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About the Book
As a data scientist, you don’t just analyze data—you lay the foundation for software that can shape the future. You write the proof-of-concept, the prototype, the starting point. But here’s the truth—how you write that initial code can determine whether it evolves smoothly into robust, maintainable software or becomes a brittle, tangled mess.
This book reveals the pivotal role data scientists play as the initial authors of software and the profound impact they have on its long-term success. More importantly, it equips you with essential software design principles to ensure your Python code is clear, adaptable, and ready to grow.
Whether you are building data pipelines, creating algorithms, or preparing data for insights, this guide will help you elevate your technical craft. Learn how to move beyond quick fixes and prototypes, creating software that is not only functional but also built to last.
About the Author
Tobias Horsmann is a computer scientist with a Bachelor's and Master's degree in Computer Science and a PhD in Natural Language Processing. With years of experience bridging the gap between data science and software engineering, he understands the challenges data scientists face when developing maintainable code. In The Data Scientist’s Impact: Building Maintainable Code That Lasts, he offers practical insights to help data scientists create long-lasting, maintainable prototypes.