Mastering Data Cleaning Advanced Techniques For Comprehensive Data Refinement Pdf
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Mastering Data Cleansing
Author: Michael E Kirshteyn Ph D
language: en
Publisher: Independently Published
Release Date: 2024-02-23
Visual overview of the book: https: //www.youtube.com/watch?v=HPir7cvPwpo&feature=youtu.be Podcast: https: //www.dropbox.com/scl/fi/o3qr6u6fxilbykw327sej/Data-Cleansing.wav?rlkey=4hgi642vx0myk050x7jt40tpq&e=1&st=kkod13wi&dl=0 Embark on a journey to data excellence with "Mastering Data Cleansing: Advanced Techniques for Comprehensive Data Refinement." In this comprehensive guide, readers are equipped with advanced techniques and strategies to elevate their data cleansing practices to new heights. From detecting and correcting errors to enhancing data quality and integrity, this book provides a deep dive into the intricacies of data refinement. Whether you're dealing with structured tabular data or unstructured textual data, this book offers practical insights and actionable advice to tackle even the most challenging data cleansing tasks. With illuminating case studies and hands-on examples, "Mastering Data Cleansing" empowers readers to unlock the full potential of their data assets and drive informed decision-making. Whether you're a seasoned data professional or a newcomer to the field, this book is your ultimate guide to mastering the art of data cleansing and achieving data excellence. https: //www.Michael-E-Kirshteyn.com/
Beyond Code
AI-powered coding tools are revolutionizing software development, transforming programming from a specialized skill into an accessible educational practice across disciplines. This book investigates how tools such as Cursor AI, GitHub Copilot, and Replit's Ghostwriter are dismantling traditional barriers to entry for learners—particularly those from non-STEM backgrounds—by enabling natural language code generation, intelligent debugging, and interactive, project-based learning. Bridging the gap between theoretical instruction and practical application, the book serves as both a guide and a critical framework for integrating generative AI into curricula. It highlights how these tools expand the boundaries of programming education by supporting interdisciplinary applications, from literary analysis to creative writing, thereby making coding relevant and actionable for students in the humanities and beyond. The book equips educators with the tools and strategies necessary to incorporate AI-assisted programming into diverse academic contexts by offering lesson plans and adaptable project models. This resource is essential for instructors seeking to demystify coding, promote inclusivity in technical learning, and reimagine the role of software literacy in the twenty-first-century classroom.
Python for DevOps
DESCRIPTION Python has emerged as a powerhouse for DevOps, enabling efficient automation across various stages of software development and deployment. This book bridges the gap between Python programming and DevOps practices, providing a practical guide for automating infrastructure, workflows, and processes, empowering you to streamline your development lifecycle. This book begins with foundational Python concepts and their application in Linux system administration and data handling. Progressing through command line tool development using argparse and Click, package management with pip, Pipenv, and Docker, you will explore automating cloud infrastructure with AWS, GCP, Azure, and Kubernetes. The book covers configuration management with Ansible, Chef, and Puppet, and CI/CD pipelines using Jenkins, GitLab, and GitHub. You will also learn monitoring with Prometheus, Grafana, and OpenTelemetry, MLOps with Kubeflow and MLflow, serverless architecture using AWS Lambda, Azure Functions and Google Cloud Functions, and security automation with DevSecOps practices. The real-world project in this book will ensure the practical application of your learning. By mastering the techniques within this guide, you will gain the expertise to automate complex DevOps workflows with Python, enhancing your productivity and ensuring robust and scalable deployments, making you a highly competent DevOps professional. WHAT YOU WILL LEARN ● Automate DevOps tasks using Python for efficiency and scalability. ● Implement infrastructure as code (IaC) with Python, Terraform, and Ansible. ● Orchestrate containers with Python, Docker, Kubernetes, and Helm charts. ● Manage cloud infrastructure on AWS, Azure, and GCP using Python. ● Enhance security, monitoring, and compliance with Python automation tools. ● Monitor with Prometheus/Grafana/OpenTelemetry, implement MLOps using Kubeflow/MLflow, and deploy serverless architecture. ● Apply real-world project skills, and integrate diverse DevOps automations using Python. ● Ensure robust code quality, apply design patterns, secure secrets, and scale script optimization. WHO THIS BOOK IS FOR This book is for DevOps engineers, system administrators, software developers, students, and IT professionals seeking to automate infrastructure, deployments, and cloud management using Python. Familiarity with Python, Linux commands, and DevOps concepts is beneficial, but the book is designed to provide guidance to all. TABLE OF CONTENTS 1. Introduction to Python and DevOps 2. Python for Linux System Administration 3. Automating Text and Data with Python 4. Building and Automating Command-line Tools 5. Package Management and Environment Isolation 6. Automating System Administration Tasks 7. Networking and Cloud Automation 8. Container Orchestration with Kubernetes 9. Configuration Management Automation 10. Continuous Integration and Continuous Deployment 11. Monitoring, Instrumentation, and Logging 12. Implementing MLOps 13. Serverless Architecture with Python 14. Security Automation and Compliance 15. Best Practices and Patterns in Automating with Python 16. Deploying a Blog in Microservices Architecture