a data science team should strive to be fully fluent in both languages. We will show simple R package development and simple Python package development. Finally, we will demonstrate how one can use an open source package to test the interface similarity between R and Python packages designed to support identical workflows.

● Text editor, command line terminal;
● Possibly RStudio/jupyter lab;

https://github.com/tedbakanas/starterkits
https://drive.google.com/open?id=1zdNO4bYIP2831ARS_SflHqAkgxkev1GN

Bio: 

Zhi Lu is self-motivated with a PhD. in computational materials science, specializing in designing next-generation functional materials using computational simulations, such as Density Functional Theory (DFT), and Monte Carlo Simulation (MC).
Quantitative background coupled with programming skills, specializing in working with large complex data sets and conducting end-to-end analysis including data gathering, clearning, machine learning modeling and presenting.