About the Book
Learning a language is easy. Whenever I start with a new language, I focus on a few things like operations and loops common to every language, and it is a breeze to get started with writing code in any language.
However, learning to write code in a language and writing a language in an optimized way are two different things.
Every Language has some ingredients which make it unique. Yet, a new programmer to any language will invariably do some forced overfitting.
In this book, I will explain some simple constructs provided by Python, some essential tips, and some use cases I regularly use in my Data Science work. Most of the book is of a practical nature, and you will find it beaming with examples.
And the best thing- It's FREE, and you can choose to pay only if you like it.
About the Author
I am Rahul Agarwal(MLWhiz), a data scientist consultant, and big data engineer based in London.
Previously, I have worked at startups like Fractal and MyCityWay and conglomerates like Citi, WalmartLabs, and Meta.
I started writing with the purpose to augment my own understanding of new things while helping others learn about them. I also write for publications on Medium like Towards Data Science and HackerNoon
As Feynman said: “I couldn’t do it. I couldn’t reduce it to the freshman level. That means we don’t really understand it”
Personally, I am tool agnostic. I like learning new tools and constantly working to add up new skills as I face new problems that cannot be accomplished with my current set of techniques. But the tools that currently get most of my work done are Python, Hadoop, and Spark.
I also like working with data-intensive problems and am constantly searching for new ideas to work on.