SALE
Software Engineering for Data Scientists: From Notebooks to …

Software Engineering for Data Scientists: From Notebooks to …

Original price was: $69.99.Current price is: $45.00.

Extra Features
  • Premium Quality
  • Secure Payments
  • Satisfaction Guarantee
  • Worldwide Shipping
  • Money Back Guarantee


Price: $69.99 - $45.00
(as of Nov 25, 2025 04:00:33 UTC – Details)

Software Engineering for Data Scientists: From Notebooks to Production-Ready Code

As a data scientist, you’re likely no stranger to working with code. You’ve probably spent countless hours writing scripts, experimenting with algorithms, and analyzing data in Jupyter notebooks or other interactive environments. However, when it comes to deploying your models and data pipelines to production, you may find yourself facing a new set of challenges.

That’s where software engineering comes in. Software engineering is the application of engineering principles to the design, development, testing, and maintenance of software systems. For data scientists, software engineering is crucial for transforming notebooks and prototypes into reliable, scalable, and maintainable production-ready code.

Why is software engineering important for data scientists?

  1. Scalability: As your data grows, your code needs to be able to handle the increased load. Software engineering principles help you design systems that can scale horizontally (add more machines) or vertically (increase power of individual machines).
  2. Reliability: Your code needs to be robust and fault-tolerant to ensure that it can handle errors and unexpected input. Software engineering techniques like testing, logging, and monitoring help you achieve this.
  3. Maintainability: As your project evolves, your codebase needs to be easy to understand, modify, and extend. Software engineering best practices like modular design, documentation, and version control make it easier to collaborate with others and maintain your code over time.
  4. Reproducibility: With software engineering, you can ensure that your results are reproducible and trustworthy. This is critical in data science, where small changes to the code or data can have significant effects on the outcomes.

Best practices for software engineering in data science

  1. Modularize your code: Break down your notebooks into smaller, independent functions or modules that can be easily reused and tested.
  2. Use version control: Track changes to your code using tools like Git, which helps you collaborate with others and maintain a history of changes.
  3. Write tests: Use unit tests and integration tests to ensure that your code works as expected and catch errors early.
  4. Document your code: Use comments, docstrings, and documentation tools like Sphinx to make your code understandable to others (and to your future self).
  5. Follow coding standards: Adhere to coding standards like PEP 8 (for Python) to ensure consistency and readability.

From notebooks to production-ready code

So, how do you take your notebooks and turn them into production-ready code? Here’s a step-by-step process:

  1. Refactor your notebooks: Break down your notebooks into smaller functions or modules, and remove any unnecessary code or output.
  2. Create a package: Organize your code into a package structure, using tools like setuptools or poetry to manage dependencies and create a distributable package.
  3. Write tests: Use testing frameworks like pytest or unittest to write unit tests and integration tests for your code.
  4. Use a CI/CD pipeline: Set up a continuous integration/continuous deployment (CI/CD) pipeline using tools like GitHub Actions, CircleCI, or Jenkins to automate testing, building, and deployment of your code.
  5. Deploy to production: Use containerization tools like Docker or Kubernetes to deploy your code to production, where it can be scaled and monitored.

Conclusion

As a data scientist, software engineering is an essential skill for transforming your notebooks and prototypes into reliable, scalable, and maintainable production-ready code. By following best practices like modularization, version control, testing, and documentation, you can ensure that your code is robust, reproducible, and trustworthy. By applying these principles, you can take your data science projects to the next level and deliver high-quality solutions that meet the needs of your stakeholders.

10 reviews for Software Engineering for Data Scientists: From Notebooks to …

  1. Joe Faith –

    The Missing Manual for Early Career Data Scientists
    As a professor who regularly teaches courses in AI, computer science, data science, and information systems, I can’t overstate how important this book is for anyone preparing for a career in data science. I work with students from a variety of backgrounds, and I constantly see the same gap: solid technical skills, but limited exposure to practical software engineering best practices. This book fills that gap brilliantly.The author’s approach is refreshingly accessible and practical. The book doesn’t just teach you how to write code; it teaches you how to write reproducible, robust, maintainable code that will actually work in a production environment. The focus on real-world Python examples (using pandas, NumPy, and other core packages) ensures that readers can apply these lessons immediately.What I especially appreciate as an educator is that the book meets students where they are. Whether you’ve just finished your degree, are transitioning from another field, or have taught yourself the basics, this book will make you a more effective and confident practitioner. Even more experienced data scientists will find valuable guidance on working with larger codebases and collaborating with software engineers.In my opinion, every early career data scientist should read this book, preferably before they start their first industry job. It’s packed with practical advice on documentation, packaging, testing, error handling, security, and more. These are the skills that set apart great data scientists from the rest.If you’re a student, a recent graduate, or even a working professional looking to up your game, this book deserves a spot on your desk. I’ll be recommending it to all my students, and I strongly suggest you don’t start your career without it.

  2. Luis –

    Practical and highly recommended for growing Data Scientists
    As a machine learning engineer with four years of experience working in a small data science team, I found Software Engineering for Data Scientists to be incredibly relevant and useful. The book addresses the real, day-to-day coding challenges we face and offers practical solutions that can be applied right away. It’s especially helpful for those of us looking to write cleaner, more maintainable code and adopt more structured development practices. I also appreciated how it points you to additional software engineering resources for continued growth. Ideal for anyone in data science or ML looking to elevate their coding practices.

  3. Azathoth –

    Decent for a beginner
    I’ve been a data scientist for about 4 years and have worked with a lot of colleagues that are terrible at coding and best practices in DS. I really was hoping this book was more in depth. If someone is new to data science and does not know what a linter is and is a poor programmer, this book is a good one to read. Unfortunately I didn’t learn anything from this book so it’s targeted to beginners or people learning DS.

  4. Naif A. Ganadily, MSEE –

    A True Software Engineering Guide for Data Scientists
    I have been meaning to write a review for a while now. To give you an answer if you should or should not buy this book as a Real Guide to Software Engineering as a Data Scientist, my answer is (YES, YOU SHOULD BUY THIS BOOK!). My background is straightforward. I graduated with a BS in Electrical Engineering (Electronics and Telecommunications), where I was mainly on the hardware side of engineering, then transitioned to an MS in Electrical Engineering (Specialized in AI), where all my coursework and projects were in Artificial Intelligence, where I learned how to be a Data Scientist and an AI Engineer, so you can see I skipped Software Engineering. The moment I knew that I struggled with these essential skills was during my first post-master’s job as an AI Consultant, where I was having a lot of issues with production-ready code, large codebases, testing,…etc.This book saved my career. It made me more of a software engineer, which, in my opinion, should be the foundational skill before entering the data science field. Now, back to the book: I found it easy to understand and follow. It was insightful about which tools to use, when to use them, and how to use them during the coding and scripting process of production-ready projects.The only issue I have with the book is not with the author but with the publishing company O’Reilly. Some books are colored, and some are not, which I find inconsistent with publishing best practices.

  5. Dylan –

    Great book!
    Heard about the book on the MLOps podcast and decided to buy it which overall enjoyed!

  6. jeff –

    Highly recommended especially for a new ds
    Outstanding book! If you are a new ds you will benefit from this book tremendously.

  7. Travis Strawn –

    Covers all the important topics succinctly and to the point. Great book!
    Data analysts and other data professionals should read this book, unless they are well-experienced software engineers as well. It covers a lot of essential ground that’s all very important to know. Highly recommend it!

  8. Rob –

    Extremely clear, great way to brush up or learn the fundamentals of SWE for Data Scientists
    This book will give you a very clear exposition of key SWE concepts to improve your coding as a data scientist, and to better understand how to work with other software engineers. It won’t teach you everything you need to know to be a SWE or become an expert python programmer. The concepts are laid out clearly, and intuitively with a lot of care to explain why, not just what. The examples are also easy to follow and illustrative, and are very DS oriented.

  9. Jéssica Mello –

    I came from Electrical Engineering with some Data Science basis in the Academia, then I worked as an ML Engineer for some time. I can say that this book provided me valuable and practical insights, which can be used in different situations to tackle a bunch of “quality problems” in software. Also, the libraries suggested are a must. The insights range from “security” to “performance”. This book provides objective information without been vague – it follows necessary scientific methodology and their good practices. I really recommend it.

  10. Denis Burakov –

    Initially came across this book through an early release, yet the full book experience was somewhat lacking. I was expecting this book to be a companion to data scientists transitioning to ML engineering roles, but it was a bit more like a collection of tips for improving code and project quality. To give the author full credit, it is a well structured book with a lot of helpful examples. If you already have extensive applied experience, you would probably want to read the DevOps for Data Science book.

Add a review

Your email address will not be published. Required fields are marked *

Related products