Software Engineering for Data Scientists (MEAP)

These easy to learn and apply software engineering techniques will radically improve collaboration, scaling, and deployment in your data science projects.
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 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.


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