Databases / Data management Books
Pearson Education (US) Data Analytics for IT Networks: Developing
Book SynopsisUse data analytics to drive innovation and value throughout your network infrastructure Network and IT professionals capture immense amounts of data from their networks. Buried in this data are multiple opportunities to solve and avoid problems, strengthen security, and improve network performance. To achieve these goals, IT networking experts need a solid understanding of data science, and data scientists need a firm grasp of modern networking concepts. Data Analytics for IT Networks fills these knowledge gaps, allowing both groups to drive unprecedented value from telemetry, event analytics, network infrastructure metadata, and other network data sources. Drawing on his pioneering experience applying data science to large-scale Cisco networks, John Garrett introduces the specific data science methodologies and algorithms network and IT professionals need, and helps data scientists understand contemporary network technologies, applications, and data sources. After establishing this shared understanding, Garrett shows how to uncover innovative use cases that integrate data science algorithms with network data. He concludes with several hands-on, Python-based case studies reflecting Cisco Customer Experience (CX) engineers’ supporting its largest customers. These are designed to serve as templates for developing custom solutions ranging from advanced troubleshooting to service assurance. Understand the data analytics landscape and its opportunities in Networking See how elements of an analytics solution come together in the practical use cases Explore and access network data sources, and choose the right data for your problem Innovate more successfully by understanding mental models and cognitive biases Walk through common analytics use cases from many industries, and adapt them to your environment Uncover new data science use cases for optimizing large networks Master proven algorithms, models, and methodologies for solving network problems Adapt use cases built with traditional statistical methods Use data science to improve network infrastructure analysisAnalyze control and data planes with greater sophistication Fully leverage your existing Cisco tools to collect, analyze, and visualize data Table of Contents Foreword xvii Introduction: Your future is in your hands! xviiiChapter 1 Getting Started with Analytics 1 What This Chapter Covers 1 Data: You as the SME 2 Use-Case Development with Bias and Mental Models 2 Data Science: Algorithms and Their Purposes 3 What This Book Does Not Cover 4 Building a Big Data Architecture 4 Microservices Architectures and Open Source Software 5 R Versus Python Versus SAS Versus Stata 6 Databases and Data Storage 6 Cisco Products in Detail 6 Analytics and Literary Perspectives 7 Analytics Maturity 7 Knowledge Management 8 Gartner Analytics 8 Strategic Thinking 9 Striving for “Up and to the Right” 9 Moving Your Perspective 10 Hot Topics in the Literature 11 Summary 12Chapter 2 Approaches for Analytics and Data Science 13 Model Building and Model Deployment 14 Analytics Methodology and Approach 15 Common Approach Walkthrough 16 Distinction Between the Use Case and the Solution 18 Logical Models for Data Science and Data 19 Analytics as an Overlay 20 Analytics Infrastructure Model 22 Summary 33Chapter 3 Understanding Networking Data Sources 35 Planes of Operation on IT Networks 36 Review of the Planes 40 Data and the Planes of Operation 42 Planes Data Examples 44 A Wider Rabbit Hole 49 A Deeper Rabbit Hole 51 Summary 53Chapter 4 Accessing Data from Network Components 55 Methods of Networking Data Access 55 Pull Data Availability 57 Push Data Availability 61 Control Plane Data 67 Data Plane Traffic Capture 68 Packet Data 70 Other Data Access Methods 74 Data Types and Measurement Considerations 76 Numbers and Text 77 Data Structure 82 Data Manipulation 84 Other Data Considerations 87 External Data for Context 89 Data Transport Methods 89 Transport Considerations for Network Data Sources 90 Summary 96Chapter 5 Mental Models and Cognitive Bias 97 Changing How You Think 98 Domain Expertise, Mental Models, and Intuition 99 Mental Models 99 Daniel Kahneman’s System 1 and System 2 102 Intuition 103 Opening Your Mind to Cognitive Bias 104 Changing Perspective, Using Bias for Good 105 Your Bias and Your Solutions 106 How You Think: Anchoring, Focalism, Narrative Fallacy, Framing, and Priming 107 How Others Think: Mirroring 110 What Just Happened? Availability, Recency, Correlation, Clustering, and Illusion of Truth 111 Enter the Boss: HIPPO and Authority Bias 113 What You Know: Confirmation, Expectation, Ambiguity, Context, and Frequency Illusion 114 What You Don’t Know: Base Rates, Small Numbers, Group Attribution, and Survivorship 117 Your Skills and Expertise: Curse of Knowledge, Group Bias, and Dunning-Kruger 119 We Don’t Need a New System: IKEA, Not Invented Here, Pro-Innovation, Endowment, Status Quo, Sunk Cost, Zero Price, and Empathy 121 I Knew It Would Happen: Hindsight, Halo Effect, and Outcome Bias 123 Summary 124Chapter 6 Innovative Thinking Techniques 127 Acting Like an Innovator and Mindfulness 128 Innovation Tips and Techniques 129 Developing Analytics for Your Company 140 Defocusing, Breaking Anchors, and Unpriming 140 Lean Thinking 142 Cognitive Trickery 143 Quick Innovation Wins 143 Summary 144Chapter 7 Analytics Use Cases and the Intuition Behind Them 147 Analytics Definitions 150 How to Use the Information from This Chapter 151 Priming and Framing Effects 151 Analytics Rube Goldberg Machines 151 Popular Analytics Use Cases 152 Machine Learning and Statistics Use Cases 153 Common IT Analytics Use Cases 170 Broadly Applicable Use Cases 199 Some Final Notes on Use Cases 214 Summary 214Chapter 8 Analytics Algorithms and the Intuition Behind Them 217 About the Algorithms 217 Algorithms and Assumptions 218 Additional Background 219 Data and Statistics 221 Statistics 221 Correlation 224 Longitudinal Data 225 ANOVA 227 Probability 228 Bayes’ Theorem 228 Feature Selection 230 Data-Encoding Methods 232 Dimensionality Reduction 233 Unsupervised Learning 234 Clustering 234 Association Rules 240 Sequential Pattern Mining 243 Collaborative Filtering 244 Supervised Learning 246 Regression Analysis 246 Classification Algorithms 248 Decision Trees 249 Random Forest 250 Gradient Boosting Methods 251 Neural Networks 252 Support Vector Machines 258 Time Series Analysis 259 Text and Document Analysis 262 Natural Language Processing (NLP) 262 Information Retrieval 263 Topic Modeling 265 Sentiment Analysis 266 Other Analytics Concepts 267 Artificial Intelligence 267 Confusion Matrix and Contingency Tables 267 Cumulative Gains and Lift 269 Simulation 271 Summary 271Chapter 9 Building Analytics Use Cases 273 Designing Your Analytics Solutions 274 Using the Analytics Infrastructure Model 275 About the Upcoming Use Cases 276 The Data 276 The Data Science 278 The Code 280 Operationalizing Solutions as Use Cases 281 Understanding and Designing Workflows 282 Tips for Setting Up an Environment to Do Your Own Analysis 282 Summary 284Chapter 10 Developing Real Use Cases: The Power of Statistics 285 Loading and Exploring Data 286 Base Rate Statistics for Platform Crashes 288 Base Rate Statistics for Software Crashes 299 ANOVA 305 Data Transformation 310 Tests for Normality 311 Examining Variance 313 Statistical Anomaly Detection 318 Summary 321Chapter 11 Developing Real Use Cases: Network Infrastructure Analytics 323 Human DNA and Fingerprinting 324 Building Search Capability 325 Loading Data and Setting Up the Environment 325 Encoding Data for Algorithmic Use 328 Search Challenges and Solutions 331 Other Uses of Encoded Data 336 Dimensionality Reduction 337 Data Visualization 340 K-Means Clustering 344 Machine Learning Guided Troubleshooting 350 Summary 353Chapter 12 Developing Real Use Cases: Control Plane Analytics Using Syslog Telemetry 355 Data for This Chapter 356 OSPF Routing Protocols 357 Non-Machine Learning Log Analysis Using pandas 357 Noise Reduction 360 Finding the Hotspots 362 Machine Learning—Based Log Evaluation 366 Data Visualization 367 Cleaning and Encoding Data 369 Clustering 373 More Data Visualization 375 Transaction Analysis 379 Task List 386 Summary 387Chapter 13 Developing Real Use Cases: Data Plane Analytics 389 The Data 390 SME Analysis 394 SME Port Clustering 407 Machine Learning: Creating Full Port Profiles 413 Machine Learning: Creating Source Port Profiles 419 Asset Discovery 422 Investigation Task List 423 Summary 424Chapter 14 Cisco Analytics 425 Architecture and Advisory Services for Analytics 426 Stealthwatch 427 Digital Network Architecture (DNA) 428 AppDynamics 428 Tetration 430 Crosswork Automation 431 IoT Analytics 432 Analytics Platforms and Partnerships 433 Cisco Open Source Platform 433 Summary 434Chapter 15 Book Summary 435 Analytics Introduction and Methodology 436 All About Networking Data 438 Using Bias and Innovation to Discover Solutions 439 Analytics Use Cases and Algorithms 439 Building Real Analytics Use Cases 440 Cisco Services and Solutions 442 In Closing 442Appendix A Function for Parsing Packets from pcap Files 4439781587145131, TOC, 9/19/18
£40.49
Manning Publications Essential Graphrag
Book Synopsis
£41.56
McGraw-Hill Education ISE Database System Concepts
Book SynopsisDatabase System Concepts by Silberschatz, Korth and Sudarshan is now in its 7th edition and is one of the cornerstone texts of database education. It presents the fundamental concepts of database management in an intuitive manner geared toward allowing students to begin working with databases as quickly as possible.The text is designed for a first course in databases at the junior/senior undergraduate level or the first year graduate level. It also contains additional material that can be used as supplements or as introductory material for an advanced course. Because the authors present concepts as intuitive descriptions, a familiarity with basic data structures, computer organization, and a high-level programming language are the only prerequisites. Important theoretical results are covered, but formal proofs are omitted. In place of proofs, figures and examples are used to suggest why a result is true. Table of ContentsChapter 1: IntroductionPart 1: Relational LanguagesChapter 2: Introduction to the Relational ModelChapter 3: Introduction to SQLChapter 4: Intermediate SQLChapter 5: Advanced SQLPart II: Database DesignChapter 6: Database Design Using the E-R ModelChapter 7: Relational Database DesignPart III: Application Design and DevelopmentChapter 8: Complex Data TypesChapter 9: Application DevelopmentPart IV: Big Data AnalyticsChapter 10: Big DataChapter 11: Data AnalyticsPart V: Storage Management and IndexingChapter 12: Physical Storage SystemsChapter 13: Data Storage StructuresChapter 14: IndexingPart VI: Query Processing and OptimizationChapter 15: Query ProcessingChapter 16: Query OptimizationPart VII: Transaction ManagementChapter 17: TransactionsChapter 18: Concurrency ControlChapter 19: Recovery SystemPart VIII: Parallel and Distributed DatabasesChapter 20: Database-System ArchitecturesChapter 21: Parallel and Distributed StorageChapter 22: Parallel and Distributed Query ProcessingChapter 23: Parallel and Distributed Transaction ProcessingPart IX: Advanced TopicsChapter 24: Advanced Indexing TechniquesChapter 25: Advanced Application DevelopmentChapter 26: Blockchain DatabasesPart X: Appendix AAppendix A: Detailed University SchemaPart XI: Online ChaptersChapter 27: Formal Relational Query LanguagesChapter 28: Advanced Relational Database DesignChapter 29: Object-Based DatabasesChapter 30: XMLChapter 31: Information RetrievalChapter 32: PostgreSQL
£59.39
Packt Publishing Limited Hands-On Big Data Modeling: Effective database
Book SynopsisSolve all big data problems by learning how to create efficient data modelsKey Features Create effective models that get the most out of big data Apply your knowledge to datasets from Twitter and weather data to learn big data Tackle different data modeling challenges with expert techniques presented in this book Book DescriptionModeling and managing data is a central focus of all big data projects. In fact, a database is considered to be effective only if you have a logical and sophisticated data model. This book will help you develop practical skills in modeling your own big data projects and improve the performance of analytical queries for your specific business requirements.To start with, you’ll get a quick introduction to big data and understand the different data modeling and data management platforms for big data. Then you’ll work with structured and semi-structured data with the help of real-life examples. Once you’ve got to grips with the basics, you’ll use the SQL Developer Data Modeler to create your own data models containing different file types such as CSV, XML, and JSON. You’ll also learn to create graph data models and explore data modeling with streaming data using real-world datasets.By the end of this book, you’ll be able to design and develop efficient data models for varying data sizes easily and efficiently.What you will learn Get insights into big data and discover various data models Explore conceptual, logical, and big data models Understand how to model data containing different file types Run through data modeling with examples of Twitter, Bitcoin, IMDB and weather data modeling Create data models such as Graph Data and Vector Space Model structured and unstructured data using Python and R Who this book is forThis book is great for programmers, geologists, biologists, and every professional who deals with spatial data. If you want to learn how to handle GIS, GPS, and remote sensing data, then this book is for you. Basic knowledge of R and QGIS would be helpful.Table of ContentsTable of Contents Introduction to Big Data and Data Management Data Modeling and Data Management platforms for Big Data Defining Data Model Categorizing Data Model Structures of Data Model Modeling Structured Data Modeling with Unstructured Data Modeling with Steaming Data Streaming Sensors Data Concept and Approaches of Big Data Management DBMS to BDMS Big Data Management Services and Vendors Modeling Twitter Feeds using Python Modeling Weather Data Points with Python Modeling IMDB Data Points with Python
£29.44
O'Reilly Media Database Internals
Book SynopsisWith this practical guide, Alex Petrov guides developers through the concepts behind modern database and storage engine internals. Throughout the book, you’ll explore relevant material gleaned from numerous books, papers, blog posts, and the source code of several open source databases.
£39.74
Pearson Education Introduction to Computer Security
Book SynopsisTable of Contents1 Introduction 11.1 Fundamental Concepts . . . . . . . . . . . . . . . . . . . . . 21.2 Access Control Models . . . . . . . . . . . . . . . . . . . . . 191.3 Cryptographic Concepts . . . . . . . . . . . . . . . . . . . . . 251.4 Implementation and Usability Issues . . . . . . . . . . . . . . 391.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 462 Physical Security 552.1 Physical Protections and Attacks . . . . . . . . . . . . . . . . 562.2 Locks and Safes . . . . . . . . . . . . . . . . . . . . . . . . . 572.3 Authentication Technologies . . . . . . . . . . . . . . . . . . . 712.4 Direct Attacks Against Computers . . . . . . . . . . . . . . . 882.5 Special-Purpose Machines . . . . . . . . . . . . . . . . . . . 992.6 Physical Intrusion Detection . . . . . . . . . . . . . . . . . . . 132.7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 3 Operating Systems Security 1133.1 Operating Systems Concepts . . . . . . . . . . . . . . . . . . 114 3.2 Process Security . . . . . . . . . . . . . . . . . . . . . . . . . 1303.3 Memory and Filesystem Security . . . . . . . . . . . . . . . . 136 3.4 Application Program Security . . . . . . . . . . . . . . . . . . 1493.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 4 Malware 173 4.1 Insider Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . 1744.2 Computer Viruses . . . . . . . . . . . . . . . . . . . . . . . . 1814.3 Malware Attacks . . . . . . . . . . . . . . . . . . . . . . . . . 1884.4 Privacy-Invasive Software . . . . . . . . . . . . . . . . . . . . 202 4.5 Countermeasures . . . . . . . . . . . . . . . . . . . . . . . . 2084.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 5 Network Security I 2215.1 Network Security Concepts . . . . . . . . . . . . . . . . . . . 2225.2 The Link Layer . . . . . . . . . . . . . . . . . . . . . . . . . . 2295.3 The Network Layer . . . . . . . . . . . . . . . . . . . . . . . . 2365.4 The Transport Layer . . . . . . . . . . . . . . . . . . . . . . . 2465.5 Denial-of-Service Attacks . . . . . . . . . . . . . . . . . . . . 256 5.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264 6 Network Security II 2696.1 The Application Layer and DNS . . . . . . . . . . . . . . . . . 2706.2 Firewalls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2876.3 Tunneling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292 6.4 Intrusion Detection . . . . . . . . . . . . . . . . . . . . . . . . 2996.5 Wireless Networking . . . . . . . . . . . . . . . . . . . . . . . 313 6.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322 7 Web Security 3277.1 The World Wide Web . . . . . . . . . . . . . . . . . . . . . . 3287.2 Attacks on Clients . . . . . . . . . . . . . . . . . . . . . . . . 347 7.3 Attacks on Servers . . . . . . . . . . . . . . . . . . . . . . . . 3687.4 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382 8 Cryptography 3878.1 Symmetric Cryptography . . . . . . . . . . . . . . . . . . . . 3888.2 Public-Key Cryptography . . . . . . . . . . . . . . . . . . . . . 4068.3 Cryptographic Hash Functions . . . . . . . . . . . . . . . . . 4178.4 Digital Signatures . . . . . . . . . . . . . . . . . . . . . . . . . 4218.5 Details on AES and RSA . . . . . . . . . . . . . . . . . . . . 4258.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439 9 Distributed-Applications Security 4879.1 Database
£66.49
Pearson Education (US) TSQL Fundamentals
Book SynopsisItzik Ben-Gan is a mentor with and co-founder of SolidQ. A Microsoft Data Platform MVP since 1999, Itzik has taught numerous training events around the world focused on T-SQL querying, query tuning, and programming. Itzik is the author of several books about T-SQL. He has written many articles for SQL Server Pro as well as articles and white papers for MSDN and The SolidQ Journal. Itzik's speaking engagements include Tech-Ed, SQL PASS, SQL Server Connections, presentations to various SQL Server user groups, and SolidQ events. Itzik is a subject-matter expert within SolidQ for its T-SQL related activities. He authored SolidQ's Advanced T-SQL and T-SQL Fundamentals courses and delivers them regularly worldwide. You can learn more about Itzik at http://tsql.solidq.com/.Table of ContentsCHAPTER 1: Background to T-SQL querying and programming CHAPTER 2: Single-table queries CHAPTER 3: Joins CHAPTER 4: Subqueries CHAPTER 5: Table expressions CHAPTER 6: Set operators CHAPTER 7: T-SQL for data analysis CHAPTER 8: Data modification CHAPTER 9: Temporal tables CHAPTER 10: Transactions and concurrency CHAPTER 11: SQL Graph CHAPTER 12: Programmable objects Appendix: Getting started
£32.29
Manning Publications Visualizing Graph Data
a huge range and FREE tracked UK delivery on ALL orders.
£26.99
Pearson Education Microsoft Power BI Visual Calculations
£31.99
O'Reilly Media Practical Statistics for Data Scientists
Book SynopsisCourses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not
£47.99
Pearson Education Limited Fundamentals of Database Systems Global Edition
Book SynopsisTable of Contents Part 1: Introduction to Databases Chapter 1: Databases and Database Users Chapter 2: Database Systems Concepts and Architecture Part 2: Conceptual Data Modeling and Database Design Chapter 3: Data Modeling Using the Entity Relationship (ER) Model Chapter 4: The Enhanced Entity Relationship (EER) Model Part 3: The Relational Data Model and SQL Chapter 5: The Relational Data Model and Relational Database Constraints Chapter 6: Basic SQL Chapter 7: More SQL: Complex Queries, Triggers, Views, and Schema Modification Chapter 8: The Relational Algebra and Relational Calculus Chapter 9: Relational Database Design by ER- and EER-to-Relational Mapping Part 4: Database Programming Techniques Chapter 10: Introduction to SQL Programming Techniques Chapter 11: Web Database Programming Using PHP Part 5: Object, Object-Relational, and XML: Concepts, Models, Languages, and Standards Chapter 12: Object and Object-Relational Databases Chapter 13: XLM: Extensible Markup Language Part 6: Database Design Theory and Normalization Chapter 14: Basics of Functional Dependencies and Normalization for Relational Databases Chapter 15: Relational Database Design Algorithms and Further Dependencies Part 7: File Structures, Hashing, Indexing, and Physical Database Design Chapter 16: Disc Storage, Basic File Structures, Hashing, and Modern Storage Architectures Chapter 17: Indexing Structures for Files and Physical Database Design Part 8: Query Processing and Optimization Chapter 18: Strategies for Query Processing Chapter 19: Query Optimization Part 9: Transaction Processing, Concurrency Control, and Recovering Chapter 20: Introduction to Transaction Processing Concepts and Theory Chapter 21: Concurrency Control Techniques Chapter 22: Database Recovery Techniques Part 10: Distributed Databases, NOSQL Systems, Cloud Computing, and Big Data Chapter 23: Distributed Database Concepts Chapter 24: NOSQL Databases and Big Data Storage Systems Chapter 25: Big Data Technologies Based on MapReduce and Hadoop Part 11: Advanced Database Models, Systems, and Applications Chapter 26: Enhanced Data Models: Introduction to Active, Temporal, Spatial, Multimedia, and Deductive Databases Chapter 27: Introduction to Information Retrieval and Web Search Chapter 28: Data Mining Concepts Chapter 29: Overview of Data Warehousing and OLAP Part 12: Additional Database Topics: Security Chapter 30: Database Security Appendix A: Alternative Diagrammatic Notations for ER Models Appendix B: Parameters of Disks Appendix C: Overview of the QBE Language Appendix D: Overview of the Hierarchical Data Model Appendix E: Overview of the Network Data Model
£65.22
APress Developing Data Migrations and Integrations with
Book SynopsisMigrate your data to Salesforce and build low-maintenance and high-performing data integrations to get the most out of Salesforce and make it a go-to place for all your organization''s customer information.When companies choose to roll out Salesforce, users expect it to be the place to find any and all Information related to a customer-the coveted Client 360 view. On the day you go live, users expect to see all their accounts, contacts, and historical data in the system. They also expect that data entered in other systems will be exposed in Salesforce automatically and in a timely manner. This book shows you how to migrate all your legacy data to Salesforce and then design integrations to your organization''s mission-critical systems. As the Salesforce platform grows more powerful, it also grows in complexity. Whether you are migrating data to Salesforce, or integrating with Salesforce, it is important to understand how these complexities need to be reflected in your desiTable of Contents
£49.49
O'Reilly Media Kafka The Definitive Guide
Book SynopsisWith this updated edition, application architects, developers, and production engineers new to the Kafka streaming platform will learn how to handle data in motion. Additional chapters cover Kafka's AdminClient API, transactions, new security features, and tooling changes.
£47.99
John Murray Press Reinventing Capitalism in the Age of Big Data
Book SynopsisA provocative look at how data is reinventing the market: where big firms will no longer be dominant.Trade ReviewIdeas on how best to organise a data economy are far and few between. This book offers plenty of food for thought * Ludwig Siegele, Technology Editor, The Economist *This landmark book . . . should challenge and inspire every corporate strategist and public policy maker * Philip Evans, Senior Advisor, The Boston Consulting Group *Anyone interested in the future of business should read this fascinating book . . . Reinventing Capitalism makes a compelling case that it will change the nature of the market itself. With brilliant insights, it explains how the shift from simple price signalling to data-rich preference matching will determine the winners and losers of the 21st century economy, and thoughtfully outlines steps to curb the excesses of this new environment * Kevin Werbach, The Wharton School, University of Pennsylvania *Praise for Big Data:An optimistic and practical look at the big data revolution - just the thing to get your head around the big changes already underway and the bigger changes to come * Cory Doctorow, Boing Boing *Teems with great insights on the new ways of harnessing information, and offers a convincing vision of the future. It is essential reading for anyone who uses - or is affected by - big data * Jeff Jonas, IBM Fellow & Chief Scientist, IBM Entity Analytics *An excellent primer * Financial Times *Fascinating * Observer *
£11.69
Pearson Education (US) Foundations of Deep Reinforcement Learning
Book Synopsis Laura Graesser is a research software engineer working in robotics at Google. She holds a master's degree in computer science from New York University, where she specialized in machine learning. Wah Loon Keng is an AI engineer at Machine Zone, where he applies deep reinforcement learning to industrial problems. He has a background in both theoretical physics and computer science. Trade Review“This book provides an accessible introduction to deep reinforcement learning covering the mathematical concepts behind popular algorithms as well as their practical implementation. I think the book will be a valuable resource for anyone looking to apply deep reinforcement learning in practice.” –Volodymyr Mnih, lead developer of DQN “An excellent book to quickly develop expertise in the theory, language, and practical implementation of deep reinforcement learning algorithms. A limpid exposition which uses familiar notation; all the most recent techniques explained with concise, readable code, and not a page wasted in irrelevant detours: it is the perfect way to develop a solid foundation on the topic.” –Vincent Vanhoucke, principal scientist, Google “As someone who spends their days trying to make deep reinforcement learning methods more useful for the general public, I can say that Laura and Keng’s book is a welcome addition to the literature. It provides both a readable introduction to the fundamental concepts in reinforcement learning as well as intuitive explanations and code for many of the major algorithms in the field. I imagine this will become an invaluable resource for individuals interested in learning about deep reinforcement learning for years to come.” –Arthur Juliani, senior machine learning engineer, Unity Technologies “Until now, the only way to get to grips with deep reinforcement learning was to slowly accumulate knowledge from dozens of different sources. Finally, we have a book bringing everything together in one place.” –Matthew Rahtz, ML researcher, ETH ZürichTable of ContentsForeword xixPreface xxiAcknowledgments xxvAbout the Authors xxvii Chapter 1: Introduction to Reinforcement Learning 1 1.1 Reinforcement Learning 1 1.2 Reinforcement Learning as MDP 6 1.3 Learnable Functions in Reinforcement Learning 9 1.4 Deep Reinforcement Learning Algorithms 11 1.5 Deep Learning for Reinforcement Learning 17 1.6 Reinforcement Learning and Supervised Learning 19 1.7 Summary 21 Part I: Policy-Based and Value-Based Algorithms 23 Chapter 2: REINFORCE 25 2.1 Policy 26 2.2 The Objective Function 26 2.3 The Policy Gradient 27 2.4 Monte Carlo Sampling 30 2.5 REINFORCE Algorithm 31 2.6 Implementing REINFORCE 33 2.7 Training a REINFORCE Agent 44 2.8 Experimental Results 47 2.9 Summary 51 2.10 Further Reading 51 2.11 History 51 Chapter 3: SARSA 53 3.1 The Q- and V-Functions 54 3.2 Temporal Difference Learning 56 3.3 Action Selection in SARSA 65 3.4 SARSA Algorithm 67 3.5 Implementing SARSA 69 3.6 Training a SARSA Agent 74 3.7 Experimental Results 76 3.8 Summary 78 3.9 Further Reading 79 3.10 History 79 Chapter 4: Deep Q-Networks (DQN) 81 4.1 Learning the Q-Function in DQN 82 4.2 Action Selection in DQN 83 4.3 Experience Replay 88 4.4 DQN Algorithm 89 4.5 Implementing DQN 91 4.6 Training a DQN Agent 96 4.7 Experimental Results 99 4.8 Summary 101 4.9 Further Reading 102 4.10 History 102 Chapter 5: Improving DQN 103 5.1 Target Networks 104 5.2 Double DQN 106 5.3 Prioritized Experience Replay (PER) 109 5.4 Modified DQN Implementation 112 5.5 Training a DQN Agent to Play Atari Games 123 5.6 Experimental Results 128 5.7 Summary 132 5.8 Further Reading 132 Part II: Combined Methods 133Chapter 6: Advantage Actor-Critic (A2C) 135 6.1 The Actor 136 6.2 The Critic 136 6.3 A2C Algorithm 141 6.4 Implementing A2C 143 6.5 Network Architecture 148 6.6 Training an A2C Agent 150 6.7 Experimental Results 157 6.8 Summary 161 6.9 Further Reading 162 6.10 History 162 Chapter 7: Proximal Policy Optimization (PPO) 165 7.1 Surrogate Objective 165 7.2 Proximal Policy Optimization (PPO) 174 7.3 PPO Algorithm 177 7.4 Implementing PPO 179 7.5 Training a PPO Agent 182 7.6 Experimental Results 188 7.7 Summary 192 7.8 Further Reading 192 Chapter 8: Parallelization Methods 195 8.1 Synchronous Parallelization 196 8.2 Asynchronous Parallelization 197 8.3 Training an A3C Agent 200 8.4 Summary 203 8.5 Further Reading 204 Chapter 9: Algorithm Summary 205Part III: Practical Details 207Chapter 10: Getting Deep RL to Work 209 10.1 Software Engineering Practices 209 10.2 Debugging Tips 218 10.3 Atari Tricks 228 10.4 Deep RL Almanac 231 10.5 Summary 238 Chapter 11: SLM Lab 239 11.1 Algorithms Implemented in SLM Lab 239 11.2 Spec File 241 11.3 Running SLM Lab 246 11.4 Analyzing Experiment Results 247 11.5 Summary 249 Chapter 12: Network Architectures 251 12.1 Types of Neural Networks 251 12.2 Guidelines for Choosing a Network Family 256 12.3 The Net API 262 12.4 Summary 271 12.5 Further Reading 271 Chapter 13: Hardware 273 13.1 Computer 273 13.2 Data Types 278 13.3 Optimizing Data Types in RL 280 13.4 Choosing Hardware 285 13.5 Summary 285 Part IV: Environment Design 287Chapter 14: States 289 14.1 Examples of States 289 14.2 State Completeness 296 14.3 State Complexity 297 14.4 State Information Loss 301 14.5 Preprocessing 306 14.6 Summary 313 Chapter 15: Actions 315 15.1 Examples of Actions 315 15.2 Action Completeness 318 15.3 Action Complexity 319 15.4 Summary 323 15.5 Further Reading: Action Design in Everyday Things 324 Chapter 16: Rewards 327 16.1 The Role of Rewards 327 16.2 Reward Design Guidelines 328 16.3 Summary 332 Chapter 17: Transition Function 333 17.1 Feasibility Checks 333 17.2 Reality Check 335 17.3 Summary 337 Epilogue 338Appendix A: Deep Reinforcement Learning Timeline 343Appendix B: Example Environments 345 B.1 Discrete Environments 346 B.2 Continuous Environments 350 References 353Index 363
£34.19
Pearson Education Modern Information Retrieval
Book SynopsisThis is a rigorous and complete textbook for a first course on information retrieval from the computer science perspective. It provides an up-to-date student oriented treatment of information retrieval including extensive coverage of new topics such as web retrieval, web crawling, open source search engines and user interfaces.Table of Contents Contents Preface Acknowledgements 1 Introduction 2 User Interfaces for Search by Marti Hearst 3 Modeling 4 Retrieval Evaluation 5 Relevance Feedback and Query Expansion 6 Documents: Languages & Properties with Gonzalo Navarro and Nivio Ziviani 7 Queries: Languages & Properties with Gonzalo Navarro 8 Text Classification with Marcos Gon¸calves 9 Indexing and Searching with Gonzalo Navarro 10 Parallel and Distributed IR with Eric Brown 11 Web Retrieval with Yoelle Maarek 12 Web Crawling with Carlos Castillo 13 Structured Text Retrieval with Mounia Lalmas 14 Multimedia Information Retrieval by Dulce Poncele´on and Malcolm Slaney 15 Enterprise Search by David Hawking 16 Library Systems by Edie Rasmussen 17 Digital Libraries by Marcos Gon¸calves A Open Source Search Engines with Christian Middleton B Biographies Bibliography Index
£64.59
Random House USA Inc AZ Password Book
Book SynopsisThis large-format, alphabetized password book is organized by tabs per letter, making it easy, fast, and safe to store and locate important login information of all kinds! Individual tabs for each letter—no more tabs cramming multiple letters into the same space! Perfect for faster lookups and better organization. Removable sticker to go incognito! Don''t want text on the cover sharing that it’s a password book? Peel it off! Bonus security tips to encourage maximized online safety. What to do (and what not to do) to stay ahead of scammers. Large trim size for extra space to record over 400 accounts, including important notes, password changes, and non-traditional records such as crypto logins.
£8.99
O'Reilly Media Feature Engineering for Machine Learning
Book SynopsisFeature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you'll learn techniques for extracting and transforming featuresthe numeric representations of raw datainto formats for machine-learning models.
£39.74
The Pragmatic Programmers SQL Antipatterns, Volume 1: Avoiding the Pitfalls
Book SynopsisSQL is the ubiquitous language for software developers working with structured data. Most developers who rely on SQL are experts in their favorite language (such as Java, Python, or Go), but they're not experts in SQL. They often depend on antipatterns - solutions that look right but become increasingly painful to work with as you uncover their hidden costs. Learn to identify and avoid many of these common blunders. Refactor an inherited nightmare into a data model that really works. Updated for the current versions of MySQL and Python, this new edition adds a dozen brand new mini-antipatterns for quick wins. No matter which platform, framework, or language you use, the database is the foundation of your application, and the SQL database language is the standard for working with it. Antipatterns are solutions that look simple at the surface, but soon mire you down with needless work. Learn to identify these traps, and craft better solutions for the often-asked questions in this book. Avoid the mistakes that lead to poor performance and quality, and master the principles that make SQL a powerful and flexible tool for handling data and logic. Dive deep into SQL and database design, and learn to recognize the most common missteps made by software developers in database modeling, SQL query logic, and code design of data-driven applications. See practical examples of misconceptions about SQL that can lure software projects astray. Find the greatest value in each group of data. Understand why an intersection table may be your new best friend. Store passwords securely and don't reinvent the wheel. Handle NULL values like a pro. Defend your web applications against the security weakness of SQL injection. Use SQL the right way - it can save you from headaches and needless work, and let your application really shine! What You Need: The SQL examples use the MySQL 8.0 flavor, but other popular brands of RDBMS are mentioned. Other code examples use Python 3.9+ or Ruby 2.7+.
£34.65
O'Reilly Media Deep Learning for Coders with fastai and PyTorch
Book SynopsisAuthors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.
£47.99
O'Reilly Media Implementing Data Mesh
Book Synopsis
£47.99
Pearson Education (US) Python for Programmers
Book Synopsis Paul Deitel, CEO and Chief Technical Officer of Deitel & Associates, Inc., is a graduate of MIT, where he studied Information Technology. Through Deitel & Associates, Inc., he has delivered hundreds of programming courses worldwide to clients, including Cisco, IBM, Siemens, Sun Microsystems, Dell, Fidelity, NASA at the Kennedy Space Center, the National Severe Storm Laboratory, White Sands Missile Range, Rogue Wave Software, Boeing, SunGard Higher Education, Nortel Networks, Puma, iRobot, Invensys and many more. He and his co-author, Dr. Harvey M. Deitel, are the world's best-selling programming-language textbook/professional book/video authors. Dr. Harvey Deitel, Chairman and Chief Strategy Officer of Deitel & Associates, Inc., has over 50 years of experience in the computer field. Dr. Deitel earned B.S. and M.S. degrees in Electrical Engineering from MIT and a Ph.D. in Mathematics from Boston University. He has extensive college teaching experienTrade Review“The chapters are clearly written with detailed explanations of the example code. The modular structure, wide range of contemporary data science topics, and code in companion Jupyter notebooks make this a fantastic resource for readers of a variety of backgrounds. Fabulous Big Data chapter—it covers all of the relevant programs and platforms. Great Watson chapter! The chapter provides a great overview of the Watson applications. Also, your translation examples are great because they provide an ‘instant reward’—it’s very satisfying to implement a task and receive results so quickly. Machine Learning is a huge topic, and the chapter serves as a great introduction. I loved the California housing data example—very relevant for business analytics. The chapter was visually stunning.” —Alison Sanchez, Assistant Professor in Economics, University of San Diego “A great introduction to Big Data concepts, notably Hadoop, Spark, and IoT. The examples are extremely realistic and practical. The authors do an excellent job of combining programming and data science topics. The material is presented in digestible sections accompanied by engaging interactive examples. Nearly all concepts are accompanied by a worked-out example. A comprehensive overview of object-oriented programming in Python—the use of card image graphics is sure to engage the reader.” —Garrett Dancik, Eastern Connecticut State University “Covers some of the most modern Python syntax approaches and introduces community standards for style and documentation. The machine learning chapter does a great job of walking people through the boilerplate code needed for ML in Python. The case studies accomplish this really well. The later examples are so visual. Many of the model evaluation tasks make for really good programming practice. I can see readers feeling really excited about playing with the animations.” —Elizabeth Wickes, Lecturer, School of Information Sciences, University of Illinois at Urbana-Champaign “An engaging, highly accessible book that will foster curiosity and motivate beginning data scientists to develop essential foundations in Python programming, statistics, data manipulation, working with APIs, data visualization, machine learning, cloud computing, and more. Great walkthrough of the Twitter APIs—sentiment analysis piece is very useful. I’ve taken several classes that cover natural language processing and this is the first time the tools and concepts have been explained so clearly. I appreciate the discussion of serialization with JSON and pickling and when to use one or the other—with an emphasis on using JSON over pickle—good to know there’s a better, safer way!” —Jamie Whitacre, Data Science Consultant “For a while, I have been looking for a book in Data Science using Python that would cover the most relevant technologies. Well, my search is over. A must-have book for any practitioner of this field. The machine learning chapter is a real winner!! The dynamic visualization is fantastic.” —Ramon Mata-Toledo, Professor, James Madison University “I like the new combination of topics from computer science, data science, and stats. This is important for building data science programs that are more than just cobbling together math and computer science courses. A book like this may help facilitate expanding our offerings and using Python as a bridge for computer and data science topics. For a data science program that focuses on a single language (mostly), I think Python is probably the way to go.” —Lance Bryant, Shippensburg University “You’ll develop applications using industry standard libraries and cloud computing services.” —Daniel Chen, Data Scientist, Lander Analytics “Great introduction to Python! This book has my strongest recommendation both as an introduction to Python as well as Data Science.” —Shyamal Mitra, Senior Lecturer, University of Texas “IBM Watson is an exciting chapter. The code examples put together a lot of Watson services in a really nifty example.” —Daniel Chen, Data Scientist, Lander Analytics “Fun, engaging real-world examples will encourage readers to conduct meaningful data analyses. Provides many of the best explanations of data science concepts I’ve encountered. Introduces the most useful starter machine learning models—does a good job explaining how to choose the best model and what ‘the best’ means. Great overview of all the big data technologies with relevant examples.” —Jamie Whitacre, Data Science Consultant “A great introduction to deep learning.” —Alison Sanchez, University of San Diego “The best designed Intro to Data Science/Python book I have seen.” —Roland DePratti, Central Connecticut State University “I like the new combination of topics from computer science, data science, and stats.” —Lance Bryant, Shippensburg University “The book’s applied approach should engage readers. A fantastic job providing background on various machine learning concepts without burdening the users with too many mathematical details.” —Garrett Dancik, Assoc. Prof. of Computer Science/Bioinformatics, Eastern Connecticut State University “Helps readers leverage the large number of existing libraries to accomplish tasks with minimal code. Concepts are accompanied by rich Python examples that readers can adapt to implement their own solutions to data science problems. I like that cloud services are used.” —David Koop, Assistant Professor, U-Mass Dartmouth “I enjoyed the OOP chapter—doctest unit testing is nice because you can have the test in the actual docstring so things are traveling together. The line-by-line explanations of the static and dynamic visualizations of the die rolling example are just great.” —Daniel Chen, Data Scientist, Lander Analytics “A lucid exposition of the fundamentals of Python and Data Science. Thanks for pointing out seeding the random number generator for reproducibility. I like the use of dictionary and set comprehensions for succinct programming. ‘List vs. Array Performance: Introducing %timeit’ is convincing on why one should use ndarrays. Good defensive programming. Great section on Pandas Series and DataFrames—one of the clearest expositions that I have seen. The section on data wrangling is excellent. Natural Language Processing is an excellent chapter! I learned a tremendous amount going through it.” —Shyamal Mitra, Senior Lecturer, University of Texas “I like the discussion of exceptions and tracebacks. I really liked the Data Mining Twitter chapter; it focused on a real data source and brought in a lot of techniques for analysis (e.g., visualization, NLP). I like that the Python modules helped hide some of the complexity. Word clouds look cool.” —David Koop, Assistant Professor, U-Mass Dartmouth “I love the book! The examples are definitely a high point.” —Dr. Irene Bruno, George Mason University “I was very excited to see this book. I like its focus on data science and a general purpose language for writing useful data science programs. The data science portion distinguishes this book from most other introductory Python books.” —Dr. Harvey Siy, University of Nebraska at Omaha “I’ve learned a lot in this review process, discovering the exciting field of AI. I’ve liked the Deep Learning chapter, which has left me amazed with the things that have already been achieved in this field.” —José Antonio González Seco, Consultant “An impressive hands-on approach to programming meant for exploration and experimentation.” —Elizabeth Wickes, Lecturer, School of Information Sciences, University of Illinois at Urbana-Champaign “I was impressed at how easy it was to get started with NLP using Python. A meaningful overview of deep learning concepts, using Keras. I like the streaming example.” —David Koop, Assistant Professor, U-Mass Dartmouth “Really like the use of f-strings, instead of the older string-formatting methods. Seeing how easy TextBlob is compared to base NLTK was great. I never made word clouds with shapes before, but I can see this being a motivating example for people getting started with NLP. I’m enjoying the case-study chapters in the latter parts of the book. They are really practical. I really enjoyed working through all the Big Data examples, especially the IoT ones.” —Daniel Chen, Data Scientist, Lander Analytics “I really liked the live IPython input-output. The thing that I like most about this product is that it is a Deitel & Deitel book (I’m a big fan) that covers Python.” —Dr. Mark Pauley, University of Nebraska at Omaha Table of ContentsPreface xviiBefore You Begin xxxiiiChapter 1: Introduction to Computers and Python 11.1 Introduction 21.2 A Quick Review of Object Technology Basics 31.3 Python 51.4 It’s the Libraries! 71.5 Test-Drives: Using IPython and Jupyter Notebooks 91.6 The Cloud and the Internet of Things 161.7 How Big Is Big Data? 171.8 Case Study—A Big-Data Mobile Application 241.9 Intro to Data Science: Artificial Intelligence—at the Intersection of CS and Data Science 261.10 Wrap-Up 29Chapter 2: Introduction to Python Programming 312.1 Introduction 322.2 Variables and Assignment Statements 322.3 Arithmetic 332.4 Function print and an Intro to Single- and Double-Quoted Strings 362.5 Triple-Quoted Strings 382.6 Getting Input from the User 392.7 Decision Making: The if Statement and Comparison Operators 412.8 Objects and Dynamic Typing 452.9 Intro to Data Science: Basic Descriptive Statistics 462.10 Wrap-Up 48Chapter 3: Control Statements 493.1 Introduction 503.2 Control Statements 503.3 if Statement 513.4 if...else and if...elif...else Statements 523.5 while Statement 553.6 for Statement 553.7 Augmented Assignments 573.8 Sequence-Controlled Iteration; Formatted Strings 583.9 Sentinel-Controlled Iteration 593.10 Built-In Function range: A Deeper Look 603.11 Using Type Decimal for Monetary Amounts 613.12 break and continue Statements 643.13 Boolean Operators and, or and not 653.14 Intro to Data Science: Measures of Central Tendency—Mean, Median and Mode 673.15 Wrap-Up 69Chapter 4: Functions 714.1 Introduction 724.2 Defining Functions 724.3 Functions with Multiple Parameters 754.4 Random-Number Generation 764.5 Case Study: A Game of Chance 784.6 Python Standard Library 814.7 math Module Functions 824.8 Using IPython Tab Completion for Discovery 834.9 Default Parameter Values 854.10 Keyword Arguments 854.11 Arbitrary Argument Lists 864.12 Methods: Functions That Belong to Objects 874.13 Scope Rules 874.14 import: A Deeper Look 894.15 Passing Arguments to Functions: A Deeper Look 904.16 Recursion 934.17 Functional-Style Programming 954.18 Intro to Data Science: Measures of Dispersion 974.19 Wrap-Up 98Chapter 5: Sequences: Lists and Tuples 1015.1 Introduction 1025.2 Lists 1025.3 Tuples 1065.4 Unpacking Sequences 1085.5 Sequence Slicing 1105.6 del Statement 1125.7 Passing Lists to Functions 1135.8 Sorting Lists 1155.9 Searching Sequences 1165.10 Other List Methods 1175.11 Simulating Stacks with Lists 1195.12 List Comprehensions 1205.13 Generator Expressions 1215.14 Filter, Map and Reduce 1225.15 Other Sequence Processing Functions 1245.16 Two-Dimensional Lists 1265.17 Intro to Data Science: Simulation and Static Visualizations 1285.18 Wrap-Up 135Chapter 6: Dictionaries and Sets 1376.1 Introduction 1386.2 Dictionaries 1386.3 Sets 1476.4 Intro to Data Science: Dynamic Visualizations 1526.5 Wrap-Up 158Chapter 7: Array-Oriented Programming with NumPy 1597.1 Introduction 1607.2 Creating arrays from Existing Data 1607.3 array Attributes 1617.4 Filling arrays with Specific Values 1637.5 Creating arrays from Ranges 1647.6 List vs. array Performance: Introducing %timeit 1657.7 array Operators 1677.8 NumPy Calculation Methods 1697.9 Universal Functions 1707.10 Indexing and Slicing 1717.11 Views: Shallow Copies 1737.12 Deep Copies 1747.13 Reshaping and Transposing 1757.14 Intro to Data Science: pandas Series and DataFrames 1777.15 Wrap-Up 189Chapter 8: Strings: A Deeper Look 1918.1 Introduction 1928.2 Formatting Strings 1938.3 Concatenating and Repeating Strings 1968.4 Stripping Whitespace from Strings 1978.5 Changing Character Case 1978.6 Comparison Operators for Strings 1988.7 Searching for Substrings 1988.8 Replacing Substrings 1998.9 Splitting and Joining Strings 2008.10 Characters and Character-Testing Methods 2028.11 Raw Strings 2038.12 Introduction to Regular Expressions 2038.13 Intro to Data Science: Pandas, Regular Expressions and Data Munging 2108.14 Wrap-Up 214Chapter 9: Files and Exceptions 2179.1 Introduction 2189.2 Files 2199.3 Text-File Processing 2199.4 Updating Text Files 2229.5 Serialization with JSON 2239.6 Focus on Security: pickle Serialization and Deserialization 2269.7 Additional Notes Regarding Files 2269.8 Handling Exceptions 2279.9 finally Clause 2319.10 Explicitly Raising an Exception 2339.11 (Optional) Stack Unwinding and Tracebacks 2339.12 Intro to Data Science: Working with CSV Files 2359.13 Wrap-Up 241Chapter 10: Object-Oriented Programming 24310.1 Introduction 24410.2 Custom Class Account 24610.3 Controlling Access to Attributes 24910.4 Properties for Data Access 25010.5 Simulating “Private” Attributes 25610.6 Case Study: Card Shuffling and Dealing Simulation 25810.7 Inheritance: Base Classes and Subclasses 26610.8 Building an Inheritance Hierarchy; Introducing Polymorphism 26710.9 Duck Typing and Polymorphism 27510.10 Operator Overloading 27610.11 Exception Class Hierarchy and Custom Exceptions 27910.12 Named Tuples 28010.13 A Brief Intro to Python 3.7’s New Data Classes 28110.14 Unit Testing with Docstrings and doctest 28710.15 Namespaces and Scopes 29010.16 Intro to Data Science: Time Series and Simple Linear Regression 29310.17 Wrap-Up 301Chapter 11: Natural Language Processing (NLP) 30311.1 Introduction 30411.2 TextBlob 30511.3 Visualizing Word Frequencies with Bar Charts and Word Clouds 31911.4 Readability Assessment with Textatistic 32411.5 Named Entity Recognition with spaCy 32611.6 Similarity Detection with spaCy 32711.7 Other NLP Libraries and Tools 32811.8 Machine Learning and Deep Learning Natural Language Applications 32811.9 Natural Language Datasets 32911.10 Wrap-Up 330Chapter 12: Data Mining Twitter 33112.1 Introduction 33212.2 Overview of the Twitter APIs 33412.3 Creating a Twitter Account 33512.4 Getting Twitter Credentials—Creating an App 33512.5 What’s in a Tweet? 33712.6 Tweepy 34012.7 Authenticating with Twitter Via Tweepy 34112.8 Getting Information About a Twitter Account 34212.9 Introduction to Tweepy Cursors: Getting an Account’s Followers and Friends 34412.10 Searching Recent Tweets 34712.11 Spotting Trends: Twitter Trends API 34912.12 Cleaning/Preprocessing Tweets for Analysis 35312.13 Twitter Streaming API 35412.14 Tweet Sentiment Analysis 35912.15 Geocoding and Mapping 36212.16 Ways to Store Tweets 37012.17 Twitter and Time Series 37012.18 Wrap-Up 371Chapter 13: IBM Watson and Cognitive Computing 37313.1 Introduction: IBM Watson and Cognitive Computing 37413.2 IBM Cloud Account and Cloud Console 37513.3 Watson Services 37613.4 Additional Services and Tools 37913.5 Watson Developer Cloud Python SDK 38113.6 Case Study: Traveler’s Companion Translation App 38113.7 Watson Resources 39413.8 Wrap-Up 395Chapter 14: Machine Learning: Classification, Regression and Clustering 39714.1 Introduction to Machine Learning 39814.2 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 1 40314.3 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 2 41314.4 Case Study: Time Series and Simple Linear Regression 42014.5 Case Study: Multiple Linear Regression with the California Housing Dataset 42514.6 Case Study: Unsupervised Machine Learning, Part 1—Dimensionality Reduction 43814.7 Case Study: Unsupervised Machine Learning, Part 2—k-Means Clustering 44214.8 Wrap-Up 455Chapter 15: Deep Learning 45715.1 Introduction 45815.2 Keras Built-In Datasets 46115.3 Custom Anaconda Environments 46215.4 Neural Networks 46315.5 Tensors 46515.6 Convolutional Neural Networks for Vision; Multi-Classification with the MNIST Dataset 46715.7 Visualizing Neural Network Training with TensorBoard 48615.8 ConvnetJS: Browser-Based Deep-Learning Training and Visualization 48915.9 Recurrent Neural Networks for Sequences; Sentiment Analysis with the IMDb Dataset 48915.10 Tuning Deep Learning Models 49715.11 Convnet Models Pretrained on ImageNet 49815.12 Wrap-Up 499Chapter 16: Big Data: Hadoop, Spark, NoSQL and IoT 50116.1 Introduction 50216.2 Relational Databases and Structured Query Language (SQL) 50616.3 NoSQL and NewSQL Big-Data Databases: A Brief Tour 51716.4 Case Study: A MongoDB JSON Document Database 52016.5 Hadoop 53016.6 Spark 54116.7 Spark Streaming: Counting Twitter Hashtags Using the pyspark-notebook Docker Stack 55116.8 Internet of Things and Dashboards 56016.9 Wrap-Up 571Index 573
£42.74
Pearson Education (US) SQL Server 2022 Administration Inside Out
Book SynopsisRandolph West (they/them) lives in Calgary, Alberta, Canada, with a husband and two dogs. After being a consultant for millennia, Randolph now writes full-time at Microsoft Docs, still yelling at the screen. Occasional voice actor. Occasional blogger at bornsql.ca. Not to be trusted around chocolate. Yes, this is a short bio. William Assaf (he/him) is a senior content developer for Microsoft, writing Learn content for SQL Server, Azure SQL Database, Azure Synapse Analytics, and more. A long-time Baton Rougean, William and his adventure buddy Christine moved to Seattle during the pandemic. They love their new home but are still New Orleans Saints fans. Before joining Microsoft, William was a Data Platform MVP, SQL Saturday and SQL community organizer, and a long-time DBA and data consultant. As a consultant for 13 years, he worked with clients across the U.S. on SQL Server and Azure SQL platform optimization, management, data integration, disa
£38.24
McGraw-Hill Education - Europe Star Schema The Complete Reference
Book SynopsisPublisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.The definitive guide to dimensional design for your data warehouseLearn the best practices of dimensional design. Star Schema: The Complete Reference offers in-depth coverage of design principles and their underlying rationales. Organized around design concepts and illustrated with detailed examples, this is a step-by-step guidebook for beginners and a comprehensive resource for experts.This all-inclusive volume begins with dimensional design fundamentals and shows how they fit into diverse data warehouse architectures, including those of W.H. Inmon and Ralph Kimball. The book progresses throuTable of ContentsPart I: Fundamentals; Chapter 1: Analytic Databases and Dimensional Design; Chapter 2: Data Warehouse Architectures; Chapter 3: Stars and Cubes; Part II: Multiple Stars; Chapter 4: A Fact Table for Each Process; Chapter 5: Conformed Dimensions; Part III: Dimension Design; Chapter 6: More on Dimension Tables; Chapter 7: Hierarchies and Snowflakes; Chapter 8: More Slow Change Techniques; Chapter 9: Multi-Value Dimensions and Bridges; Chapter 10: Recursive Hierarchies and Bridges;Part IV: Fact Table Design; Chapter 11: Transactions, Snapshots and Accumulating Snapshots; Chapter 12: Factless Fact Tables; Chapter 13: Type-Specific Stars; Part V: Performance; Chapter 14: Derived Schemas; Chapter 15: Aggregates; Part VI: Tools and Documentation; Chapter 16: Design and Business Intelligence; Chapter 17: Design and ETL; Chapter 18: How to Design and Document a Dimensional Model; Index
£999.99
CRC Press Data Analytics for Internal Auditors
Book SynopsisThere are many webinars and training courses on Data Analytics for Internal Auditors, but no handbook written from the practitionerâs viewpoint covering not only the need and the theory, but a practical hands-on approach to conducting Data Analytics. The spread of IT systems makes it necessary that auditors as well as management have the ability to examine high volumes of data and transactions to determine patterns and trends. The increasing need to continuously monitor and audit IT systems has created an imperative for the effective use of appropriate data mining tools. This book takes an auditor from a zero base to an ability to professionally analyze corporate data seeking anomalies.Table of ContentsIntroduction to Data Analysis. Understanding Sampling. Judgmental vs Statistical Sampling. Probability theory in Data Analysis. Types of Evidence. Population Analysis. Correlations and Regressions. Conducting the Audit. Obtaining Information from IT Systems for Analysis. Use of Computer Assisted Audit Techniques. Analysis of Big Data. Results Analysis and Validation. Root Cause Analysis. Data Analysis and Continuous Monitoring. Continuous Auditing. Financial Analysis. Excel and Data Analysis. ACL and Data Analysis. IDEA and Data Analysis. Analysis Reporting.
£44.99
Taylor & Francis Ltd Statistics and Data Visualisation with Python
Book SynopsisThis book is intended to serve as a bridge in statistics for graduates and business practitioners interested in using their skills in the area of data science and analytics as well as statistical analysis in general. On the one hand, the book is intended to be a refresher for readers who have taken some courses in statistics, but who have not necessarily used it in their day-to-day work. On the other hand, the material can be suitable for readers interested in the subject as a first encounter with statistical work in Python. Statistics and Data Visualisation with Python aims to build statistical knowledge from the ground up by enabling the reader to understand the ideas behind inferential statistics and begin to formulate hypotheses that form the foundations for the applications and algorithms in statistical analysis, business analytics, machine learning, and applied machine learning. This book begins with the basics of programming in Python and data analysTable of Contents1. Data, Stats and Stories - An Introduction 2. Python Programming Primer 3. Snakes, Bears & Other Numerical Beasts: NumPy, SciPy & Pandas 4. The Measure of All Things - Statistics 5. Definitely Maybe: Probability and Distributions 6. Alluring Arguments and Ugly Facts - Statistical Modelling and Hypothesis Testing 7. Delightful Details - Data Visualisation 8. Dazzling Data Designs - Creating Charts A. Variance: Population v Sample B. Sum of First n Integers C. Sum of Squares of the First n Integers D. The Binomial Coefficient E. The Hypergeometric Distribution F. The Poisson Distribution G. The Normal Distribution H. Skewness and Kurtosis I. Kruskal-Wallis Test - No Ties
£44.99
Taylor & Francis Ltd Database Systems in Science and Engineering
Book SynopsisComputerized databases provide a powerful everyday tool for data handling by scientists and engineers. However, the unique nature of many technical tasks requires a specialized approach to make use of the many powerful commercial database tools now available. Using these tools has proved difficult because database technology is often shrouded in layers of jargon. An essential guide for scientists and engineers who use computers to avoid drowning in a flood of data, Database Systems in Science and Engineering dispels the myths associated with database design and breaks the barriers to successful databases. Using the language of scientists and engineers, this book explains concepts and problems, offers practical steps and solutions, and provides new ideas for better data handling. The first part of the book presents an overview of technical databases using examples taken from real applications and the current state of technical databases. The second part covers the computer impleTrade Review"… a valuable resource for scientists … a fine job of presenting the information necessary to plan, design, and use computerized technical databases …The language is clear, the treatment easy to follow, and the presentation is copiously illustrated with realistic diagrams and flow charts … the authors frequently emphasize that the user and his or her needs are the key to successful database design. Chapters on developing an effective user interface and on efficient dissemination of data provide specific suggestions for accomplishing this purpose." -Journal of Chemical Information and Computer SciencesTable of ContentsIntroduction to technical databases. The nature of technical data. Types of technical databases. The use of technical databases. User interfaces. The dissemination of technical databases. Data structures. Architecture of a database. Data models. Database planning. Database design. Expert systems.
£123.50
Taylor & Francis Ltd Big Data Concepts Technologies and Applications
Book SynopsisWith the advent of such advanced technologies as cloud computing, the Internet of Things, the Medical Internet of Things, the Industry Internet of Things and sensor networks as well as the exponential growth in the usage of Internet-based and social media platforms, there are enormous oceans of data. These huge volumes of data can be used for effective decision making and improved performance if analyzed properly. Due to its inherent characteristics, big data is very complex and cannot be handled and processed by traditional database management approaches. There is a need for sophisticated approaches, tools and technologies that can be used to store, manage and analyze these enormous amounts of data to make the best use of them.Big Data Concepts, Technologies, and Applications covers the concepts, technologies, and applications of big data analytics. Presenting the state-of-the-art technologies in use for big data analytics. it provides an in-depth discussiTable of ContentsSection A. Understanding Big Data Chapter 1. Overview of Big Data Chapter 2. Challenges of Big Data Chapter 3. Big Data Analytics Section B. Big Data Technologies Chapter 4. Hadoop Ecosystem Chapter 5. NoSQL Databases Chapter 6. Data Lakes Chapter 7. Deep Learning Chapter 8. Blockchain Section C. Big Data Applications Chapter 9. Big Data for Healthcare Chapter 10. Big Data Analytics for Fraud Detection and Prevention Chapter 11. Big Data Analytics in Social Media Chapter 12. Novel Applications and Research Directions in Big Data Analytics
£44.99
APress Pro Entity Framework Core 2 for ASP.NET Core MVC
Book SynopsisModel, map, and access data effectively with Entity Framework Core 2, the latest evolution of Microsoft''s object-relational mapping framework. You will access data utilizing .NET objects via the most common data access layer used in ASP.NET Core MVC 2 projects. Best-selling author Adam Freeman explains how to get the most from Entity Framework Core 2 in MVC projects. He begins by describing the different ways that Entity Framework Core 2 can model data and the different types of databases that can be used. He then shows you how to use Entity Framework Core 2 in your own MVC projects, starting from the nuts and bolts and building up to the most advanced and sophisticated features, going in-depth to give you the knowledge you need. Chapters include common problems and how to avoid them. What You''ll Learn Gain a solid architectural understanding of Entity Framework Core 2 CrTable of ContentsPart 1------1 - Entity Framework Core in Context2 - Your First Entity Framework Core Application3 - Working with Databases4 - SportsStore - A Real (Data) Application5 - SportsStore - Storing Data6 - SportsStore - Modifying Data7 - SportsStore - Expanding the Data Model8 - SportsStore - Scaling Up9 - SportsStore - Customer Features10 - SportsStore - Creating An APIPart 2-----11 - Working with Entity Framework Core12 - Performing Data Operations13 - Understanding Migrations14 - Creating Data Relationships15 - Working with Relationships, Part 116 - Working with Relationships, Part 217 - Scaffolding an Existing Database18 - Manually Modelling a DatabasePart 3-----19 - Keys20 - Querying Data21 - Storing Data22 - Deleting Data23 - Using Database Server Features24 - Using Transactions
£56.24
APress Simulation with Python
Book SynopsisUnderstand the theory and implementation of simulation. This book covers simulation topics from a scenario-driven approach using Python and rich visualizations and tabulations. The book discusses simulation used in the natural and social sciences and with simulations taken from the top algorithms used in the industry today. The authors use an engaging approach that mixes mathematics and programming experiments with beginning-intermediate level Python code to create an immersive learning experience that is cohesive and integrated. After reading this book, you will have an understanding of simulation used in natural sciences, engineering, and social sciences using Python.What You''ll Learn Use Python and numerical computation to demonstrate the power of simulation Choose a paradigm to run a simulation Draw statistical inTable of ContentsChapter 1: Calculating Pi and Beyond: Searching Order in Disorder with Simulation [30]Description: The beginning chapter will use Monte Carlo simulation as a topic to introduce some fundamental concepts in simulation.Topics to be covered: 1. Simulating Pi2. The goat problem and uniform sampling3. How to properly set a simulation environment Chapter 2: Markov Chain: A Peek into the Future [20]Description: Markov chain simulation will be introduced from both probabilistic perspective and matrix multiplication perspective.Topics to be covered: 1. How to predict weather?2. The transition matrix and stability states3. Markov chain Monte Carlo simulation Chapter 3: Multi-Armed Bandits: Probability Simulation and Bayesian Statistics [30]Description: Classical multi-armed bandits’ model will be introduced to continue the probabilistic perspective of the previous chapter. In addition, Bayesian statistics will be introduced.Topics to be covered: 1. Introduction to multi-armed bandit2. Greedy versus explorative strategies3. The interpretation of a Bayesian statistician. Chapter 4: Balls in 2D Box: A Simplest Physics Engine [20]Description: This chapter is mainly about event-driven simulation. It is not about simulation in the time space but in the event space.Topics to be covered: 1. Introduce the physics laws that govern motion2. Use event-driven paradigm to build a physics engine3. More realistic simulation with friction Chapter 5: Percolation: Threshold and Phase Change [25]Description: Phase changing is an important physics behavior for systems near critical boundaries. We are going to simulate critical behaviors using percolation as examples.Topics to be covered: 1. The concept of percolation and 2. Why dimension matters: 1D percolation and 2D percolation3. 3D percolation and even higher dimensionsChapter 6: Queuing System: How Stock Trades are Made [30]Description: As the first example in the business world, concepts in queuing systems are introduced and the simulation using basic data structures like queue and deque will be carried out.Topics to be covered: 1. Basic data structures in Python2. Microstructure of trading3. Simulating trading Chapter 7: Rock, Scissor and Paper: Multi-Agent Simulation [30]Description: Sometimes we want to simulate a system with multiple agents acting on their own behalf. In this chapter, we are going to run a multi-agent simulation and test the performance of different competing strategies in such a scenario.Topics to be covered: 1. Characteristics of multi-agent system2. Baseline strategies3. Analyzing nontrivial strategiesChapter 8: Matthew Effect and Tax Policy: Why the Rich Keeps Getting Richer[30]Description: Differential equation is an important field of study that governs a big group of phenomena. In this chapter, we are going to study it with a very relevant topic: wealth distribution in modern society. Topics to be covered: 1. Introduction of differential equations2. Matthew effect and ROI3. How tax policy can gauge social wealth distribution Chapter 9: Misinformation Spreading: Simulation on a Graph (Centrality, Networkx)[30]Description: Network simulation is another important domain. Nowadays social media like Twitter, Facebook and reddit can be easily modelled as a network. We will cover a simple simulation to study how misinformation can spread in a network and how we can fight against it.Topics to be covered: 1. Concepts of a network2. Simulate misinformation spreading in a directed network3. How to fight misinformation (or suppress freedom of expression)Chapter 10: Simulated Annealing and Genetic Algorithm [30]Description: There are two simulation algorithms widely used in research and industry that mimic natural phenomena. We are going to use them to solve two real world problems and explain the origin of their power.Topics to be covered: 4. Simulated Annealing Basics5. Use Simulated Annealing to solve an optimization problem6. Genetic Algorithm7. Use Genetic algorithm to solve an optimization problem
£37.49
O'Reilly Media Seeking SRE
Book SynopsisInspired by Site Reliability Engineering, the successful O'Reilly book, this book explores a very different part of the SRE space. The more than two dozen chapters in Seeking SRE bring you into some of the important conversations going on in the SRE world right now.
£35.99
O'Reilly Media The SelfService Data Roadmap
Book SynopsisData-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data.
£999.99
APress SQL Server 2005 T-SQL Recipes: A Problem-Solution
Book Synopsis* Comprehensive T-SQL Coverage, including all SQL Server 2005 new features, from an established SQL Server expert and author. * Broad appeal, with practical ‘How to’ answers to common SQL Server T-SQL questions for both novice and advanced DBAs and developers. * Unique, easy-reference format – ideal for preparing for a SQL Server job interview, or for a SQL Server certification testTable of ContentsA table of contents is not available for this title.
£37.49
BPB Publications Databricks Lakehouse Platform Cookbook: 100+
Book Synopsis
£26.59
Apress Unlocking dbt
£35.99
APress Transitioning to Microsoft Power Platform
Book SynopsisWelcome to this step-by-step guide for Excel users, data analysts, and finance specialists. It is designed to take you through practical report and development scenarios, including both the approach and the technical challenges. This book will equip you with an understanding of the overall Power Platform use case for addressing common business challenges. While Power BI continues to be an excellent tool of choice in the BI space, Power Platform is the real game changer. Using an integrated architecture, a small team of citizen developers can build solutions for all kinds of business problems. For small businesses, Power Platform can be used to build bespoke CRM, Finance, and Warehouse management tools. For large businesses, it can be used to build an integration point for existing systems to simplify reporting, operation, and approval processes.The author has drawn on his15 years of hands-on analytics experience to help you pivot from the traditional Excel-based rTable of Contents1. Power BI SolutionsGoal: as the introduction chapter, this chapter starts with the most popular tool in Power Platform. It covers the important components relating to the integrated architecture. The same components are also powerful in their own rights in building powerful reports. 2. Data VisualisationGoal: After covering the key components of Power BI, this chapter focus on the design and user experience, which is also a key component in a great report. 3. Power BI GovernanceGoal: The readers will understand that report governance is an enabler not a restrictor. This chapter break governance into 4 key components and discusses the needs in each area. 4. SQL ServerGoal: Most business data stores in SQL Server. SQL is by far the most common data language. The readers will understand the basics of SQL and able to write the most common queries.5. SharePoint ListGoal: The readers will understand how to setup and utilize SharePoint list as a security measure. 6. Power Automate SolutionsGoal: The readers will understand the basic concept of Power Automate as well as some practical applications. 7. Power Apps SolutionsGoal: PowerApps is another critical component in the book. This chapter will spend considerably more time in explaining the concept and construct. The readers will understand how to build PowerApps and how to integrate it with Power BI and Power Automate. 8. Integrated SolutionsGoal: In the final chapter of the book, readers will start to explore the full architecture. How different parts add value to the business application. The readers will understand the full potential of Power Platform. By this stage, the users also have the skillset required to implement such solutions at work.
£41.24
Microsoft Press,U.S. Tabular Modeling in Microsoft SQL Server Analysis
Book SynopsisWith SQL Server Analysis Services 2016, Microsoft has dramatically upgraded its Tabular approach to business intelligence data modeling, making Tabular the easiest and best solution for most new projects. In this book, two world-renowned experts in Microsoft data modeling and analysis cover all you need to know to create complete BI solutions with these powerful new tools. Marco Russo and Alberto Ferrari walk you step-by-step through creating powerful data models, and then illuminate advanced features such as optimization, deployment, and scalability. Tabular Modeling in Microsoft SQL Server Analysis Services will be indispensable for everyone moving to Analysis Services Tabular, regardless of their previous experience with tabular-style models or with Microsoft's older Analysis Services offerings. It will also be an essential follow-up for every reader of the authors' highly-praised Microsoft SQL Server 2012 Analysis Services: The BISM Tabular Model.Table of Contents CHAPTER 1 Introducing the tabular model CHAPTER 2 Getting started with the tabular model CHAPTER 3 Loading data inside Tabular CHAPTER 4 Introducing calculations in DAX CHAPTER 5 Building hierarchies CHAPTER 6 Data modeling in Tabular CHAPTER 7 Tabular Model Scripting Language (TMSL) CHAPTER 8 The tabular presentation layer CHAPTER 9 Using DirectQuery CHAPTER 10 Security CHAPTER 11 Processing and partitioning tabular models CHAPTER 12 Inside VertiPaq CHAPTER 13 Interfacing with Tabular CHAPTER 14 Monitoring and tuning a Tabular service CHAPTER 15 Optimizing tabular models CHAPTER 16 Choosing hardware and virtualization
£33.37
Pearson Education (US) MySQL Crash Course
Book SynopsisBen Forta is Adobe's Senior Director of Education Initiatives and has more than three decades of experience in the computer industryin product development, support, training, and product marketing. He is the author of the best-selling Sams Teach Yourself SQL in 10 Minutes (as well as spinoff titles like this one and versions on SQL Server T-SQL, Oracle PL/SQL, and MariaDB), Learning Regular Expressions, and Captain Code, which teaches Python to younger coders (and those young at heart), Java, Windows, and more. He has extensive experience in database design and development, has implemented databases for several highly successful commercial software programs and websites, and is a frequent lecturer and columnist on application development and Internet technologies. Ben lives in Oak Park, Michigan, with his wife, Dr. Marcy Forta, and their children. He welcomes your email at ben@forta.com and invites you to visit his website at http://forta.Table of ContentsChapter 1 Understanding SQL 1 Database Basics 1 What Is a Database? 2 Tables 2 Columns and Datatypes 3 Rows 4 Primary Keys 4 What Is SQL? 6 Try It Yourself 6 Summary 7 Chapter 2 Introducing MySQL 9 What Is MySQL? 9 Client/Server Software 9 MySQL Versions 10 MySQL Tools 11 mysql Command-Line Utility 11 MySQL Workbench 12 Other Tools 13 Summary 13 Chapter 3 Working with MySQL 15 Using the Command-Line Tool 15 Selecting a Database 16 Learning About Databases and Tables 17 Using MySQL Workbench 20 Getting Started 20 Using MySQL Workbench 21 Selecting a Database 22 Learning About Databases and Tables 22 Executing SQL Statements 23 Next Steps 23 Summary 24 Chapter 4 Retrieving Data 25 The SELECT Statement 25 Retrieving Individual Columns 25 Retrieving Multiple Columns 27 Retrieving All Columns 29 Retrieving Distinct Rows 29 Limiting Results 31 Using Fully Qualified Table Names 32 Using Comments 33 Summary 34 Challenges 34 Chapter 5 Sorting Retrieved Data 35 Sorting Data 35 Sorting by Multiple Columns 37 Sorting by Column Position 38 Specifying Sort Direction 39 Summary 41 Challenges 42 Chapter 6 Filtering Data 43 Using the WHERE Clause 43 WHERE Clause Operators 44 Checking Against a Single Value 45 Checking for Nonmatches 46 Checking for a Range of Values 47 Checking for No Value 48 Summary 49 Challenges 49 Chapter 7 Advanced Data Filtering 51 Combining WHERE Clauses 51 Using the AND Operator 51 Using the OR Operator 52 Understanding the Order of Evaluation 53 Using the IN Operator 54 Using the NOT Operator 56 Summary 58 Challenges 58 Chapter 8 Using Wildcard Filtering 59 Using the LIKE Operator 59 The Percent Sign (%) Wildcard 60 The Underscore (_) Wildcard 61 Tips for Using Wildcards 63 Summary 63 Challenges 63 Chapter 9 Searching Using Regular Expressions 65 Understanding Regular Expressions 65 Using MySQL Regular Expressions 66 Basic Character Matching 66 Performing OR Matches 68 Matching One of Several Characters 68 Matching Ranges 70 Matching Special Characters 70 Matching Character Classes 72 Matching Multiple Instances 72 Anchors 74 Summary 75 Challenges 76 Chapter 10 Creating Calculated Fields 77 Understanding Calculated Fields 77 Concatenating Fields 78 Using Aliases 80 Performing Mathematical Calculations 81 Summary 83 Challenges 83 Chapter 11 Using Data Manipulation Functions 85 Understanding Functions 85 Using Functions 86 Text Manipulation Functions 86 Date and Time Manipulation Functions 88 Numeric Manipulation Functions 91 Summary 92 Challenges 92 Chapter 12 Summarizing Data 93 Using Aggregate Functions 93 The Avg() Function 94 The Count() Function 95 The Max() Function 96 The Min() Function 97 The Sum() Function 98 Aggregates on Distinct Values 99 Combining Aggregate Functions 100 Summary 101 Challenges 101 Chapter 13 Grouping Data 103 Understanding Data Grouping 103 Creating Groups 104 Filtering Groups 105 Grouping and Sorting 107 Combining Grouping and Data Summarization 109 SELECT Clause Ordering 110 Summary 110 Challenges 110 Chapter 14 Working with Subqueries 113 Understanding Subqueries 113 Filtering by Subquery 113 Using Subqueries As Calculated Fields 117 Summary 119 Challenges 119 Chapter 15 Joining Tables 121 Understanding Joins 121 Understanding Relational Tables 121 Why Use Joins? 122 Creating a Join 123 The Importance of the WHERE Clause 124 Inner Joins 127 Joining Multiple Tables 128 Summary 130 Challenges 130 Chapter 16 Creating Advanced Joins 133 Using Table Aliases 133 Using Different Join Types 134 Self-Joins 134 Natural Joins 136 Outer Joins 137 Using Joins with Aggregate Functions 138 Using Joins and Join Conditions 139 Summary 140 Challenges 140 Chapter 17 Combining Queries 141 Understanding Combined Queries 141 Creating Combined Queries 141 Using UNION 141 UNION Rules 143 Including or Eliminating Duplicate Rows 144 Sorting Combined Query Results 145 Summary 146 Challenges 146 Chapter 18 Full-Text Searching 147 Understanding Full-Text Searching 147 Using Full-Text Searching 148 Performing Full-Text Searches 148 Using Query Expansion 151 Boolean Text Searches 153 Full-Text Searching Notes 156 Summary 157 Challenges 157 Chapter 19 Inserting Data 159 Understanding Data Insertion 159 Inserting Complete Rows 159 Inserting Multiple Rows 163 Inserting Retrieved Data 164 Summary 166 Challenges 166 Chapter 20 Updating and Deleting Data 167 Updating Data 167 Deleting Data 169 Guidelines for Updating and Deleting Data 170 Summary 171 Challenges 171 Chapter 21 Creating and Manipulating Tables 173 Creating Tables 173 Basic Table Creation 173 Working with NULL Values 175 Primary Keys Revisited 176 Using AUTO_INCREMENT 177 Specifying Default Values 178 Engine Types 179 Updating Tables 180 Deleting Tables 182 Renaming Tables 182 Summary 182 Challenges 182 Chapter 22 Using Views 183 Understanding Views 183 Why Use Views 184 View Rules and Restrictions 185 Using Views 185 Using Views to Simplify Complex Joins 185 Using Views to Reformat Retrieved Data 186 Using Views to Filter Unwanted Data 188 Using Views with Calculated Fields 188 Updating Views 189 Summary 190 Challenges 190 Chapter 23 Working with Stored Procedures 191 Understanding Stored Procedures 191 Why Use Stored Procedures 192 Using Stored Procedures 193 Executing Stored Procedures 193 Creating Stored Procedures 193 The DELIMITER Challenge 194 Dropping Stored Procedures 195 Working with Parameters 195 Building Intelligent Stored Procedures 199 Inspecting Stored Procedures 201 Summary 202 Challenges 202 Chapter 24 Using Cursors 203 Understanding Cursors 203 Working with Cursors 204 Creating Cursors 204 Opening and Closing Cursors 205 Using Cursor Data 206 Summary 210 Chapter 25 Using Triggers 211 Understanding Triggers 211 Creating Triggers 212 Dropping Triggers 213 Using Triggers 213 INSERT Triggers 213 DELETE Triggers 214 UPDATE Triggers 215 More on Triggers 216 Summary 216 Chapter 26 Managing Transaction Processing 217 Understanding Transaction Processing 217 Controlling Transactions 219 Using ROLLBACK 219 Using COMMIT 220 Using Savepoints 220 Changing the Default Commit Behavior 221 Summary 222 Chapter 27 Globalization and Localization 223 Understanding Character Sets and Collation Sequences 223 Working with Character Sets and Collation Sequences 224 Summary 226 Chapter 28 Managing Security 227 Understanding Access Control 227 Managing Users 228 Creating User Accounts 229 Deleting User Accounts 230 Setting Access Rights 230 Changing Passwords 233 Summary 234 Chapter 29 Database Maintenance 235 Backing Up Data 235 Performing Database Maintenance 235 Diagnosing Startup Problems 237 Reviewing Log Files 237 Summary 238 Chapter 30 Improving Performance 239 Improving Performance 239 Summary 240 Appendix A Getting Started with MySQL 241 What You Need 241 Obtaining the Software 242 Installing the Software 242 Preparing to Read This Book 242 Appendix B The Example Tables 243 Understanding the Example Tables 243 Table Descriptions 244 The vendors Table 244 The products Table 244 The customers Table 245 The orders Table 245 The orderitems Table 246 The productnotes Table 246 Creating the Sample Tables 247 Using Data Import 247 Using SQL Scripts 248 Appendix C MySQL Statement Syntax 249 ALTER TABLE 249 COMMIT 249 CREATE INDEX 250 CREATE PROCEDURE 250 CREATE TABLE 250 CREATE USER 250 CREATE VIEW 251 DELETE 251 DROP 251 INSERT 251 INSERT SELECT 251 ROLLBACK 252 SAVEPOINT 252 SELECT 252 START TRANSACTION 252 UPDATE 252 Appendix D MySQL Datatypes 253 String Datatypes 253 Numeric Datatypes 255 Date and Time Datatypes 256 Binary Datatypes 256 Appendix E MySQL Reserved Words 257 9780138223021 TOC 10/2/2023
£25.49
O'Reilly Media Reliable Machine Learning
Book SynopsisWhether you're part of a small startup or a planet-spanning megacorp, this practical book shows data scientists, SREs, and business owners how to run ML reliably, effectively, and accountably within your organization.
£47.99
Pearson Education Limited Cryptography and Network Security Principles and
Book SynopsisDr. William Stallings hasauthored 19 titles, and counting revised editions, over 40 books on computersecurity, computer networking, and computer architecture. His writings haveappeared in numerous publications, including the Proceedings of the IEEE,ACM Computing Reviews and Cryptologia. He has received 13 times theaward for the best Computer Science textbook of the year from the Text andAcademic Authors Association. In over 30 years in thefield, he has been a technical contributor, technical manager, and an executivewith several high-technology firms. He has designeTable of Contents Computer and Network Security Concepts Introduction to Number Theory Classical Encryption Techniques Block Ciphers and the Data Encryption Standard Finite Fields Advanced Encryption Standard Block Cipher Operation Random Bit Generation and Stream Ciphers Public-Key Cryptography and RSA Other Public-Key Cryptosystems Cryptographic Hash Functions Message Authentication Codes Digital Signatures Lightweight Cryptography and Post-Quantum Cryptography Key Management and Distribution User Authentication Protocols Transport-Level Security Wireless Network Security Electronic Mail Security IP Security Network Endpoint Security Cloud Security Internet of Things (IoT) Security Appendix A. Basic Concepts from Linear Algebra Appendix B. Measures of Security and Secrecy Appendix C. Data Encryption Standard (DES) Appendix D. Simplified AES Appendix E. Mathematical Basis of the Birthday Attack
£74.09
Pearson Education Limited Database Systems A Practical Approach to Design
Book SynopsisTeach database theory with the bestselling text on the subject Database Systems: A Practical Approach to Design, Implementation, and Management introduces the theory behind databases in a concise yet comprehensive manner. Designed for undergraduate courses, the text is accessible for non-technical readers. This title comes with a Companion Website.Table of ContentsPart 1 Background Chapter 1 Introduction to Databases Database Environment Database Architectures and the Web Part 2 The Relational Model and Languages Chapter 4 The Rational Model Chapter 5 Relational Algebra and Relational Calculus Chapter 6 SQL: Data Manipulation Chapter 7 SQL: Data Definition Chapter 8 Advanced SQL Chapter 9 Object-Relational DBMSs Part 3 Database Analysis and Design Chapter 10 Database System Development Lifecycle Chapter 11 Database Analysis and the DreamHome Case Study Chapter 12 Entity-Relationship Modeling Chapter 13 Enhanced Entity-Relationship Modeling Chapter 14 Normalization Chapter 15 Advanced Normalization Part 4 Methodology Chapter 16 Methodology – Conceptual Database Design Chapter 17 Methodology – Logical Database Design for the Relational Model Chapter 18 Methodology – Physical Database Design for Relational Databases Chapter 19 Methodology – Monitoring and Tuning the Operational System Part 5 Selected Database Issues Chapter 20 Security and Administration Chapter 21 Professional, Legal, and Ethical Issues in Data Management Chapter 22 Transaction Management Chapter 23 Query Processing Part 6 Distributed DBMSs and Replication Chapter 24 Distributed DBMSs – Concepts and Design Chapter 25 Distributed DBMSs – Advanced Concepts Chapter 26 Replication and Mobile Databases Part 7 Object DBMSs Chapter 27 Object-Oriented DBMSs – Concepts and Design Chapter 28 Object-Oriented DBMSs – Standard Systems Part 8 The Web and DBMSs Chapter 29 Web Technology and DBMSs Chapter 30 Semistructured Data and XML Part 9 Business Intelligence Chapter 31 Data Warehousing Concepts Chapter 32 Data Warehousing Design Chapter 33 OLAP Chapter 34 Data Mining Appendices References Further Reading Index
£63.25
John Wiley & Sons Inc Statistics for Big Data For Dummies
Book SynopsisDoes the subject of data analysis make you dizzy? This book features introduction to exploratory data analysis, the lowdown on collecting, cleaning, and organizing data, everything you need to know about interpreting data using common software and programming languages. It helps you to identify valid, useful, and understandable patterns in data.Table of ContentsIntroduction 1 Part I: Introducing Big Data Statistics 7 Chapter 1: What Is Big Data and What Do You Do With It? 9 Chapter 2: Characteristics of Big Data: The Three Vs 19 Chapter 3: Using Big Data: The Hot Applications 27 Chapter 4: Understanding Probabilities 41 Chapter 5: Basic Statistical Ideas 57 Part II: Preparing and Cleaning Data 81 Chapter 6: Dirty Work: Preparing Your Data for Analysis 83 Chapter 7: Figuring the Format: Important Computer File Formats 99 Chapter 8: Checking Assumptions: Testing for Normality 107 Chapter 9: Dealing with Missing or Incomplete Data 119 Chapter 10: Sending Out a Posse: Searching for Outliers 129 Part III: Exploratory Data Analysis (EDA) 141 Chapter 11: An Overview of Exploratory Data Analysis (EDA) 143 Chapter 12: A Plot to Get Graphical: Graphical Techniques 155 Chapter 13: You’re the Only Variable for Me: Univariate Statistical Techniques 173 Chapter 14: To All the Variables We’ve Encountered: Multivariate Statistical Techniques 191 Chapter 15: Regression Analysis 215 Chapter 16: When You’ve Got the Time: Time Series Analysis 243 Part IV: Big Data Applications 269 Chapter 17: Using Your Crystal Ball: Forecasting with Big Data 271 Chapter 18: Crunching Numbers: Performing Statistical Analysis on Your Computer 297 Chapter 19: Seeking Free Sources of Financial Data 319 Part V: The Part of Tens 331 Chapter 20: Ten (or So) Best Practices in Data Preparation 333 Chapter 21: Ten (or So) Questions Answered by Exploratory Data Analysis (EDA) 339 Index 349
£15.29
Manning Publications Phoenix in Action_p1
Book SynopsisDescription Phoenix is a modern web framework built for the Elixir programming language. Elegant, fault-tolerant, and performant, Phoenix is as easy to use as Rails and as rock-solid as Elixir’s Erlang-based foundation. Phoenix in Action builds on your existing web dev skills, teaching you the unique benefits of Phoenix along with just enough Elixir to get the job done. Phoenix in Action is an example-based tutorial that teaches you how to use the Phoenix framework to build production-quality web apps. Following a running example of an online auction site, you’ll design and build everything from the core components that drive the app to the real-time user interactions where Phoenix really shines. You’ll handle business logic, database interactions, and app designs that take advantage of functional programming as you discover a better way to develop web applications. Key features · Use channels for real-time communication · Learn database interactions with Ecto · Hands-on examples · Step-by-step instructions · Jargon-free Audience Written for web developers familiar with a framework like Rails or ASP.NET. No experience of Elixir or Phoenix required. About the technology Phoenix is a web framework for the Elixir language. Phoenix applications are blazingly fast, and as a developer you’ll appreciate the attention to detail in the framework design that makes you superproductive almost immediately. In particular, Phoenix channels provide an easy way to set up and manage real-time communication.
£37.99
Pearson Education (US) Information Privacy Engineering and Privacy by
Book SynopsisDr. William Stallings has made a unique contribution to understanding the broad sweep of technical developments in computer security, computer networking, and computer architecture. He has authored 18 textbooks and, counting revised editions, a total of 70 books on various aspects of these subjects. His writings have appeared in numerous ACM and IEEE publications, including the Proceedings of the IEEE and ACM Computing Reviews. He has 13 times received the award for the best computer science textbook of the year from the Text and Academic Authors Association. With more than 30 years in the field, he has been a technical contributor, a technical manager, and an executive with several high-technology firms. He has designed and implemented both TCP/IP-based and OSI-based protocol suites on a variety of computers and operating systems, ranging from microcomputers to mainframes. Currently he is an independent consultant whose clients have included computer and Table of Contents Part I: Planning for Privacy 1. Information Privacy Concepts 2. Security Governance and Management 3. Risk Assessment Part II: Privacy Threats 4. Information Storage and Processing 5. Information Collection and Dissemination 6. Intrusion and Interference Part III: Information Privacy Technology 7. Basic Privacy Controls 8. Privacy Enhancing Technology 9. Data Loss Prevention 10. Online Privacy 11. Detection of Conflicts In Security Policies 12. Privacy Evaluation Part IV: Information Privacy Regulations 13. GDPR 14. U.S. Privacy Laws and Regulations
£49.39
CRC Press GraphBased Social Media Analysis
Book SynopsisFocused on the mathematical foundations of social media analysis, Graph-Based Social Media Analysis provides a comprehensive introduction to the use of graph analysis in the study of social and digital media. It addresses an important scientific and technological challenge, namely the confluence of graph analysis and network theory with linear algebra, digital media, machine learning, big data analysis, and signal processing. Supplying an overview of graph-based social media analysis, the book provides readers with a clear understanding of social media structure. It uses graph theory, particularly the algebraic description and analysis of graphs, in social media studies.The book emphasizes the big data aspects of social and digital media. It presents various approaches to storing vast amounts of data online and retrieving that data in real-time. It demystifies complex social media phenomena, such as information diffusion, marketing and recommendationTable of ContentsGraphs in Social and Digital Media. Mathematical Preliminaries: Graphs and Matrices. Algebraic Graph Analysis. Web Search Based on Ranking. Label Propagation and Information Diffusion in Graphs. Graph-Based Pattern Classification and Dimensionality Reduction. Matrix and Tensor Factorization with Recommender System Applications. Multimedia Social Search Based on Hypergraph Learning. Graph Signal Processing in Social Media. Big Data Analytics for Social Networks. Semantic Model Adaptation for Evolving Big Social Data. Big Graph Storage, Processing and Visualization.
£42.74
Taylor & Francis Ltd Situating Data Science
Book SynopsisThe emerging field of Data Science has had a large impact on science and society. This book explores how one distinguishing feature of Data Science its focus on data collected from social and environmental contexts within which learners often find themselves deeply embedded suggests serious implications for learning and education.Drawing from theories of learning and identity development in the learning sciences, this volume investigates the impacts of these complex relationships on how learners think about, use, and share data, including their understandings of data in light of history, race, geography, and politics. More than just using real world examples' to motivate students to work with data, this book demonstrates how learners' relationships to data shape how they approach those data with agency, as part of their social and cultural lives. Together, the contributions offer a vision of how the learning sciences can contribute to a more expansive, socially awareTable of Contents1. Introduction: Situating Data Science—Exploring How Relationships to Data Shape Learning 2. At Home with Data: Family Engagements with Data Involved in Type 1 Diabetes Management 3. Examining Spontaneous Perspective Taking and Fluid Self-to-Data Relationships in Informal Open-Ended Data Exploration 4. Learning at the Intersection of Self and Society: The Family Geobiography as a Context for Data Science Education 5. Authoring Data Stories in a Media Makerspace: Adolescents Developing Critical Data Literacies 6. From Data Collectors to Data Producers: Shifting Students’ Relationship to Data, Lisa Hardy 7. Scripts and Counterscripts in Community-Based Data Science: Participatory Digital Mapping and the Pursuit of a Third Space 8. Learning to Reason with Data: How Did We Get Here and What Do We Know? 9. Educating Data Scientists and Data Literate Citizens for a New Generation of Data
£128.25
Taylor & Francis Ltd Computational Design
Book SynopsisNew computational design tools have evolved rapidly and been increasingly applied in the field of design in recent years, complimenting and even replacing the traditional design media and approaches. Design as both the process and product are changing due to the emergence and adoption of these new technologies. Understanding and assessing the impact of these new computational design environments on design and designers is important for advancing design in the contemporary context. Do these new computational environments support or hinder design creativity? How do those tools facilitate designers' thinking? Such knowledge is also important for the future development of design technologies. Research shows that design is never a mysterious non-understandable process, for example, one general view is that design process shares a common analysis-synthesis-evaluation model, during which designers interact between design problem and solution spaces. Understanding designers' thinking in difTable of ContentsIntroduction. Emergent technologies in computational design. Understanding design cognition in computational and generative design. Cognitive impacts and computational design environments. Conclusion.
£55.79
Cambridge University Press Deep Learning Recommender Systems
Book SynopsisRecommender systems are ubiquitous in modern life and are one of the main monetization channels for Internet technology giants. This book helps graduate students, researchers and practitioners to get to grips with this cutting-edge field and build the thorough understanding and practical skills needed to progress in the area. It not only introduces the applications of deep learning and generative AI for recommendation models, but also focuses on the industry architecture of the recommender systems. The authors include a detailed discussion of the implementation solutions used by companies such as YouTube, Alibaba, Airbnb and Netflix, as well as the related machine learning framework including model serving, model training, feature storage and data stream processing.
£47.49
