If you are trying to hone your skills in the field of data science, there are many books and courses out there with varying levels of difficulty. What usually happens is that you start to study introductory resources and then continue with more in-depth, technical ones to get a taste of a new field or technology. If you were following this kind of learning path for sometime, you must have realized that it becomes very time consuming journey. We, as lifelong learners, need books with more compact representation of knowledge and experience which requires the right balance between theory and practice. This book aims to bring beginner, intermediate, and advanced concepts together and it is our humble effort to build up your knowledge from scratch.
This book assumes no previous background of scientific computing and will introduce various subjects using practical examples. It may sometimes feel like separate topics pulled together randomly and the book's flow doesn't stick to one consistent path. This was a deliberate decision we made to give you a little taste of several different topics and applications.
We hope that you will read this book to have a broader overview of scientific computing as well as to master the nitty-gritty of NumPy and other supporting scientific libraries of Python such as SciPy and Scikit-Learn.
