The data science skill set is ever-expanding to include more and more of the analytics pipeline. In addition to fitting statistical and machine learning models, data scientists are expected to ingest data from different file formats, interact with APIs, work at the command line, manipulate data, create plots, build dashboards, and track all their work in git. By combining all of these components, data scientists can produce amazing results. In this text, Michael Freeman and Joel Ross have created the definitive resource for new and aspiring data scientists to learn foundational programming skills.

Michael and Joel are best known for leveraging visualization and front-end interfaces to compose explanations of complex data science topics. In addition to their written work, they have created interactive explanations of statistical methods, including a particularly clarifying and captivating introduction to hierarchical modeling. It is this sensibility and deep commitment to demystifying complicated topics that they bring to their new book, which teaches a plethora of data science skills.

This tour of data science begins by setting up the local computing environment such as text editors, RStudio, the command line, and git. This lays a solid foundation—that is far too often glossed over—making it easier to learn core data skills. After this, those core skills are given attention, including data manipulation, visualization, reporting, and an excellent explanation of APIs. They even show how to use git collaboratively, something data scientists all too often neglect to integrate into their projects.

Programming Skills for Data Science lives up to its name in teaching the foundational skills needed to get started in data science. This book provides valuable insights for both beginners and those with more experience who may be missing some key knowledge. Michael and Joel made full use of their years of teaching experience to craft an engrossing tutorial.

—Jared Lander, series editor

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