SQL

• A guide to infrastructure hardening

  Canonical Limited · Guide

  summary Cloud technology and virtualisation enable developers and administrators to deploy infrastructure at a pace previously unheard of, which has brought them huge gains and enabled almost anyone to create internet services. However, this facility and flexibility does not mask the underlying need for deployments to be secure. The ever-present threats of ransomware attacks and data breaches make it imperative to lock down systems and prevent attackers from gaining an easy foothold. A fully secure system is made up of many layers, from the hardware to the operating system and the applicati...

  Also filed under Software Engineering, Computer Science

• Advancing into Analytics

  George Mount · Book

  Foundations of Analytics in Excel 1. Foundations of Exploratory Data Analysis. 3 What Is Exploratory Data Analysis?

  Also filed under Data Visualization, Machine Learning

• Database Design

  Adrienne Watt & Nelson Eng · Book

  Database Design - 2nd Edition by Adrienne Watt and Nelson Eng is licensed under a Creative Commons Attribution 4.0 International License, except where otherwise noted. If you redistribute all or part of this book, you must include the following on the copyright notice page: Download for free from the B.C. Sample APA-style citation: This textbook can be referenced. In APA citation style, it would appear as follows: Watt, A.

  Also filed under Software Engineering, Data Visualization

• Deep Learning with Azure

  Mathew Salvaris, Danielle Dean, Wee Hyong Tok · Book

  "Deep Learning with Azure" by Mathew Salvaris, Danielle Dean, and Wee Hyong Tok is a practical guide for data scientists, engineers, and AI practitioners who want to harness the power of Microsoft Azure to build, train, and deploy deep learning models. Through a series of hands-on tutorials, the book covers everything from setting up the development environment and creating deep learning models to deploying those models at scale on Azure. Key Concepts Covered Introduction to Azure and Deep Learning: The book begins with an introduction to Azure, particularly Azure Machine Learning (AML), a cloud-based service that simplifies the deployment of machine learning models. It explains how to use Azure's services to handle the computational resources required for deep learning tasks. Setting Up Your Deep Learning Environment: A key focus of the book is the setup and configuration of the deep learning environment on Azure. The authors walk through the steps required to set up Azure Machine Learning workspaces, create compute clusters, and use Azure Notebooks for efficient experimentation. The authors also discuss the integration of Azure Databricks for big data analytics and model training. Building Deep Learning Models: The book provides practical, hands-on examples of building various types of deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative models, using frameworks like TensorFlow, Keras, and PyTorch on Azure. By the end of the book, readers will have the skills necessary to leverage Azure’s cloud capabilities for deep learning, optimizing workflows, scaling model training, and deploying models for real-world applications. The book provides a comprehensive and structured approach to using Azure for deep learning, making it an essential resource for anyone working with AI in the cloud.

  Also filed under Machine Learning, AI

• Flexible Automation and Intelligent Manufacturing: The Human-Data- Technology Nexus

  Kyoung-Yun Kim, Leslie Monplaisir & Jeremy Rickli · Book

  This book is an open access publication, corrected publication 2023. The images or other third party material in this book are included in the book's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book'

  Also filed under Machine Learning, AI

• Learn SQL Fast

  D Armstrong · Book

  "Learn SQL Fast: SQL Made Simple! A Beginner's Guide to SQL, with Practical Exercises for Microsoft SQL Server" by D. Armstrong is a comprehensive resource designed to take readers from beginner to proficient in SQL. The book provides a step-by-step approach to learning SQL, focusing on practical application and hands-on exercises. Key Features: Guided Setup: The book begins by assisting readers in acquiring and setting up free SQL Server software and a sample database directly from Microsoft, ensuring a solid foundation for practice. Incremental Learning: SQL concepts are introduced in manageable segments, with each new topic accompanied by clear explanations and examples. This methodical approach facilitates a deeper understanding of each concept before progressing. Hands-On Exercises: At each stage, the book provides practice exercises, allowing readers to apply what they've learned and reinforce their skills through practical application. Progressive Complexity: As the course advances, examples and exercises become more complex, demonstrating how to combine basic concepts to write advanced queries. This progression ensures that readers build upon their knowledge systematically. This book is tailored for individuals who aim to learn SQL efficiently and effectively, providing both the practical skills and the underlying principles necessary for proficient database management.

  Also filed under Machine Learning, Data Visualization

• Learn SQL Quickly

  Code Quickly · Book

  "Learn SQL Quickly: A Beginner’s Guide to Learning SQL, Even If You’re New to Databases" is a comprehensive introduction to SQL designed for readers without prior coding experience. Authored by Code Quickly, the book aims to demystify SQL and equip readers with practical skills for managing and manipulating databases. Key Features: Clear and Concise Explanations: The book offers straightforward examples to help readers grasp essential SQL concepts, ensuring a solid foundational understanding. Foundational Knowledge: It provides an in-depth breakdown of what SQL is, making complex topics accessible to beginners. Database Management Setup: Readers are guided through setting up their database management systems, emphasizing a methodical approach to ensure a strong grasp of the basics. Efficient Data Handling: The book teaches how to control data movement effectively, optimizing workflows and minimizing potential issues. Real-World Applications: Incorporating practical examples, the book demonstrates how SQL skills can be applied in real-life scenarios, enhancing learning and retention. Focus on MySQL: It discusses the advantages of using MySQL over other database systems and provides guidance on getting started with it. In today's data-driven world, proficiency in SQL is increasingly valuable. "Learn SQL Quickly" serves as a practical resource for those eager to acquire this in-demand skill efficiently.

  Also filed under Data Visualization, Machine Learning

• Learning Node.js

  Stack Overflow Documentation · Book

  Learning Node.js (Stack Overflow Documentation) is a practical guide that compiles community-driven knowledge from Stack Overflow, offering a hands-on approach to mastering Node.js. This book is designed for developers of all levels who want to build scalable and high-performance applications using JavaScript on the server side. Key Highlights: Introduction to Node.js Explains the event-driven, non-blocking I/O model and why Node.js is ideal for real-time applications. Covers installation, setting up a development environment, and running a basic Node.js script. Core Node.js Modules and APIs In-depth coverage of built-in modules like fs (File System), http, events, path, os, and util. Working with buffers, streams, and process management. Asynchronous Programming Explains callbacks, Promises, and async/await to handle asynchronous operations effectively. Covers best practices for avoiding callback hell and improving code readability. Building Web Servers with Node.js Creating a basic HTTP server using the http module. Introduction to Express.js, a popular framework for building web applications and APIs. Working with Databases Connecting Node.js to relational (MySQL, PostgreSQL) and NoSQL (MongoDB) databases. Performing CRUD operations and handling database connections efficiently.

  Also filed under Software Engineering, Machine Learning

• ML Cheatsheet Documentation

  Author Unknown · Guide

  Warning: If you find errors, please raise an issue or contribute a better definition!

  Also filed under Data Visualization, Math

• Python for Excel

  Felix Zumstein · Book

  "Python for Excel" by Felix Zumstein is a comprehensive guide that demonstrates how to integrate Python into Excel workflows, enhancing data analysis and automation capabilities for experienced Excel users. As the creator of xlwings, a popular open-source package for automating Excel with Python, Zumstein provides practical insights and tools to bridge the gap between these two platforms. Key Features: Introduction to Python in Excel: The book begins by introducing Python as a powerful tool for Excel users, emphasizing its advantages over traditional VBA scripting. It guides readers through setting up modern development environments, including Jupyter notebooks and Visual Studio Code, to streamline their workflow. Data Analysis with pandas: Readers learn to utilize the pandas library to acquire, clean, and analyze data, effectively replacing typical Excel calculations with more robust Python operations. This section covers data manipulation techniques essential for efficient analysis. Automation of Excel Tasks: The book provides strategies to automate repetitive tasks such as consolidating workbooks and generating reports. By leveraging Python scripts, users can significantly reduce manual effort and increase productivity. Building Interactive Tools with xlwings: Zumstein delves into using xlwings to create interactive Excel tools that harness Python as a calculation engine. This enables the development of sophisticated applications within the familiar Excel interface. Database Integration and Web Data Retrieval: The guide covers connecting Excel to external databases and importing data from the web using Python, expanding the scope of data sources available for analysis. Unified Tool for Automation and Analysis: By combining Python with Excel, users can replace multiple tools such as VBA, Power Query, and Power Pivot, creating a cohesive and versatile environment for data tasks. For hands-on practice, the author provides a companion repository containing all relevant Jupyter notebooks and code samples, facilitating practical application of the concepts discussed.

  Also filed under Python, Data Visualization

• Python in Excel

  Hayden Van Der Post · Book

  Excel, a stalwart of data manipulation, visualization, and analysis, is ubiquitous in business environments. Python, on the other hand, brings unparalleled versatility and efficiency to data handling tasks. Integrating these two can significantly enhance your data processing capabilities, streamline workflows, and open up new possibilities for advanced analytics. The Foundation: Why Integrate Python with Excel?

  Also filed under Python, Data Visualization

• Simple Devops Projects

  Author Unknown · Notes

  • GitHub - As Distributed version control system. • Jenkins - Continous Integration tool. • Anisible - Configuration Management & Deployment tool. • docker -Containerization • Kubernetes - As Container Management Tool.

  Also filed under Software Engineering, Data Visualization

• The Analytics Setup Guidebook

  Huy Nguyen, Ha Pham, Cedric Chin · Book

  "The Analytics Setup Guidebook," authored by Huy Nguyen, Ha Pham, and Cedric Chin, serves as a practical manual for constructing modern, scalable analytics and business intelligence (BI) systems. Targeted primarily at technical team members embarking on setting up analytics infrastructures, the book offers a structured approach to understanding and implementing contemporary data analytics architectures. Key Concepts Covered: High-Level Overview of Analytics Systems: The guidebook introduces the components of modern analytics stacks, emphasizing their interconnections and roles in data processing pipelines. Data Consolidation: It discusses strategies for centralizing data from diverse sources, highlighting best practices in Extract, Load, Transform (ELT) processes and the rationale behind the industry's shift from traditional Extract, Transform, Load (ETL) methods. Data Transformation and Modeling: The book delves into transforming and modeling data to align with business objectives, comparing modern approaches to traditional methodologies and offering insights into efficient data structuring. Data Utilization: It explores the application of data through various means such as ad-hoc reporting, data exploration, and visualization, providing guidance on selecting appropriate BI tools and understanding their roles within the analytics ecosystem.

  Also filed under Data Visualization, Math

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