This book focuses on the Python-based tools and techniques to help you become highly productive at all aspects of typical data science stacks such as statistical analysis, visualization, model selection, and feature engineering.Yoüll review the inefficiencies and bottlenecks lurking in the daily business process and solve them with practical solutions. Automation of repetitive data science tasks is a key mindset that is promoted throughout the book. Yoüll learn how to extend the existing coding practice to handle larger datasets with high efficiency with the help of advanced libraries and packages that already exist in the Python ecosystem.The book focuses on topics such as how to measure the memory footprint and execution speed of machine learning models, quality test a data science pipelines, and modularizing a data science pipeline for app development. Yoüll review Python libraries which come in very handy for automating and speeding up the day-to-day tasks.In the end, yoüll understand and perform data science and machine learning tasks beyond the traditional methods and utilize the full spectrum of the Python data science ecosystem to increase productivity.What Yoüll Learn Write fast and efficient code for data science and machine learningBuild robust and expressive data science pipelinesMeasure memory and CPU profile for machine learning methodsUtilize the full potential of GPU for data science tasksHandle large and complex data sets efficientlyWho This Book Is ForData scientists, data analysts, machine learning engineers, Artificial intelligence practitioners, statisticians who want to take full advantage of Python ecosystem.

Explains how to look out for inefficiencies and bottlenecks in the standard data science codes

Chapter 1: What is Productive and Efficient Data Science?