In this book, we take a practical approach to data analysis with R and Python. With relative ease, we can answer questions about particular datasets, produce models, and export visualizations. For this reason, R is an excellent choice for rapid prototyping and analytics since it is a domain-specific language designed for statistical data analysis, and it does its job well.

In this book, we will take a look at a different approach to analytics that is more geared towards production environments and applications. The data science pipeline of hypothesis, acquisition, cleaning and munging, analysis, modeling, visualization, and application is not a clean and linear process by any means. Moreover, when the analysis is meant to be reproducible at scale in an automated fashion, many new considerations and requirements enter into the picture. Thus, many data applications require a broader toolkit. This toolkit should still provide rapid prototyping, be generally available on all systems...