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about the technology

Many basic software engineering skills apply directly to data science! As a data scientist, learning the right software engineering techniques can save you a world of time and frustration. Source control simplifies sharing, tracking, and backing up code. Testing helps reduce future errors in your models or pipelines. Exception handling automatically responds to unexpected events as they crop up. Using established engineering conventions makes it easy to collaborate with software developers. This book teaches you to handle these situations and more in your data science projects.

about the book

In Software Engineering for Data Scientists you’ll find tested software engineering techniques that will make your daily life easier as a data scientist. You’ll quickly get up to speed with how software engineering can solve common problems, then dive straight into source control, object-oriented programming, code testing, and packaging. Hands-on examples make it easy to see how new principles can be put into practice in a data science context.

• Improve code structuring and reusability in a customer churn prediction model
• Learn to scale data processing code by experimenting with Spotify data
• Build a lightweight web app to monitor a machine learning model
• Master the software design conventions that make your code easy to share and modify
• …and much more!

Every chapter comes with focused exercises and downloadable code for you to experiment and explore. You’ll be amazed at how a few changes in your process can make your data science projects so much easier to create and maintain.

about the reader

For data scientists who know the basics of Python.

about the author

Andrew Treadway is a Senior Research Data Scientist at Meta. He has over a decade of experience working in data science. Andrew has taught data science and programming courses through Baruch College, 365 Data Science, and Interview Kickstart. He runs a blog at TheAutomatic.net, covering topics around Python and R.