Tirthajyoti Sarkar - Productive and Efficient Data Science with Python: With Modularizing, Memory profiles, and Parallel/GPU Processing

This Apress imprint is published by the registered company APress Media, LLC, part of Springer Nature.

The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

Data science and machine learning can be practiced with various degrees of efficiency and productivity. This book focuses specifically on Python-based tools and techniques to help data scientists, beginners and seasoned professionals alike, become highly productive at all aspects of typical data science tasks.

This book is specifically intended for those who wish to leapfrog beyond the standard way of performing data science and machine learning tasks, and utilize the full spectrum of the Python data science ecosystem for a much higher level of productivity. You will be taught how to look out for inefficiencies and bottlenecks in the standard process and how to think beyond the box. Automation of repetitive data science tasks is a key mindset that you will develop from reading this book. In many cases, you will also learn how to extend existing coding practices to handle larger datasets, with high efficiency, with the help of advanced software tools that already exist in the Python ecosystem but are not taught in any standard data science book.

This is not a regular Python cookbook that teaches standard libraries like NumPy or Pandas. Rather, it focuses on useful techniques such as how to measure the memory footprint and execution speed of ML models, modularize a data science or deep learning task, write object-oriented code for a data science library or web app development, and so on. It also covers Python libraries, which come in handy for automating and speeding up the day-to-day tasks of any data scientist. Furthermore, it touches upon tools and packages that help a data scientist tackle large and complex datasets in a far more optimal way than what would have been possible by following standard Python data science technology wisdom.

If you take away a mentality of probing and measuring inefficiency in your data science code, and you learn tricks to discover effective solutions for those productivity issues, I will consider this book to be successful. This will be an immense reward for me.

All source code used in this books examples can be downloaded from https://github.com/Apress/Sarkar_Productive-and-Efficient-Data-Science-with-Python

This book has been a great journey for me, and it will not be complete without acknowledging some of the people who helped me in this quest.

First, I would like to thank my editor, Aditee Mirashi, who guided me patiently on the authoring process and its specifics as this is my first collaboration with Apress. She has been unfailingly helpful and understanding while I navigated through the chapters and technical reviews.

I would like to acknowledge some of the open-source developers and data science communicators whose work I have cited or used in various chapters, with their kind permissions. Khyuen Tran has contributed greatly to the community by publishing efficient data science tricks (with Python) and I have had the pleasure of discussing these ideas with her. Her work is cited in Chapter .

My wife, Chitrita Chakravarti, an accomplished DataOps Solutions Architect herself, has provided support both professionally and personally while I was working on this book. She deserves my sincere gratitude.

Lastly, I am eternally grateful to all my friends and professional connections, especially on LinkedIn, who always had kind and encouraging words for me when I described the painstaking process of working through this project. Their support and words have been a primary source of motivation.