Refactor a throwaway model you have, parameterize any hardcoded variables, and expose preprocessing, training, and prediction endpoints using FastAPI. This is by no means production ready code, but it’s a good start. Nobody really learns these …

Jan 12, 2023 · A traditional software engineer sets out rules in code. In contrast, a data scientist identifies with learning algorithms that analyze patterns in data. But analytics projects are still bound together with conventional code, and as a data scientist, you can benefit from best practices first pioneered by software engineering.

Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code.

Sep 9, 2024 · While expertise in algorithms and statistical methods is essential, understanding how to develop robust, scalable, and maintainable software systems is equally important for data scientists and...

In this course, you'll learn all about the important ideas of modularity, documentation, & automated testing, and you'll see how they can help you solve Data Science problems quicker and in a way that will make future you happy.

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 …

There are several key components of software engineering that can enhance the data science workflow. These include exception handling, source control, object-oriented programming (OOP), and scale (among others).

Apr 16, 2025 · Software engineering principles, particularly the SOLID principles, provide a robust framework for enhancing data engineering practices. These principles, originally designed for software development, can be adapted to improve the maintainability and scalability of …

Learn object-oriented programming, how to test and log code, git, and the command line. Preview what you will learn in Software Engineering for Data Science! Learn about the fundamental principles that differentiate programming for data science from programming for engineering.

Learn about the key approaches for making your code production-ready that will save you time and future headaches. Source: Chris Ried @ unsplash. As a field, Data Science has caused polemic with other disciplines ever since it started to grow in popularity.