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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

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