This post is not meant for seasoned software engineers. This is geared towards data scientists and machine learning (ML) practitioners, who like me, do not come from a software engineering background. We use Python a lot for our work. It is on the way to becoming the fastest growing major language for modern data-driven analytics and artificial intelligence (AI) apps. However, it is also used for simple scripting purpose, to automate stuff, to test a hypothesis, create interactive plots for brainstorming, to control lab instruments, etc.

ECMAScript 6 introduces several new Object-Oriented features that drastically change the way developers structure their projects. Web developers now have some advanced OOP functionality at their disposal to build large-scale applications in JavaScript. With this book, we'll provide you with a comprehensive overview of OOP principles in JavaScript and how they can be implemented to build sophisticated web applications. Kicking off with a subtle refresher on objects, we'll show you how easy it is to define objects with the new ES6 classes. From there, we'll fly you through some essential OOP principles, forming a base for you to get hands-on with encapsulation.

UPDATE: You will always find the latest Python script (with the linear regression class definition and methods) HERE. Use it to build further or experiment. Data scientists often come from a background which is quite far removed from traditional computer science/software engineering -- physics, biology, statistics, economics, electrical engineering, etc. But ultimately, they are expected to pick up a sufficient amount of programming/software engineering to be truly impactful for their organization and business. Being a Data Scientist does not make you a Software Engineer! And, what is at the heart of most modern programming languages and software engineering paradigms?