Productive and Efficient Data Science with Python

With Modularizing, Memory profiles, and Parallel/GPU Processing

Tirthajyoti Sarkar

• Publisher: Apress
• Year: 2022
• ISBN:
  9781484281215
• Topic: Computer Science
• Subtopic: Databases
• Index: Computer Science

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.

You'll 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. You'll 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. You'll review Python libraries which come in very handy for automating and speeding up the day-to-day tasks.

In the end, you'll 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 You'll Learn

• Write fast and efficient code for data science and machine learning
• Build robust and expressive data science pipelines
• Measure memory and CPU profile for machine learning methods
• Utilize the full potential of GPU for data science tasks
• Handle large and complex data sets efficiently

Who This Book Is For

Data scientists, data analysts, machine learning engineers, Artificial intelligence practitioners, statisticians who want to take full advantage of Python ecosystem.

Table of contents

• Cover
• Front Matter
• 1. What Is Productive and Efficient Data Science?
• 2. Better Programming Principles for Efficient Data Science
• 3. How to Use Python Data Science Packages More Productively
• 4. Writing Machine Learning Code More Productively
• 5. Modular and Productive Deep Learning Code
• 6. Build Your Own ML Estimator/Package
• 7. Some Cool Utility Packages
• 8. Memory and Timing Profile
• 9. Scalable Data Science
• 10. Parallelized Data Science
• 11. GPU-Based Data Science for High Productivity
• 12. Other Useful Skills to Master
• 13. Wrapping It Up
• Back Matter