Bøger af Kennedy Behrman

Data science and machine learningtwo of the world's hottest fieldsare attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the world's #1 programming language, is also the most popular language for data science and machine learning. This is the first guide specifically designed to help students with widely diverse backgrounds learn foundational Python so they can use it for data science and machine learning. This book is catered to introductory-level college courses on data science. Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master. Once students have learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving. Throughout, Foundational Python for Data Science presents hands-on exercises, learning assessments, case studies, and moreall created with Colab (Jupyter compatible) notebooks, so students can execute all coding examples interactively without installing or configuring any software.

Much has changed in technology over the past decade. Data is hot, the cloud is ubiquitous, and many organizations need some form of automation. Throughout these transformations, Python has become one of the most popular languages in the world. This practical resource shows you how to use Python for everyday Linux systems administration tasks with todays most useful DevOps tools, including Docker, Kubernetes, and Terraform.Learning how to interact and automate with Linux is essential for millions of professionals. Python makes it much easier. With this book, youll learn how to develop software and solve problems using containers, as well as how to monitor, instrument, load-test, and operationalize your software. Looking for effective ways to "e;get stuff done"e; in Python? This is your guide.Python foundations, including a brief introduction to the languageHow to automate text, write command-line tools, and automate the filesystemLinux utilities, package management, build systems, monitoring and instrumentation, and automated testingCloud computing, infrastructure as code, Kubernetes, and serverlessMachine learning operations and data engineering from a DevOps perspectiveBuilding, deploying, and operationalizing a machine learning project