Productive and efficient data science with Python : with modularizing, memory profiles, and parallel/GPU processing / Tirthajyoti Sarkar.

Format:

Book

Author/Creator:

Sarkar, Tirthajyoti, author.

Language:

English

Subjects (All):

Python (Computer program language).

Data mining.

Machine learning.

Physical Description:

1 online resource (395 pages)

Edition:

[First edition].

Place of Publication:

New York, NY : Apress, [2022]

Summary:

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.

Contents:

Chapter 1: What is Productive and Efficient Data Science

Chapter 2: Better Programming Principles for Efficient Data Science

Chapter 3: How to Use Python Data Science Packages more Productively

Chapter 4: Writing Machine Learning Code More Productively

Chapter 5: Modular and Productive Deep Learning Code

Chapter 6: Build Your Own Machine Learning Estimator/Package

Chapter 7: Some Cool Utility Packages

Chapter 8: Testing the Machine Learning Code

Chapter 9: Memory and Timing Profiling

Chapter 10: Scalable Data Science

Chapter 11: Parallelized Data Science

Chapter 12: GPU-Based Data Science for High Productivity

Chapter 13: Other Useful Skills to Master

Chapter 14: Wrapping It Up.

Notes:

Description based on print version record.

Includes index.

Other Format:

Print version: Sarkar, Tirthajyoti Productive and Efficient Data Science with Python

ISBN:

9781484281215

1484281217

OCLC:

1334596685