Welcome
A Practical Guide to Gear Up for Data Science
Welcome to “Ready Before Run(): A Practical Guide to Gear Up for Data Science.” This guide is designed for readers from diverse backgrounds — economists, statisticians, engineers, and beyond — who are interested in producing advanced analytics workflows, but don’t necessarily have any computer science foundations.
If you’ve found this resource, you likely already possess strong analytical skills from your domain. What you may lack is familiarity with the technical infrastructure that supports modern data science work. A chef needs a working kitchen before cooking anything; data scientists need configured tools before they can turn data into insight.
This guide will walk you through setting up the essential components of a data science environment — from programming languages and version control to visualisation tools and cloud platforms. By the end of it you’ll have a working technical foundation, and your infrastructure will stop competing with your ideas for attention.
1 Preface
The embers to write this book began to glow in a pre-AI era — an age when the Harvard Business Review called Data Scientist the “sexiest job of the 21st century.” Much has changed over the last few years, and it is fair to ask whether data scientists still matter. My view is that the advent of AI makes understanding the mechanics of data science more important, not less. Machines may remove much of the grunt work that so many have been well paid for over the last fifteen years. But if we cannot tell what the machines are doing, cannot make use of what they hand back, and cannot ask them precisely for what we need, we have no basis on which to remain useful in our respective fields.
This book grew out of my experience as an analyst, and the recognition that many people who need advanced data processing are held up by the technical infrastructure rather than by the analytical concepts. While there are countless resources teaching statistical methods, machine learning algorithms, and data manipulation techniques, relatively few focus on the foundation setup that makes this work possible.
“Ready Before Run()” fills this gap by providing clear, practical guidance for establishing your data science workspace. Rather than diving immediately into coding, we’ll first ensure you have the proper environment configured — allowing you to build technical confidence before tackling analytical challenges.
The book is structured as a step-by-step guide, beginning with basic command line operations and progressing through programming language setup, version control, visualisation tools, and more advanced topics like containerisation and cloud computing. While each chapter builds on the previous one, they are written to be referenced independently if needed.
My hope is that this book lowers the technical barriers in your way and leaves you with a foundation you can build on. Free of the infrastructure burden, you can concentrate on developing your analytical expertise and making meaningful contributions in your field.
Ready? Let’s gear up for data science.
Cesaire Tobias
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