Today, most people enter the world of Data Science through the buzz and allure of “AI.” We tackle Kaggle challenges, voraciously consume Stack Overflow, and eat, live, and breathe through the Jupyter Notebook. Python, along with its “killer app” of Machine Learning, has done nothing short of revolutionize the way we “do data science,” and the world is a more interesting place because of it!

The Big Cloud providers, and many open source tools, have done wonders to democratize this technology. But, ‘easy access’ to high technology comes with a cost - we can easily go too far, rely too much on the tools we have today, and forget how to build the tools we need to truly transform our individual projects.

Most of the time, your impact as a Data Scientist is limited by your ability to enact your ideas - not by the ideas themselves. You can train a model on ‘clean’ data using Scikit Learn or FastAI, or run an ANOVA, in a notebook. Enacting that idea means getting to the data in the first place. It means knowing how to store it. It means processing your data at scale. It means running your processing script, reliably, every day on fresh data. It means testing that script. It means collaborating on that script with a coworker - or 10 - as the project scales. It means curating a library and building tools to solve the same problem for 5 new projects. It means packaging a model up for distribution - sharing with another data scientist, or deploying it as a service.

It means changing the way you think about problems by adopting new paradigms that accelerate you - and your work - across your organization. It means building an approach to data science within the broader python ecosystem.

This book is about python, and how to be an effective python programmer, as a Data Scientist. We learn the advanced python skills we need to accelerate you, and solve the real, daily problems you face in your DS role.