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 byUsing source controlHandling exceptions and errors in your codeImproving the design of your tools and applicationsScaling code to handle large data efficientlyTesting model and data processing code before deploymentScheduling a model to run automaticallyPackaging Python code into reusable librariesGenerating automated reports for monitoring a model in productionSoftware 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 technologyMany 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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