Applied Computational Thinking with Python. Design algorithmic solutions for complex and challenging real-world problems

Sofía De Jesús, Dayrene Martinez

Computational thinking helps you to develop logical processing and algorithmic thinking while solving real-world problems across a wide range of domains. It's an essential skill that you should possess to keep ahead of the curve in this modern era of information technology. Developers can apply their knowledge of computational thinking to solve problems in multiple areas, including economics, mathematics, and artificial intelligence.This book begins by helping you get to grips with decomposition, pattern recognition, pattern generalization and abstraction, and algorithm design, along with teaching you how to apply these elements practically while designing solutions for challenging problems. You’ll then learn about various techniques involved in problem analysis, logical reasoning, algorithm design, clusters and classification, data analysis, and modeling, and understand how computational thinking elements can be used together with these aspects to design solutions. Toward the end, you will discover how to identify pitfalls in the solution design process and how to choose the right functionalities to create the best possible algorithmic solutions.By the end of this algorithm book, you will have gained the confidence to successfully apply computational thinking techniques to software development.

Mastering Python. Master the art of writing beautiful and powerful Python by using all of the features that Python 3.5 offers

Igor Milovanovic, Rick van Hattem

Python is a dynamic programming language. It is known for its high readability and hence it is often the first language learned by new programmers. Python being multi-paradigm, it can be used to achieve the same thing in different ways and it is compatible across different platforms. Even if you find writing Python code easy, writing code that is efficient, easy to maintain, and reuse is not so straightforward. This book is an authoritative guide that will help you learn new advanced methods in a clear and contextualised way. It starts off by creating a project-specific environment using venv, introducing you to different Pythonic syntax and common pitfalls before moving on to cover the functional features in Python. It covers how to create different decorators, generators, and metaclasses. It also introduces you to functools.wraps and coroutines and how they work. Later on you will learn to use asyncio module for asynchronous clients and servers. You will also get familiar with different testing systems such as py.test, doctest, and unittest, and debugging tools such as Python debugger and faulthandler. You will learn to optimize application performance so that it works efficiently across multiple machines and Python versions. Finally, it will teach you how to access C functions with a simple Python call. By the end of the book, you will be able to write more advanced scripts and take on bigger challenges.

VMware vRealize Operations Performance and Capacity Management. A hands-on guide to mastering performance and capacity management in a virtual data center

Iwan 'e1' Rahabok

Kubernetes Cookbook. Practical solutions to container orchestration - Second Edition

Hideto Saito, Hui-Chuan Chloe Lee, Ke-Jou Carol Hsu

Kubernetes is an open source orchestration platform to manage containers in a cluster environment. With Kubernetes, you can configure and deploy containerized applications easily. This book gives you a quick brush up on how Kubernetes works with containers, and an overview of main Kubernetes concepts, such as Pods, Deployments, Services and etc. This book explains how to create Kubernetes clusters and run applications with proper authentication and authorization configurations. With real-world recipes, you'll learn how to create high availability Kubernetes clusters on AWS, GCP and in on-premise datacenters with proper logging and monitoring setup. You'll also learn some useful tips about how to build a continuous delivery pipeline for your application. Upon completion of this book, you will be able to use Kubernetes in production and will have a better understanding of how to manage containers using Kubernetes.

Drone Development from Concept to Flight. Design, assemble, and discover the applications of unmanned aerial vehicles

Sumit Sharma

Unlock opportunities in the growing UAV market where drones are revolutionizing diverse sectors like agriculture, surveying, and the military. This book walks you through the complete drone development life cycle, from concept to pilot stage, prototyping, and ultimately, a market-ready product, with domain-specific applications.Starting with an introduction to unmanned systems, principles of drone flight, and it's motion in 3D space, this book shows you how to design a propulsion system tailored to your drone’s needs. You’ll then get hands on with the entire drone assembly process, covering airframe, components, and wiring. Next, you’ll enhance drone connectivity and navigation with communication devices, such as RFD900, Herelink, and H-16 Pro GCS and hardware protocols like I2C, and UART. The book also guides you in using the open-source flight software ArduPilot and PX4, along with firmware architecture and PID tuning for advanced control. Additionally, you’ll go learn about AeroGCS, Mission Planner, and UGCS ground control stations, tips for maiden flight and log analysis for optimizing performance while building a custom survey drone with a 60-min endurance, 10km range, live video feed, and photography options.By the end of this book, you’ll be equipped with all you need to build and fly your own drones and UAVs.

Creating Development Environments with Vagrant. Leverage the power of Vagrant to create and manage virtual development environments with Puppet, Chef, and VirtualBox

MICHAEL KEITH PEACOCK

Getting Started with Elastic Stack 8.0. Run powerful and scalable data platforms to search, observe, and secure your organization

Asjad Athick, Shay Banon

The Elastic Stack helps you work with massive volumes of data to power use cases in the search, observability, and security solution areas.This three-part book starts with an introduction to the Elastic Stack with high-level commentary on the solutions the stack can be leveraged for. The second section focuses on each core component, giving you a detailed understanding of the component and the role it plays. You’ll start by working with Elasticsearch to ingest, search, analyze, and store data for your use cases. Next, you’ll look at Logstash, Beats, and Elastic Agent as components that can collect, transform, and load data. Later chapters help you use Kibana as an interface to consume Elastic solutions and interact with data on Elasticsearch. The last section explores the three main use cases offered on top of the Elastic Stack. You’ll start with a full-text search and look at real-world outcomes powered by search capabilities. Furthermore, you’ll learn how the stack can be used to monitor and observe large and complex IT environments. Finally, you’ll understand how to detect, prevent, and respond to security threats across your environment. The book ends by highlighting architecture best practices for successful Elastic Stack deployments.By the end of this book, you’ll be able to implement the Elastic Stack and derive value from it.

Python Data Cleaning Cookbook. Modern techniques and Python tools to detect and remove dirty data and extract key insights

Michael Walker

Getting clean data to reveal insights is essential, as directly jumping into data analysis without proper data cleaning may lead to incorrect results. This book shows you tools and techniques that you can apply to clean and handle data with Python. You'll begin by getting familiar with the shape of data by using practices that can be deployed routinely with most data sources. Then, the book teaches you how to manipulate data to get it into a useful form. You'll also learn how to filter and summarize data to gain insights and better understand what makes sense and what does not, along with discovering how to operate on data to address the issues you've identified. Moving on, you'll perform key tasks, such as handling missing values, validating errors, removing duplicate data, monitoring high volumes of data, and handling outliers and invalid dates. Next, you'll cover recipes on using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors, and generate visualizations for exploratory data analysis (EDA) to visualize unexpected values. Finally, you'll build functions and classes that you can reuse without modification when you have new data. By the end of this Python book, you'll be equipped with all the key skills that you need to clean data and diagnose problems within it.