It S On My List Or In My List
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bash Idioms
Author: Carl Albing
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2022-03-16
Shell scripts are everywhere, especially those written in bash-compatible syntax. But these scripts can be complex and obscure. Complexity is the enemy of security, but it's also the enemy of readability and understanding. With this practical book, you'll learn how to decipher old bash code and write new code that's as clear and readable as possible. Authors Carl Albing and JP Vossen show you how to use the power and flexibility of the shell to your advantage. You may know enough bash to get by, but this book will take your skills from manageable to magnificent. Whether you use Linux, Unix, Windows, or a Mac, you'll learn how to read and write scripts like an expert. Your future you will thank you. You'll explore the clear idioms to use and obscure ones to avoid, so that you can: Write useful, flexible, and readable bash code with style Decode bash code such as ${MAKEMELC,,} and ${PATHNAME##*/} Save time and ensure consistency when automating tasks Discover how bash idioms can make your code clean and concise
Modern Data Science with R
From a review of the first edition: "Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics" (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice.
Machine Learning for Decision Sciences with Case Studies in Python
This book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical, oil and gas, e-commerce, and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data. Features: Explains the basic concepts of Python and its role in machine learning. Provides comprehensive coverage of feature engineering including real-time case studies. Perceives the structural patterns with reference to data science and statistics and analytics. Includes machine learning-based structured exercises. Appreciates different algorithmic concepts of machine learning including unsupervised, supervised, and reinforcement learning. This book is aimed at researchers, professionals, and graduate students in data science, machine learning, computer science, and electrical and computer engineering.