Software Engineering for Data Scientists (MEAP V03)

In Software Engineering for Data Scientists you’ll learn to improve performance and efficiency by:

• Using source control
• Handling exceptions and errors in your code
• Improving the design of your tools and applications
• Scaling code to handle large data efficiently
• Testing model and data processing code before deployment
• Scheduling a model to run automatically
• Packaging Python code into reusable libraries
• Generating automated reports for monitoring a model in production

Software Engineering for Data Scientists presents important software engineering principles that will radically improve the performance and efficiency of data science projects. Author and Meta data scientist Andrew Treadway has spent over a decade guiding models and pipelines to production. This practical handbook is full of his sage advice that will change the way you structure your code, monitor model performance, and work effectively with the software engineering teams.

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.