Databases / Data management Books

Book SynopsisGain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making. Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data proTable of ContentsPart I. Getting Started1. The Problem with Data Science2. Data StrategyPart II. Toward DataOps3. Lean Thinking4. Agile Collaboration5. Build Feedback and MeasurementPart III. Further Steps6. Building Trust7. DevOps for DataOps8. Organizing for DataOpsPart IV. The Self-Service Organization9. DataOps Technology10. The DataOps Factory

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Book Synopsis Deliver eye-catching and insightful business intelligence with Microsoft Power BI Desktop. This new edition has been updated to cover all the latest features of Microsoft''s continually evolving visualization product. New in this edition is help with storytelling-adapted to PCs, tablets, and smartphones-and the building of a data narrative. You will find coverage of templates and JSON style sheets, data model annotations, and the use of composite data sources. Also provided is an introduction to incorporating Python visuals and the much awaited Decomposition Tree visual. Pro Power BI Desktop shows you how to use source data to produce stunning dashboards and compelling reports that you mold into a data narrative to seize your audience''s attention. Slice and dice the data with remarkable ease and then add metrics and KPIs to project the insights that create your competitive advantage. Convert raw data into clear, accurTable of Contents1. Discovering and Loading Data with Power BI Desktop2. Discovering and Loading File-Based Data with Power BI Desktop3. Discovering and Loading File-Based Data with Power BI Desktop4. DirectQuery and Connect Live5. Loading Data from the Web and the Cloud6. Loading Data from Other Data Sources7. Structuring Imported Data8. Data Transformation and Cleansing9. Restructuring Data10. Complex Data Loads11. Organizing, Managing, and Parameterizing Queries12. The M Language13. Creating a Data Model14. Table Visuals15. Matrix and Card Visuals16. Charts in Power BI Desktop17. Formatting Charts in Power BI Desktop18. Other Types of Visuals19. Third-Party Visuals20. Maps in Power BI Desktop21. Filtering Data22. Using Slicers23. Enhancing Dashboards24. Advanced Dashboarding Techniques25. Appendix A: Sample Data

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Book SynopsisBuild a modern data warehouse on Microsoft's Azure Platform that is flexible, adaptable, and fastfast to snap together, reconfigure, and fast at delivering results to drive good decision making in your business. Gone are the days when data warehousing projects were lumbering dinosaur-style projects that took forever, drained budgets, and produced business intelligence (BI) just in time to tell you what to do 10 years ago. This book will show you how to assemble a data warehouse solution like a jigsaw puzzle by connecting specific Azure technologies that address your own needs and bring value to your business. You will see how to implement a range of architectural patterns using batches, events, and streams for both data lake technology and SQL databases. You will discover how to manage metadata and automation to accelerate the development of your warehouse while establishing resilience at every level. And you will know how to feed downstream analytic solutions such as Power BI and AzTable of Contents1. The Rise of the Modern Data Warehouse2. The SQL Engine3. The Integration Engine4. The Ingestion Architecture5. The Role of the Data Lake6. The Role of the Data Contract7. Logging, Auditing, and Resilience8. Using Scripting & Automation9. Beyond the Modern Data Warehouse

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Book SynopsisIntermediate-Advanced user levelTable of ContentsIntroductionPart I. Getting Started1. Create an Account2. Identity and Access ManagementPart II. Infrastructure and Operations3. Basic Networking4. Compute Instances5. Storage6. Oracle Autonomous Linux7. Autonomous Databases8. Load Balancers9. Notifications and MonitoringPart III. Applications10. SQL Developer Web11. Oracle Application Express12. Oracle REST Data Services13. Deploy Multitiered Web Applications14. Oracle Machine Learning NotebooksPart IV. Next Steps15. Infrastructure as Code16. Account Management

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Book SynopsisThere is no denying that the 21st century is data driven, with many digital industries relying on careful collection and analysis of mass volumes of information. A Chief Data Officer (CDO) at a company is the leader of this process, making the position an often daunting one. The Chief Data Officer Management Handbook is here to help. With this book, author Martin Treder advises CDOs on how to be better prepared for their swath of responsibilities, how to develop a more sustainable approach, and how to avoid the typical pitfalls. Based on positive and negative experiences shared by current CDOs, The Chief Data Officer Management Handbook guides you in designing the ideal structure of a data office, implementing it, and getting the right people on board. Important topics such as the data supply chain, data strategy, and data governance are thoughtfully covered by Treder. As a CDO it is important to use your position effectively with your entire team. The Chief Data Officer ManagementTable of Contents

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Book SynopsisPart 1: Basics.- Chapter 1: Data Science in Action.- Chapter 2: Getting Started.- Chapter 3: Data Visualization.- Part 2: More Programming.- Chapter 4: Statistical Analysis and Linear Models.- Chapter 5: Advanced Data Preprocessing.- Chapter 6: Preparing Data for Analysis.- Part 3: Advanced Topics.- Chapter 7: Regression.- Chapter 8: SAS Visual Statistics Viya.- Chapter 9: What Is Next?.- Appendix: Resources.- Table of Contents

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Book SynopsisBeginning-Intermediate user levelTable of Contents​Chapter 1: Introduction and The Need of Data LakeChapter Goal: The chapter introduces the readers to the concept & need of a data lake in this big data environment.The chapter also covers how to create a data lake & architecture patterns to be followed for data lake analytics. No of pages 15 Sub -Topics 1. Relational and non-relation data stores 2. Base for data: relational and non-relational databases 3. Warehouses of data: data warehouses 4. Markets for data: data marts 5. Introduction to data lake 6. Need to create a data lake Chapter 2: Data Just Got Bigger Chapter Goal: Today, enterprises have mix of relational and non-relational stores. However, when it comes to analyzing all this data – there must be a neutral platform which can understand these types of data. This introduces us to modern world concepts of distributed data storage & processing. It also talks about data sciences & machine learning concepts & how they are revolutionizing the data analysis world. No of pages : 20 Sub - Topics: 1. Massively parallel processing, distributed data and spark the Hadoop 2. Distributed systems vs massively parallel processing systems (MPP) 3. Respective use cases for distributed and MPP systems 4. Science for data 5. Learning of machines 6. Overview of data analytics and advanced data analytics Chapter 3: Emergence of Cloud Lakes Chapter Goal: The chapter enlighten the users with multiple cloud-based technologies available which are scalable, agile and performance in terms of computation, storage & analytics options. It goes into details about the suggested architecture on Microsoft Azure to solve Modern data warehouse, analytics use cases. No of pages: 20 Sub - Topics: 1. Data travels to Cloud with added benefits 2. Overview of phases of data analytics architecture 3. Available products under each phase on Microsoft Azure Chapter 4: Phases in Managing Data Analytics Pipeline Chapter Goal: This chapter covers in-depth context of this book. After we understand the background, this chapter will provide understanding of what are the phases of building entire data analytics pipeline. All the phases discussed in this book are critical to understand and any analytics solution will adhere to this common principle some way or the other. In each phase, there are different solutions to cater respective issues. It covers the data life cycle from upstream to downstream applications. No of pages: 20 Sub - Topics: 1. Real time and batch mode data processing 2. Phases in data Management · Ingest · Store · Analytics · Visualization 3. Cloud data lake architecture patterns Chapter 5: Data Ingestion in the Lake Chapter Goal: The chapter talks about the limitations about the traditional storage & how the big data technologies has emerged as the champion in solving the limitations & changing the concepts of Extract, Transform & Load (ETL) to Extract, Load & Transform(ELT). No of pages: 20 Sub - Topics: 1. Traditional limitations, can big data help? 2. ETL now becomes ELT 3. Tools in cloud for data ingestion · Azure Data Factory on Microsoft Azure · SQL server integration services on-premise 4. Overview of partner solutions for ETL/ELT – Informatica PowerEdge Chapter 6: Data Storage & Farming Chapter Goal: The chapter shares with readers that how once the data is available in storage layers, how it can be grown & real time data storage & analysis needs can be catered, it also talks about batch & real time data processing & storage. No of pages: 20 Sub - Topics: 1. Grow the data 2. Role of Azure data lake store, Blob, relational and non-relational stores 3. Architecting the Lambda & Kappa 4. Manage storage for real time and batch processing Chapter 7: Analyzing the Bigger Data in Real Time Chapter Goal: Analysis of data is crucial for enterprises to get the business insights from the historic, present & future data to make descriptive, streaming & predictive analytics. In this chapter, we will specifically talk about real time analytics. Components required to perform real time analytics and how to optimize the cost using Azure PaaS solutions. No of pages: 30 Sub - Topics: 1. Need of real time analytics 2. Approach to build data analytics on data lake for real time processing 3. Leverage event hubs/IOT hubs as a queuing solution on Azure 4. Why Edge computing and digital twins are gaining limelight 5. Choice between PaaS vs IaaS solution for streaming data processing 6. PaaS – stream analytics or spark streaming 7. Infuse R and Python on real-time data analytics pipelines 8. Use cases for real time analytics Chapter 8: Analyzing the Bigger Data in Batch Mode Chapter Goal: Analysis of data is crucial for enterprises to get the business insights from the historic, present & future data to make descriptive, streaming & predictive analytics. Analytics can help companies identify new business opportunities and revenue streams which results in an increase in profits, new customers, and improved customer service. No of pages: 30 Sub - Topics: 9. Role of big data and massively parallel processing systems 10. Approach to build data analytics on data lake for batch processing 11. Approach to build data analytics solution for real time analytics 12. When to leverage HDInsight and Spark clusters 13. Infuse R and Python in data analytics pipelines 14. How it's different from conventional data warehousing and massively parallel processing solutions 15. Use cases for batch mode processing Chapter 9: Visualization and Other Downstream Choices Chapter Goal: Visualization of data is crucial for reporting& also to perform exploratory data analytics. The chapter talks about the visual elements like charts, graphs, and maps, data visualization tools which provide an accessible way to see and understand trends, outliers, and patterns in data No of pages: 10 Sub - Topics: 1. Visualizations tools – Power BI 2. Downstream applications – LOB applications, notification applications 3. Choice of data stores for downstream applications – Cosmos DB, Azure SQL Database Chapter 10: Summary of Data Lake components in Azure Chapter Goal: The chapter takes a dig at multiple azure components which makes its easy to create an enterprise data lake in cloud & talks about in details the usage of each No of pages: 20 Sub - Topics: 1. Azure data factory 2. Azure data lake storage 3. Azure HDInsight 4. Azure databricks 5. Azure data warehouse 6. Azure PowerBI Chapter 11: Conclusion Chapter Goal: The concluding chapter summarizes the information shared around the data lake in the book No of pages: 5

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Book Synopsis Access much-needed information for building scalable, high-concurrency applications and deploying them against the Oracle Database. This new edition is updated to be current with Oracle Database 19. It includes a new chapter with troubleshooting recipes to help you quickly diagnose and resolve locking problems that are urgent and block production. Good transaction design is an important facet of highly-concurrent applications that are run by hundreds, even thousands, of users who are executing transactions at the same time. Transaction design, in turn, relies on a good understanding of how the database engine manages the locking of resources to prevent access conflicts and data loss that might otherwise result from concurrent access to data in the database. This book provides a solid and accurate explanation of how locking and concurrency are dealt with by Oracle Database. You will learn how the Oracle Database architecture accommodates user transactiTable of Contents1. Getting Started 2. Locking and Blocking 3. Locks, Latches, and Mutexes 4. Concurrency and Multiversioning 5. Transactions 6. Redo and Undo 7. Investigating Redo 8. Investigating Undo 9. Troubleshooting

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Book Synopsis1. The Fundamentals.- 2. Introduction to Requirements.- 3. The Language of Data Modeling.- 4. Conceptual and Logical Data Model Production.- 5. Normalization.- 6. Physical Model Case Study.- 7. Physical Model Implementation.- 8. Data Protection Patterns with Check Constraints and Triggers.- 9.  Patterns and Anti-Patterns.- 10. Database Security and Security Patterns.- 11. Data Structures, Indexes, and Their Applications.- 12. Matters of Concurrency.- 13. Coding Architecture.- 14. Appendix A: Scalar Datatype Reference.Table of Contents1. The Fundamentals2. Introduction to Requirements3. The Language of Data Modeling4. Conceptual and Logical Data Model Production5. Normalization6. Physical Model Case Study7. Physical Model Implementation 8. Data Protection Patterns with Check Constraints and Triggers9. Patterns and Anti-Patterns10. Database Security and Security Patterns11. Data Structures, Indexes, and Their Applications12. Matters of Concurrency13. Coding Architecture 14. Appendix A: Scalar Datatype Reference.

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Book SynopsisLearn all about blockchain and its applications in cryptocurrency, healthcare, Internet of Things, finance, decentralized organizations, and more. Featuring case studies and practical insights, this book covers a unique mix of topics and offers insight into how to overcome hurdles that arise as the market and consumers grow accustomed to blockchain-based organizations and services.The book is divided into three major sections. The first section provides a historical background to blockchain technology. You will start with a historical context to financial capital markets when Bitcoin was invented, followed by mining protocols, the need for consensus, hardware mining, etc. Next, a formal introduction to blockchain is provided covering transaction workflow, role of decentralized network, and payment verification. Then, we dive deep into a different implementation of a blockchain: Ethereum. The main technical features, such as Ethereum Virtual Machine, are presented aTable of ContentsChapter 1: Behold the DeamersChapter Goal: Provide a backdrop for introducing blockhain and the basics of a decentralized appSub -Topics:1. Financial crisis of 2008, the origins of bitcoin2. Basics of private-public keys3. What is a block, how is a block created4. What's a blockchain-enabled application? What is a decentralized application?Chapter 2: Gold Rush: Mining BitcoinChapter Goal: Provide a technical introduction to mining and the mathematical background to hashes, block headers, and consensusSub -Topics:1. Overview of mining, why is mining necessary for Bitcoin2. What is consensus3. Components of a block and a block header (mining components)4. What are hashes and how are they used in Bitcoin5. Hardware for mining (the gold rush part refers to the arms race that happened in hardware mining field).Chapter 3: Foundations of a BlockchainChapter Goal: Provide a technical introduction to transaction workflow, a blockchain network, simple payment verification, merkel roots, and block identifiersSub -Topics:1. What is a block header (block identifiers)2. How does the network participate3. A transaction workflow 4. Unspent transaction outputs, transaction propagation5. Simple payment verification6. Merkel roots, blockchain forksChapter 4: Unpacking EthereumChapter Goal: Provide a technical introduction to Ethereum, the differences between a Bitcoin blockchain and Ethereum blockchain, internal states, Ethereum Virtual Machine, and dAppsSub -Topics:1. Overview of Ethereum 2. Proof of stake3. Accounts and contract model in Ethereum4. Global state, gas, internal storage5. Ethereum Virtual Machine6. Solidity programming language + Smart Contracts7. World Computer Model and components8. Blockchain as a service9. Decentralized apps10. Geth, MistChapter 5: Decentralized Organizations (DAOs)Chapter Goal: Provide a technical introduction to DAOs and Aragon for setting up a DAO, including updates to new implementations of decentralized organizations in 2020. Sub -Topics:1. What is a DAO 2. What is a blockchain organizations/companies3. Aragon-core and Kernel4. How do you make DAOs and other blockchain organizations using Aragon?5. How do you operate DAOs?Chapter 6: The DAO HackedChapter Goal: Provide an overview to the vulnerabilities in the original DAO model, the conditions that led up to the hack, and the consequences to security hardening since. Sub -Topics:1. Concept of a DAO building on Vitalik’s concepts2. Slock.it and its involvement in making the DAO 3. The Smart Contract for DAO4. The code vulnerability responsible for the hack5. Consequences of the hack6. Ethereum splitting into ETCChapter 7: Ethereum Tokens: High Performance Computing (HPC)Chapter Goal: Provide an introduction to token in Ethereum by highlighting applications in HPC. Particularly, focus on Golem, SONM, and iEx.ec grid computing for off-chain computations and conflict resolution.Sub -Topics:1. Why tokens and what’s the value of using tokens2. Introduction to tokens, ERC 20 compatibility3. Token layer and an app layer4. Prototype for tokens and HPC – Ethereum Computation Markets5. Golem network, app registry, transaction framework, use-cases initially, and how the Smart Contract system ties them together6. SONM network and fog computing, use-cases, Smart Contract system, buyer-miner-hub interactions, purchasing computational power, Superglobal architecture, and OS7. iEx.ec, grid computing, sidechains, and how iEx.ec works Chapter 8: Blockchain in HealthcareChapter Goal: Provide an introduction to areas in healthcare where using a blockchain can provide benefit - Patient workflows, insurance claims processing, lightning network, verifiable data auditSub -Topics:1. Payer-provider-patient model, how claims work within this framework, and how will that change in the future2. Patient workflow based on permissions, blockchain-based workflow of a simple EHR, how permissions are passed as a patient moves from a general physician to a specialist 3. Show how permissions work in blockchain insurance claims processing4. Waste management in healthcare and claims processing5. Concept of hotswitching, mentioning lightning network6. How can blockchain be used to reduce economic waste 7. DeepMind’s Verifiable Data Audit as an alternative to blockchain8. Blockchain to streamline business processesChapter 9: Blockchain in ScienceChapter Goal: Provide an introduction to major topics in science where blockchain can be beneficial – Reputation markets, reproducibility crisis, drug tracking, digital clinical trialsSub -Topics:1. Reproducibility crisis in science2. Prediction markets in science – Augur and Gnosis3. Initiatives to fix reproducibility traditionally4. Clinical trials using the blockchain, colored coins to demonstrate workflow5. Reputation systems using Blockchain6. Pharma drug tracking using blockchainChapter 10: Building Healthcare Companies on BlockchainChapter Goal: Interview with John Bass on how to build a healthcare company on the blockchain and lessons learned along the way.Sub -Topics:1. The makings of Hashed Health2. Collaborative and consortium models3. Working groups for high-risk, high-reward technologies4. Governance models for Hashed Health consortia5. Member participationChapter 11: Rise of ConsortiumsChapter Goal: Provide an overview of consortium models that have become popular in the blockchain industry, the challenges consortiums hope to solve, and advantages to individual membersSub -Topics:1. Collaborative and consortium models2. Working groups for high-risk, high-reward technologies3. Governance models for Hashed Health consortiaChapter 12: The Hyperledger ProjectChapter Goal: Provide a broad overview of the Hyperledger Project and cover the rapid pace of developments since 2018 to the new products launched.Sub -Topics:1. Updates to all the components under Hyperledger umbrella including Fabric and Sawtooth2. New consensus algorithm (PBFT)3. Demo of Hyperledger Fabric Constructor4. Does your business need a blockchain (flowcharts)?5. Security in enterprise-grade Blockchains6. Smart Contracts in FabricChapter 13: Recent Advances in BlockchainChapter Goal: Provide a review of three major networks shaping the future of Blockchain – EOS.io with parallel processing virtual machine, chain-core with asset management, and Ivy Playground and Quorum with private–public transaction interfacesSub -Topics:1. EOS.io, how the tech works, the new advances such as parallel processing of smart contracts and instructions2. Chain Core, managing assets on blockchain, Ivy Playground as the new programming language to manage assets3. Quorum by JP Morgan, how the consensus works, how private transactions work, zero-knowledge proofs, Ethereum Enterprise AllianceChapter 14: Blockchain GamesChapter Goal: Provide a review of the educational games and APIs that have been released to teach the basic concepts of a blockchain networkSub -Topics:1. Components of a blockchain game2. Formal education and training in blockchain3. Formalization of blockchain study with journals and research articles4. Review of major blockchain games (3)Chapter 15: Cloud BlockchainsChapter Goal: Provide an overview of how to click and deploy a blockchain using cloud services and give a visual tutorial on how to set it upSub -Topics:1. Demo of Hyperledger Fabric Constructor on IBM Bluemix 2. Azure blockchain deployment3. Amazon ECS cloud blockchain deployment4. Setting up your own blockchain test-lab and budget itChapter 16: Technological Revolutions and Financial CapitalChapter Goal: Provide an overview of the financial markets and ICOs, focusing particularly on how to set up ICOs, how to manage them, advances to financial regulation technology based on blockchainSub -Topics:1. Dr. Hooper’s chapter focused on ICOs and financial capital markets2. Setting up ICOs, major pitfalls to avoid, and challenges to overcome during an ICO3. Major tech advances in financial markets using blockchain4. Reg TechAfterword – Call to Action and The Future of Blockchain

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Book SynopsisIntermediate-Advanced user levelTable of Contents1. Introduction to Deep Learning2. Build Your First Neural Network with Google Colab3. Working with TensorFlow Data4. Working with Other Data5. Classification6. Regression7. Convolutional Neural Networks8. Automated Text Generation9. Sentiment Analysis10. Time Series Forecasting with RNNs

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Book SynopsisImplement numerical algorithms in Java using NM Dev, an object-oriented and high-performance programming library for mathematics.You'll see how it can help you easily create a solution for your complex engineering problem by quickly putting together classes.Numerical Methods Using Java covers a wide range of topics, including chapters on linear algebra, root finding, curve fitting, differentiation and integration, solving differential equations, random numbers and simulation, a whole suite of unconstrained and constrained optimization algorithms, statistics, regression and time series analysis. The mathematical concepts behind the algorithms are clearly explained, with plenty of code examples and illustrations to help even beginners get started. What You Will Learn Program in Java using a high-performance numerical library Learn the mathematics for a wide range of numerical computing algorithms Trade Review“The book is primarily a user’s guide to the NM DEV commercial software library … .” (Anthony J. Duben, Computing Reviews, December 6, 2022)Table of ContentsTable of ContentsAbout the Authors...........................................................................................................iPreface............................................................................................................................ii1. Why Java?..............................................................................................................61.1. Java in 2020.....................................................................................................61.2. Java vs. C++....................................................................................................61.3. Java vs. Python................................................................................................61.4. Java in the future .............................................................................................62. Data Structures.......................................................................................................72.1. Function...........................................................................................................72.2. Polynomial ......................................................................................................73. Linear Algebra .......................................................................................................83.1. Vector and Matrix ...........................................................................................83.1.1. Vector Properties .....................................................................................83.1.2. Element-wise Operations.........................................................................83.1.3. Norm ........................................................................................................93.1.4. Inner product and angle ...........................................................................93.2. Matrix............................................................................................................103.3. Determinant, Transpose and Inverse.............................................................103.4. Diagonal Matrices and Diagonal of a Matrix................................................103.5. Eigenvalues and Eigenvectors.......................................................................103.5.1. Householder Tridiagonalization and QR Factorization Methods..........103.5.2. Transformation to Hessenberg Form (Nonsymmetric Matrices)...........104. Finding Roots of Single Variable Equations .......................................................114.1. Bracketing Methods ......................................................................................114.1.1. Bisection Method ...................................................................................114.2. Open Methods...............................................................................................114.2.1. Fixed-Point Method ...............................................................................114.2.2. Newton’s Method (Newton-Raphson Method) .....................................114.2.3. Secant Method .......................................................................................114.2.4. Brent’s Method ......................................................................................115. Finding Roots of Systems of Equations...............................................................125.1. Linear Systems of Equations.........................................................................125.2. Gauss Elimination Method............................................................................125.3. LU Factorization Methods ............................................................................125.3.1. Cholesky Factorization ..........................................................................125.4. Iterative Solution of Linear Systems.............................................................125.5. System of Nonlinear Equations.....................................................................126. Curve Fitting and Interpolation............................................................................146.1. Least-Squares Regression .............................................................................146.2. Linear Regression..........................................................................................146.3. Polynomial Regression..................................................................................146.4. Polynomial Interpolation...............................................................................146.5. Spline Interpolation .......................................................................................147. Numerical Differentiation and Integration...........................................................157.1. Numerical Differentiation .............................................................................157.2. Finite-Difference Formulas...........................................................................157.3. Newton-Cotes Formulas................................................................................157.3.1. Rectangular Rule....................................................................................157.3.2. Trapezoidal Rule....................................................................................157.3.3. Simpson’s Rules.....................................................................................157.3.4. Higher-Order Newton-Coles Formulas..................................................157.4. Romberg Integration .....................................................................................157.4.1. Gaussian Quadrature..............................................................................157.4.2. Improper Integrals..................................................................................158. Numerical Solution of Initial-Value Problems....................................................168.1. One-Step Methods.........................................................................................168.2. Euler’s Method..............................................................................................168.3. Runge-Kutta Methods...................................................................................168.4. Systems of Ordinary Differential Equations.................................................169. Numerical Solution of Partial Differential Equations..........................................179.1. Elliptic Partial Differential Equations...........................................................179.1.1. Dirichlet Problem...................................................................................179.2. Parabolic Partial Differential Equations........................................................179.2.1. Finite-Difference Method ......................................................................179.2.2. Crank-Nicolson Method.........................................................................179.3. Hyperbolic Partial Differential Equations.....................................................1710..................................................................................................................................1811..................................................................................................................................1912. Random Numbers and Simulation ....................................................................2012.1. Uniform Distribution .................................................................................2012.2. Normal Distribution...................................................................................2012.3. Exponential Distribution............................................................................2012.4. Poisson Distribution ..................................................................................2012.5. Beta Distribution........................................................................................2012.6. Gamma Distribution ..................................................................................2012.7. Multi-dimension Distribution ....................................................................2013. Unconstrainted Optimization ............................................................................2113.1. Single Variable Optimization ....................................................................2113.2. Multi Variable Optimization .....................................................................2114. Constrained Optimization .................................................................................2214.1. Linear Programming..................................................................................2214.2. Quadratic Programming ............................................................................2214.3. Second Order Conic Programming............................................................2214.4. Sequential Quadratic Programming...........................................................2214.5. Integer Programming.................................................................................2215. Heuristic Optimization......................................................................................2315.1. Genetic Algorithm .....................................................................................2315.2. Simulated Annealing .................................................................................2316. Basic Statistics..................................................................................................2416.1. Mean, Variance and Covariance................................................................2416.2. Moment......................................................................................................2416.3. Rank...........................................................................................................2417. Linear Regression .............................................................................................2517.1. Least-Squares Regression..........................................................................2517.2. General Linear Least Squares....................................................................2518. Time Series Analysis ........................................................................................2618.1. Univariate Time Series..............................................................................2618.2. Multivariate Time Series ...........................................................................2618.3. ARMA .......................................................................................................2618.4. GARCH .....................................................................................................2618.5. Cointegration .............................................................................................2619. Bibliography .....................................................................................................2720. Index .....................................................................................................

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Book SynopsisThis module is not sponsored by IBM and must be installed separately from the Db2 database.After reading Foundation Db2 and Python you'll be able to install Db2 on Windows or Linux, and perform backups and restore data.Table of ContentsChapter 1: Introduction Chapter Goal: An introduction to Db2 for Linux and Windows.No. of Pages: 10Sub-Topics:Glossary of termsIntroduction to the Db2Obtaining the install files for Db2What you need as far as your hardware and OS for your Db2 machineHow to organize your file system to support Db2 data basesChapter 2: Installing Db2Chapter Goal: Describes how to install Db2 on Linux and WindowsNo. of Pages: 50Sub-Topics:Preparing your server for Db2Installing Db2 on LinuxInstalling Db2 on WindowsInstalling the ibm_db moduleTest the connection between Python/ibm_db and Db2Chapter 3: Db2 ManagementChapter Goal: Create Python scripts to access Db2No. of Pages: 40Sub-Topics:Layout of the typical Python script to access Db2The ibm_db moduleThe order of processing in almost all Python scripts accessing Db2Using exceptions in your Python scriptsChapter 4: Installing the Db2 sample database and a custom databaseChapter Goal: Learn how to design and create your own databasesNo. of Pages: 35Sub-Topics:Install the Db2 sample databaseWrite some simple Python scripts to access the sample data baseDesign and install the Orbital Launch data baseWrite some simple Python scripts to assess the Orbital Launch data baseChapter 5: Creating Utility Modules for Accessing Db2Chapter Goal: Creating modules that access ibm_dbNo. of Pages: 40Sub-Topics:Create you first utility moduleUse functions or classes in your module, or both?Organizing your module(s)Accessing your moduleChapter 6: Documenting the ibm_db ModuleChapter Goal: Documenting the ibm_db Module APINo. of Pages: 40Sub-Topics:Document each API in the ibm_db moduleDocument all input and outputs to each APIProvide multiple examples for each APIChapter 7: Writing Good SQL for Db2 (this might cover multiple chapters)Chapter Goal: Creating optimized SQL for Db2No. of Pages: 60Sub-Topics:Minimize the passes through the dataCode for concurrencyLocking and isolation levelsHow to avoid writing codeThe importance of indexesOptimizationSequential vs. random data accessTypes of joinsChapter 8: Where is the ibm_db Module GoingChapter Goal: Explain why IBM does not directly support the ibm_db module No. of Pages: 25Sub-Topics:Why ibm_db is open source?How does ibm_db use underlying systems?Why is this module not included with Db2?Some future items to be added to ibm_db moduleChapter 9: Db2 provided utilitiesChapter Goal: Describe some of the utilities that come with Db2 and possibly some extra cost utilitiesNo. of Pages: 50Subtopics:What utilities are covered has yet to be identifiedChapter 10: BLOB data, what is it and how do you use itChapter Goals: Describe what a blob is and how to use oneNo. of Pages: 50Subtopics:Describe the different kinds of BLOBsAccessing BLOB data in PythonWhat kinds of data can be stored in a BLOBUtilizing BLOBs to store Python data, how to keep data and metadata together in Db2

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Book SynopsisIntermediate user levelTable of ContentsIntroductionPart I. The Reactive Movement and R2DBC1. The Case for Reactive Programming2. Introduction to R2DBCPart II. The R2DBC Service-Provider Interface3. The Path to Implementation4. Connections5. Transactions6. Statements7. Handling Results8. Result Metadata9. Mapping Data Types10. Handling ExceptionsPart III. Getting Started with R2DBC and MariaDB11. Getting Stated with R2DBC12. Managing Connections13. Managing Data14. Managing Transactions15. Connection Pooling16. Practical Applications with Spring Data and R2DBC

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Book SynopsisUnderstand the essentials of the Snowflake Database and the overall Snowflake Data Cloud. This book covers how Snowflake's architecture is different from prior on-premises and cloud databases. The authors also discuss, from an insider perspective, how Snowflake grew so fast to become the largest software IPO of all time. Snowflake was the first database made specifically to be optimized with a cloud architecture. This book helps you get started using Snowflake by first understanding its architecture and what separates it from other database platforms you may have used. You will learn about setting up users and accounts, and then creating database objects. You will know how to load data into Snowflake and query and analyze that data, including unstructured data such as data in XML and JSON formats. You will also learn about Snowflake's compute platform and the different data sharing options that are available.What YouWill LearnRun analytics in the Snowflake Data CloudCreate users and Table of Contents1. The Snowflake Data Cloud2. Snowflake Quick Start3. Snowflake Data Cloud Architecture4. Snowflake Web Interface -- Classic Console5. Snowflake Web Interface -- Preview App (Snowsight)6. Account Management7. Security8. Database Objects9. Querying and Cloning Data in Snowflake10. How Snowflake Compute Works11. Semi-Structured Data in Snowflake12. Loading Data13. Unloading Data14. Data Sharing, Data Exchanges, and the Snowflake Data Marketplace

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Book SynopsisUse this one-stop resource for both basic and advanced administration of Exchange Server 2019 and 2016. It will help you in running an Exchange environment, whether it be 100% on-premises or a hybrid configuration with Exchange Online (as part of Office 365). This revised version is divided into four parts, describing Exchange infrastructure, upgrading Exchange server, integration with Office 365, and security and compliance. In the first part, you will go through a short introduction of Exchange server followed by its installation and configuration. You will learn client access along with Exchange mailbox and managing Exchange recipients. In the second part, you will learn how to upgrade from Exchange 2010 to 2016 and from 2013 to Exchange 2019. The third part is dedicated to the Exchange integration with Office 365, followed by the last part that teaches you how to secure your Exchange environment and its compliance. After reading this book, you will understand best practices, doTable of ContentsSection 1 - Exchange Infrastructure 1. Introduction 1.1. History of Exchange server 1.2. Exchange 2016 or Exchange 2019? 1.3. Exchange Admin Center 1.4. PowerShell 1.5. Exchange and Active Directory 2. Installing and configuring Exchange 2.1. Designing the Exchange environment 2.2. Testing the Exchange environment 2.3. Building the Exchange environment 2.4. Exchange Edge Transport Server 2.5. Cumulative Updates 3. Exchange and Client Access 3.1. Clients 3.2. Outlook on the Web 3.3. Outlook 3.4. Autodiscover 3.5. Exchange Web Services 3.6. ActiveSync 3.7. Namespaces 3.8. Virtual Directories 3.9. Certificates 3.10. Client Access High Availability 4. Exchange Mailbox and Mailbox server 4.1. Mailbox databases 4.2. Backup and Restore 4.3. High Availability 4.4. Mailbox database High Availability 5. Managing Exchange Recipients 5.1. Managing Recipients 5.2. Mailboxes 5.3. Distribution Groups 5.4. Contacts 5.5. Public Folders 5.6. Address Lists 5.7. Global Address List 5.8. Custom Address List 5.9. Offline Address Books 5.10. Address Book Policies 6. Exchange Transport 6.1. Transport pipeline 6.2. Send and Receive Connectors 6.3. SMTP Relay 6.4. Edge Transport server 6.5. Load balancing transport Section 2 - Upgrading Exchange server 7. Upgrading from Exchange 2010 to Exchange 2016 7.1. Preparing Active Directory 7.2. Installing the Exchange servers 7.3. Change client access 7.4. Move Mailboxes 7.5. Move Public Folders 7.6. Decommission Exchange 2010 8. Upgrading from Exchange 2013 to Exchange 2019 8.1. Preparing AD en Installing Exchange is identical to 7.1 and 7.2 8.2. Client access in 2013/2019 coexistence 8.3. Move Mailboxes and Public Folders 8.4. Decommission Exchange 2013 Section 3 - integration with Office 365 9. Exchange Hybrid 9.1. Identities and source of authority 9.2. Managing Hybrid Recipients 9.3. Federation 9.4. Federation with other organizations 9.5. Organizational relationships 9.6. Sharing policies 9.7. Directory Synchronization 9.8. Exchange Hybrid Configuration Wizard 9.9. Autodiscover continued 9.10. Mailflow in coexistence 9.11. Exchange Online Protection 9.12. Modern Authentication 9.13. Bulk Email Section 4 - Security and Compliance 10. Securing your Exchange environment 10.1. Hybrid Modern Authentication 10.2. Message hygiene 10.3. Multi Factor Authentication 10.4. Role Based Access Control 11. Compliance 11.1. Archiving 11.2. Journaling 11.3. In-place hold 11.4. In-place eDiscovery 11.5. Messaging Records management 11.6. Data Loss Prevention 11.7. Auditing 11.8. Reporting

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Book SynopsisLearn the fundamentals of SAP Enterprise Project and Portfolio management Project Systems (PS), Portfolio and Project Management (PPM) and Commercial Project Management (CPM) and their integration with other SAP modules. This book covers various business scenarios from different industries including the public sector, engineering and construction, professional services, telecom, mining, chemical, and pharmaceutical.Author Joseph Alexander Soosaimuthu will help you understand common business challenges and pain areas faced in portfolio, program and project management, and will provide suitable recommendations to overcome these challenges. This book not only suggests solutions within SAP, but also provides workarounds or integrations with third-party tools based on various Industry-specific business requirements.SAP Portfolio and Project Management addresses commonly asked questions regarding SAP EPPM implementation and deployment, and conveys a framework to facilTable of ContentsChapter 1: Project, Program and Portfolio Management - FundamentalsChapter Goal: To familiarise project, program and portfolio management structures, which subsequent chapters are based on. This chapter will act as building block for further concepts discussed in this book. Sub -Topics 1. Enterprise and Organisation Structure 2. Project Work Breakdown Structure 3. Portfolio and Program Structure 4. Synchronisation of Project, Program and Portfolio Structures 5. Prioritisation Framework Chapter 2: Project Life Cycle – Concept to Closure Chapter Goal: This chapter discusses in detail the various functionalities that will be used during the lifecycle of the project. Sub - Topics 1. Project Planning, Forecasting and Budgeting 2. Project Variation Management 3. Project Commentary 4. Project Issue, Risk and Action item Registers 5. Project Procurement 6. Project Resourcing 7. Project Billing 8. Project Capitalisation 9. Project Closure Chapter 3: Integration Chapter Goal: This chapter will cover critical integration touch points with 3rd party application and also other modules within SAP. Sub - Topics: 1. Detailed level planning of dates and schedules planning with integration to procurement and resourcing. 2. Integration with Schedule Management Applications such as MS project and Oracle Primavera 3. Integration with estimation and costing applications. 4. Integration with Forecasting Application or Excel Integration.Chapter 4: Industry Best Practise and RecommendationChapter Goal: The goal of this chapter is to provide the target audience with insight on business challenges faced during the implementation of Industry best practise and to discuss various solution options with recommendations. Sub - Topics: 1. Industry Best Practise 2. Business Challenges 3. Solution Options 4. Recommendation 5. Commonly asked questions 6. Standard RICEFW List by Industry 7. Standard Functionality List by Industry. Chapter 5: ReportingChapter Goal: This chapter covers reporting related to project, program and portfolio management. It also covers usage of standard ECC and BW Reports/Contents. Sub - Topics: 1. Operational Reporting 2. Month End Reporting 3. Strategic Reporting 4. Long Term Trend Analysis

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Book SynopsisUnderstand the different access control paradigms available in the Snowflake Data Cloud and learn how to implement access control in support of data privacy and compliance with regulations such as GDPR, APPI, CCPA, and SOX. The information in this book will help you and your organization adhere to privacy requirements that are important to consumers and becoming codified in the law. You will learn to protect your valuable data from those who should not see it while making it accessible to the analysts whom you trust to mine the data and create business value for your organization. Snowflake is increasingly the choice for companies looking to move to a data warehousing solution, and security is an increasing concern due to recent high-profile attacks. This book shows how to use Snowflake's wide range of features that support access control, making it easier to protect data access from the data origination point all the way to the presentation and visualization layer.Reading this book Table of Contents​Part I. Background1. What is Access Control?2. Data Types Requiring Access Control3. Data Privacy Laws and Regulatory Drivers4. Permission typesPart II. Creating Roles5. Functional Roles - What A Person Does6. Team Roles - Who A Person Is7. Assuming A Primary Role8. Secondary RolesPart III. Granting Permissions to Roles9. Role Inheritance10. Account and Database Level Privileges 11. Schema-Level Privileges12. Table and View Level Privileges13. Row-Level Permissioning and Fine-Grained Access Control14. Column-Level Permissioning and Data MaskingPart IV. Operationally Managing Access Control15. Secure Data Sharing16. Separating Production from Development17. Upstream & Downstream Services18. Managing Access Requests

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Book SynopsisGet started with blockchain development with this step-by-step guide. This book takes you all the way from installing requisite software through writing, testing, and deploying smart contracts. Getting Started with Ethereum delves into technologies most closely associated with Ethereum, such as IPFS, Filecoin, ENS, Chainlink, Truffle, Ganache, OpenZeppelin, Pinata, Fleek, Infura, Metamask, and Opensea. Author Davi Bauer walks you through project creation, how to compile projects and contracts, configure networks, and deploy smart contracts on blockchains. He then covers smart contracts, including deploying and verifying them. This book approaches blockchain in a way that allows you to focus on the topic that most interests you, covering Ethereum-related technologies broadly and not just focusing on Solidity.This hands-on guide offers a practical rather than conceptual approach get you coding. Upon completing this book, you Table of Contents● Pre requirements ○ Install Blockchain Dev Kit Extension on VS Code ■ Installing the extension ○ Install Truffle ■ Installing Truffle ■ Checking Truffle installation ○ Install Ganache CLI ■ Installing Ganache ■ Starting Ganache locally ○ Install Docker Chapter 1: MetaMask ○ Install and Setup MetaMask Wallet ■ Installing the wallet ■ Configuring the wallet ■ Accessing your wallet ■ Discovering your wallet address Chapter 2: Infura ○ Create an account on Infura ■ Creating a new account ■ Setting up your Infura project Chapter 3: Solidity ○ Get started with Solidity project on VS Code ■ Creating a new project ■ Compiling the project ■ Deploying to development Blockchain Chapter 4: ERC20 Tokens ○ Write a simple ERC20 token using OpenZeppelin ■ Preparing the environment ■ Writing the contract ■ Setting the Solidity compiler version ■ Compiling the contract ■ Verifying the result ○ Deploy ERC20 token to ganache development Blockchain ■ Preparing the migration ■ Writing the contract ■ Starting the Blockchain ■ Configuring the Blockchain network ■ Deploying the contract ■ Adding the token to a wallet ○ Create an ERC20 token with fixed supply ■ Creating the project ■ Writing the contract ■ Starting Ganache development Blockchain ■ Migrating the contract ■ Configuring MetaMask ■ Adding the token ■ Transferring tokens between accounts ○ Deploy ERC20 token to Testnet using Infura ■ Installing the pre-requirements ■ Setting up your Infura project ■ Setting up your Smart Contract ■ Configuring the private key ■ Deploying the Smart Contract ■ Checking your wallet balance ■ Verifying the Smart Contract on Etherscan ○ Deploy ERC20 token to Polygon Testnet (Layer 2) ■ Installing the pre-requirements ■ Adding Polygon Mumbai to MetaMask networks ■ Activating the Polygon add-on on Infura ■ Setting up your Infura project ■ Setting up your Smart Contract ■ Configuring the network (using Matic endpoint) ■ Configuring the network (using Infura endpoint) ■ Configuring the private key ■ Deploying the Smart Contract ■ Checking your wallet balance ■ Verifying the Smart Contract on Polygan Scan ○ Deploy ERC20 Token to Polygon Mainnet (Layer 2) ■ Adding Polygon Mainnet to MetaMask networks ■ Configuring the network (using Infura endpoint) ■ Deploying the Smart Contract ■ Checking your wallet balance ■ Verifying the Smart Contract on polyganscan Chapter 5: Unit Tests for Smart Contracts ○ Write Unit Tests for ERC20 Smart Contracts ■ Creating a new unit test file ■ Writing test for the contract total supply ■ Writing test asserting for the contract balance Chapter 6: ERC721- Non-Fungible Tokens ○ Create your art NFT using Ganache and OpenZeppelin ■ Creating the project ■ Configuring the wallet ■ Configuring the network ■ Configuring the solidity compiler ■ Configuring the private key ■ Creating the badge image ■ Adding the badge to your local IPFS ■ Pinning the badge to a remote IPFS node ■ Creating the badge metadata ■ Compiling the Smart Contract ■ Migrating the Smart Contract ■ Instantiate the Smart Contract ■ Awarding badge to a wallet ■ Checking badge on Etherscan ■ Adding the NFT token to your wallet ○ Sell your art NFT on Opensea ■ Connecting to OpenSea ■ Viewing your badge ■ Listing your badge for sale ■ Exploring listing details Chapter 7: Faucets ○ Get Test Ether From Faucet on Ropsten Network ■ Accessing the faucet ■ Waiting for the transaction ○ Get Test Ether From Faucet on Rinkeby Testnet ■ Preparing for funding ■ Funding your wallet ■ Checking your wallet ○ Get Test MATIC From Faucet on Mumbai Testnet ■ Preparing for funding ■ Funding your wallet ■ Checking your wallet ○ Get Test MATIC From Faucet on Mainnet ■ Preparing for funding ■ Funding your wallet ■ Checking your wallet Chapter 8: IPFS - InterPlanetary File System ○ Create Your IPFS Node ■ Installing the node ■ Configuring the node ■ Testing the node ■ Exploring your IPFS node ○ Add Files to IPFS ■ Adding the file ■ Viewing the file content on the console ■ Checking the file in the web ui ■ Viewing the file content in the browser ○ Setup IPFS Browser Extension ■ Installing the browser extension ■ Configuring the node type ■ Starting an external node ■ Importing a file ○ Pin and Unpin IPFS Files on Local Node ■ Starting your local node ■ Adding file to your node ■ Checking the file was added ■ Verifying your file was pinned ■ Unpinning your file ■ Pinning your file manually ○ Pin and Unpin Files on Remote Node using Pinata ■ Setting up API Keys on Pinata ■ Setting up Pinata as a remove service on your terminal ■ Adding a new file to your local IPFS node ■ Pinning your file to the remote IPFS node ■ Unpinning your file from the remote IPFS node ○ Host Your Site on IPFS Using Fleek ■ Login on Fleek ■ Cloning your existing repository ■ Installing Fleek ■ Initializing Fleek ■ Deploying your site Chapter 9: Filecoin ○ How to preserve files on Filecoin local node ■ Creating the project ■ Configuring truffle ■ Adding an image to be preserved ■ Installing dependencies ■ Starting local endpoints ■ Preserving files to Filecoin Chapter 10: ENS - Ethereum Name Service ○ Register your ENS to Receive any Crypto, Token or NFT on Your Wallet ■ Searching your domain name ■ Request to register ■ Managing your registration name ■ Checking the name resolution Chapter 11: Chainlink ○ Get Crypto Prices Inside Smart Contracts using Chainlink Oracles ■ Creating the project ■ Creating the Smart Contract ■ Creating the migration ■ Setting up your Infura project ■ Configuring the wallet ■ Configuring the network ■ Configuring the solidity compiler ■ Configuring the private key ■ Compiling the Smart Contract ■ Deploying the Smart Contract ■ Getting the price information from the Smart Contract Chapter 12: Nethereum ○ Get Ether Balance using Nethereum ■ Creating the project ■ Installing web3 ■ Creating the method ■ Getting the balance

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Book SynopsisGet introduced to Azure Arc-enabled Data Services and the powerful capabilities to deploy and manage local, on-premises, and hybrid cloud data resources using the same centralized management and tooling you get from the Azure cloud. This book shows how you can deploy and manage databases running on SQL Server and Postgres in your corporate data center or any cloud as if they were part of the Azure platform. This second edition has been updated to the latest codebase, allowing you to use this book as your handbook to get started with Azure Arc-enabled Data Services today. Learn how to benefit from Azure's centralized management, the automated rollout of patches and updates, managed backups, and more.This book is the perfect choice for anyone looking for a hybrid or multi-vendor cloud strategy for their data estate. The authors walk you through the possibilities and requirements to get Azure SQL Managed Instance and PostgresSQL Hyperscale deployed outside of Azure, so the services are accessible to companies that cannot move to the cloud or do not want to use the Microsoft cloud exclusively. The technology described in this book will benefit those required to keep sensitive services, such as medical databases, away from the public cloud equally as those who can't move to a public cloud for other reasons such as infrastructure constraints but still want to benefit from the Azure cloud and the centralized management and tooling that it supports. What You Will LearnUnderstand the fundamentals and architecture of Azure Arc-enabled data servicesBuild a multi-cloud strategy based on Azure Data ServicesDeploy Azure Arc-enabled data services on premises or in any cloudDeploy Azure Arc-enabled SQL Managed Instance on premises or in any cloudDeploy Azure Arc-enabled PostgreSQL Hyperscale on premises or in any cloudBackupand Restore your data that is managed by Azure Arc-enabled data servicesManage Azure-enabled data services running outside of AzureMonitor Azure-enabled data services through Grafana and KibanaMonitor Azure-enabled data services running outside of Azure through Azure MonitorWho This Book Is ForDatabase administrators and architects who want to manage on-premises or hybrid cloud data resources from the Microsoft Azure cloud. Especially for those wishing to take advantage of cloud technologies while keeping sensitive data on premises and under physical control.Table of Contents1. A Kubernetes Primer2. Azure Arc-Enabled Data Services3. Getting Ready for Deployment4. Installing Kubernetes5. Deploying a Data Controller in Indirect Mode 6. Deploying a Data Controller in Direct Mode 7. Deploying an Azure Arc-Enabled SQL Managed Instance8. Deploying Azure Arc-Enabled PostgreSQL Hyperscale 9. Monitoring and Management

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Book SynopsisDiscover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. Updated for the R 4.0 release, this book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R.Beginning Data Science in R 4, Second Editiondetails how data science is a combination of statistics, computational science, and machine learning. You'll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this.Modern data analysis requires computational skills and usually a minimum of programming. After reading and using this book, you'll have what you need to get started with R programming with data science applications. Source code will be available to support your next projects as well. Source code is available at github.cTable of Contents1. Introduction to R programming. 2. Reproducible analysis. 3. Data manipulation. 4. Visualizing and exploring data. 5. Working with large data sets.6. Supervised learning. 7. Unsupervised learning. 8. More R programming.9. Advanced R programming.10. Object oriented programming.11. Building an R package.12. Testing and checking. 13. Version control. 14. Profiling and optimizing.

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Book SynopsisThis book helps SAP architects and SAP Basis administrators deploy and operate SAP S/4HANA systems on the most common public cloud platforms. Market-leading cloud offerings are covered, including Amazon Web Services, Microsoft Azure, and Google Cloud. You will gain an end-to-end understanding of the initial implementation of SAP S/4HANA systems on those platforms. You will learn how to move away from the big monolithic SAP ERP systems and arrive at an environment with a central SAP S/4HANA system as the digital core surrounded by cloud-native services. The book begins by introducing the core concepts of Hyperscaler cloud platforms that are relevant to SAP. You will learn about the architecture of SAP S/4HANA systems on public cloud platforms, with specific content provided for each of the major platforms. The book simplifies the deployment of SAP S/4HANA systems in public clouds by providing step-by-step instructions and helping you deal with thecomplexity of such a deployment. ConteTable of Contents1. Introduction to Public Cloud and Hyperscalers2. SAP S/4HANA systems on Public Cloud3. SAP S/4HANA Deployment and Migration4. SAP S/4HANA on AWS Elastic Compute Cloud – Concepts and Architecture5. SAP S/4HANA on AWS Elastic Compute Cloud – Deployment 6. SAP S/4HANA on Microsoft Azure – Concepts and Architecture7. SAP S/4HANA on Microsoft Azure – Deployment 8. SAP S/4HANA on Google Cloud – Concepts and Architecture9. SAP S/4HANA on Google Cloud – Deployment and Setup10. Summary and Outlook

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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

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Book SynopsisTake a concise approach to learning how DAX, the function language of Power BI and PowerPivot, works. This book focuses on explaining the core concepts of DAX so that ordinary folks can gain the skills required to tackle complex data analysis problems. But make no mistake, this is in no way an introductory book on DAX. A number of the topics you will learn, such as the concepts of context transition and table expansion, are considered advanced and challenging areas of DAX.While there are numerous resources on DAX, most are written with developers in mind, making learning DAX appear an overwhelming challenge, especially for those who are coming from an Excel background or with limited coding experience. The reality is, to hit the ground running with DAX, it''s not necessary to wade through copious pages on rarified DAX functions and the technical aspects of the language. There are just a few mandatory concepts that must be fully understood before DAX can be mastered. Table of ContentsChapter 1: Show Me the Data Chapter 2: DAX Objects, Syntax & Formatting Chapter 3: Calculated Columns & Measures Chapter 4: Evaluation Context Chapter 5: Iterators Chapter 6: The CALCULATE Function Chapter 7: DAX Table Functions Chapter 8: The ALL Function and All its Variations Chapter 9: Calculations on Dates: Using DAX Time Intelligence Chapter 10: Empty Values Versus Zero Chapter 11: Using Variables: Making Our Code More Readable Chapter 12: Returning Values in the Current Filter Chapter 13: Controlling the Direction of Filter Propagation Chapter 14: Working with Multiple Relationships Between Tables Chapter 15: Understanding Context Transition Chapter 16: Leveraging Context Transition Chapter 17: Virtual Relationships: the LOOKUPVALUE and TREATAS Functions Chapter 18: Table Expansion Chapter 19: The CALCULATETABLE Function

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Book SynopsisBegin working with the Grafana data visualization platform. This book is a how-to manual for deploying and administering Grafana, creating real-time dashboards and alerts, exploring the data you have, and even synthesizing new data by combining and manipulating data from multiple different sources. You'll be able to see and manage data on any scale, from your laptop or a Raspberry Pi to a production datacenter or even a multi-region cloud environment!Getting Started with Grafana takes a hands-on approach. You'll learn by doing with easy-to-follow examples along with pointers to more resources to help you go deeper. The skills you'll learn will help you provide business value by monitoring your operations in real time and reacting to changing circumstances as they occur. You'll be able to derive new insights from your existing data through Grafana's powerful and beautiful graphing capabilities, and you'll be able to share your dashboards with colleagues soeveryone in your organization cTable of ContentsIntroductionPart I. Getting Started1. Grafana Cloud2. Working with PanelsPart II. Deploying and Managing Grafana3. Deploying Grafana Locally4. Connecting to Data Sources5. User AdministrationPart III. Making Things Useful6. Dashboard Design7. Workflow8. Working with Multiple Data Sources9. Advanced Panels10. Dashboard Variables11. AlertingPart IV. Advanced Grafana12. Advanced Deployment and Management13. Programmatic Grafana14. Grafana Enterprise

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Book SynopsisThis book provides all you need to find data from external sources and load and transform that data into Power BI where you can mine it for business insights and a competitive edge. This ranges from connecting to corporate databases such as Azure SQL and SQL Server to file-based data sources, and cloud- and web-based data sources. The book also explains the use of Direct Query and Live Connect to establish instant connections to databases and data warehouses and avoid loading data.The book provides detailed guidance on techniques for transforming inbound data into normalized data sets that are easy to query and analyze. This covers data cleansing, data modification, and standardization as well as merging source data into robust data structures that can feed into your data model. You will learn how to pivot and transpose data and extrapolate missing values as well as harness external programs such as R and Python into a Power Query data flow. You also will see how to handle errors in soTable of Contents1. Discovering and Loading Data with Power BI Desktop2. Discovering and Loading File-Based Data with Power BI Desktop3. Loading Data From Databases and Data Warehouses4. DirectQuery and Live Connect5. Loading Data from the Web and Cloud6. Loading Data from Other Data Sources7. Power Query8. Structuring Data9. Shaping Data10. Data Cleansing11. Data Transformation12. Complex Data Structures13. Organizing, Managing, and Parameterizing Queries14. The M LanguageAppendix A: Sample Data

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Book SynopsisDeep learning is one of the most powerful tools in the modern artificial intelligence landscape. While having been predominantly applied to highly specialized image, text, and signal datasets, this book synthesizes and presents novel deep learning approaches to a seemingly unlikely domain - tabular data. Whether for finance, business, security, medicine, or countless other domain, deep learning can help mine and model complex patterns in tabular data - an incredibly ubiquitous form of structured data.Part I of the book offers a rigorous overview of machine learning principles, algorithms, and implementation skills relevant to holistically modeling and manipulating tabular data. Part II studies five dominant deep learning model designs - Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Attention and Transformers, and Tree-Rooted Networks - through both their ''default'' usage and their application to tabular data. Part III compounds the powTable of Contents○ Section 1: Machine Learning and Tabular Data ■ Chapter 1 – Introduction to Machine Learning ■ Chapter 2 – Data Tools ○ Section 2: Applied Deep Learning Architectures ■ Chapter 3 – Artificial Neural Networks ■ Chapter 4 – Convolutional Neural Networks ■ Chapter 5 – Recurrent Neural Networks ■ Chapter 6 – Attention Mechanism ■ Chapter 7 – Tree-based Neural Networks ○ Section 3: Deep Learning Design and Tools ■ Chapter 8 – Autoencoders ■ Chapter 9 – Data Generation ■ Chapter 10 – Meta-optimization ■ Chapter 11 – Multi-model arrangement ■ Chapter 12 – Deep Learning Interpretability ○ Appendix A

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Book SynopsisThis book illustrates how data can be useful in solving business problems. It explores various analytics techniques for using data to discover hidden patterns and relationships, predict future outcomes, optimize efficiency and improve the performance of organizations. You''ll learn how to analyze data by applying concepts of statistics, probability theory, and linear algebra. In this new edition, both R and Python are used to demonstrate these analyses. Practical Business Analytics Using R and Python also features new chapters covering databases, SQL, Neural networks, Text Analytics, and Natural Language Processing.Part one begins with an introduction to analytics, the foundations required to perform data analytics, and explains different analytics terms and concepts such as databases and SQL, basic statistics, probability theory, and data exploration. Part two introduces predictive models using statistical machine learning and discusses concepts like regression, classifiTable of ContentsSection 1: Introduction to AnalyticsIn this section, we discuss the necessary foundations required to perform data analytics. We discuss different analytics terms, basics statistics and probability theory, descriptive statistics including various plots, and various measures for evaluating your predictive models. Chapter 1: Business Analytics RevolutionChapter 2: Foundations of Business AnalyticsChapter 3: Structured Query Language (SQL) AnalyticsChapter 4: Business Analytics Process Chapter 5: Exploratory Data Analysis (EDA)Chapter 6: Evaluating Analytics Model PerformanceSection II: Supervised Learning and Predictive AnalyticsIn this section, we introduce statistical learning models and machine learning models. We present various regression analysis and classification analysis. We also discuss logistic regression and end our discussion by introducing Neural Network and gradient descent algorithms. Chapter 7: Simple Linear RegressionsChapter 8: Multiple Linear RegressionsChapter 9: ClassificationChapter 10: Neural NetworksChapter 11: Logistic RegressionSection III: Time series modelsIn this section, we introduce optimization models and Time series analysis. In time series, we discuss different forecasting models, and in optimization models, we introduce both linear and non-linear optimization models.Chapter 12: Time Series – ForecastingSection IV: Unsupervised model and Text MiningIn this section, we discuss two popular unsupervised models - cluster analysis and relationship data mining techniques. Finally, we end this section by introducing text mining and NLP and briefly introducing big data. Chapter 13: Cluster AnalysisChapter 14: Relationship Data MiningChapter 15: Mining Text and Text Analytics Chapter 16: Big Data and Big Data AnalyticsSection V: Business Analytics ToolsThis is the last part. In this section we This section summarizes what we have learned in the earlier section by working on some case studies. We work on practical cases using public datasets using both ‘R’ and ‘Python’.Chapter 17: R programming for AnalyticsChapter 18: Python Programming for Analytics

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Book SynopsisIntermediate-Advanced user levelTable of Contents1: Introduction to Numerical Methods in Kotlin.- 2: Linear Algebra.- 3: Finding Roots of Equations.-4: Finding Roots of Systems of Equations.- 5: Curve Fitting and Interpolation.- 6: Numerical Differentiation and Integration.- 7: Ordinary Differential Equations.- 8: Partial Differential Equations.- 9: Unconstrained Optimization.- 10: Constrained Optimization.- 11: Heuristics.- 12: Basic Statistics.- 13: Random Numbers and Simulation.- 14: Linear Regression.- 15: Time Series Analysis.- References.Table of ContentsAbout the Authors...........................................................................................................iPreface............................................................................................................................ii1. Why Kotlin?..............................................................................................................61.1. Kotlin in 2022.....................................................................................................61.2. Kotlin vs. C++....................................................................................................61.3. Kotlin vs. Python................................................................................................61.4. Kotlin in the future .............................................................................................62. Data Structures.......................................................................................................72.1. Function...........................................................................................................72.2. Polynomial ......................................................................................................73. Linear Algebra .......................................................................................................83.1. Vector and Matrix ...........................................................................................83.1.1. Vector Properties .....................................................................................83.1.2. Element-wise Operations.........................................................................83.1.3. Norm ........................................................................................................93.1.4. Inner product and angle ...........................................................................93.2. Matrix............................................................................................................103.3. Determinant, Transpose and Inverse.............................................................103.4. Diagonal Matrices and Diagonal of a Matrix................................................103.5. Eigenvalues and Eigenvectors.......................................................................103.5.1. Householder Tridiagonalization and QR Factorization Methods..........103.5.2. Transformation to Hessenberg Form (Nonsymmetric Matrices)...........104. Finding Roots of Single Variable Equations .......................................................114.1. Bracketing Methods ......................................................................................114.1.1. Bisection Method ...................................................................................114.2. Open Methods...............................................................................................114.2.1. Fixed-Point Method ...............................................................................114.2.2. Newton’s Method (Newton-Raphson Method) .....................................114.2.3. Secant Method .......................................................................................114.2.4. Brent’s Method ......................................................................................115. Finding Roots of Systems of Equations...............................................................125.1. Linear Systems of Equations.........................................................................125.2. Gauss Elimination Method............................................................................125.3. LU Factorization Methods ............................................................................125.3.1. Cholesky Factorization ..........................................................................125.4. Iterative Solution of Linear Systems.............................................................125.5. System of Nonlinear Equations.....................................................................126. Curve Fitting and Interpolation............................................................................146.1. Least-Squares Regression .............................................................................146.2. Linear Regression..........................................................................................146.3. Polynomial Regression..................................................................................146.4. Polynomial Interpolation...............................................................................146.5. Spline Interpolation .......................................................................................147. Numerical Differentiation and Integration...........................................................157.1. Numerical Differentiation .............................................................................157.2. Finite-Difference Formulas...........................................................................157.3. Newton-Cotes Formulas................................................................................157.3.1. Rectangular Rule....................................................................................157.3.2. Trapezoidal Rule....................................................................................157.3.3. Simpson’s Rules.....................................................................................157.3.4. Higher-Order Newton-Coles Formulas..................................................157.4. Romberg Integration .....................................................................................157.4.1. Gaussian Quadrature..............................................................................157.4.2. Improper Integrals..................................................................................158. Numerical Solution of Initial-Value Problems....................................................168.1. One-Step Methods.........................................................................................168.2. Euler’s Method..............................................................................................168.3. Runge-Kutta Methods...................................................................................168.4. Systems of Ordinary Differential Equations.................................................169. Numerical Solution of Partial Differential Equations..........................................179.1. Elliptic Partial Differential Equations...........................................................179.1.1. Dirichlet Problem...................................................................................179.2. Parabolic Partial Differential Equations........................................................179.2.1. Finite-Difference Method ......................................................................179.2.2. Crank-Nicolson Method.........................................................................179.3. Hyperbolic Partial Differential Equations.....................................................1710..................................................................................................................................1811..................................................................................................................................1912. Random Numbers and Simulation ....................................................................2012.1. Uniform Distribution .................................................................................2012.2. Normal Distribution...................................................................................2012.3. Exponential Distribution............................................................................2012.4. Poisson Distribution ..................................................................................2012.5. Beta Distribution........................................................................................2012.6. Gamma Distribution ..................................................................................2012.7. Multi-dimension Distribution ....................................................................2013. Unconstrainted Optimization ............................................................................2113.1. Single Variable Optimization ....................................................................2113.2. Multi Variable Optimization .....................................................................2114. Constrained Optimization .................................................................................2214.1. Linear Programming..................................................................................2214.2. Quadratic Programming ............................................................................2214.3. Second Order Conic Programming............................................................2214.4. Sequential Quadratic Programming...........................................................2214.5. Integer Programming.................................................................................2215. Heuristic Optimization......................................................................................2315.1. Genetic Algorithm .....................................................................................2315.2. Simulated Annealing .................................................................................2316. Basic Statistics..................................................................................................2416.1. Mean, Variance and Covariance................................................................2416.2. Moment......................................................................................................2416.3. Rank...........................................................................................................2417. Linear Regression .............................................................................................2517.1. Least-Squares Regression..........................................................................2517.2. General Linear Least Squares....................................................................2518. Time Series Analysis ........................................................................................2618.1. Univariate Time Series..............................................................................2618.2. Multivariate Time Series ...........................................................................2618.3. ARMA .......................................................................................................2618.4. GARCH .....................................................................................................2618.5. Cointegration .............................................................................................2619. Bibliography .....................................................................................................2720. Index .....................................................................................................

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Book SynopsisThis unique open access book applies the functional OCaml programming language to numerical or computational weighted data science, engineering, and scientific applications. This book is based on the authors' first-hand experience building and maintaining Owl, an OCaml-based numerical computing library.You'll first learn the various components in a modern numerical computation library. Then, you will learn how these components are designed and built up and how to optimize their performance. After reading and using this book, you'll have the knowledge required to design and build real-world complex systems that effectively leverage the advantages of the OCaml functional programming language. What You Will LearnOptimize core operations based on N-dimensional arraysDesign and implement an industry-level algorithmic differentiation moduleImplement mathematical optimization, regression, and deep neural network functionalities based on algorithmic differentiationDesign and optimize a compTable of ContentsPrologueA Brief HistoryReductionism vs. HolismKey FeaturesContact MePART 1: NUMERICAL TECHNIQUES1. IntroductionWhat Is Scientific ComputingWhat is Functional ProgrammingWho Is This Book ForStructure of the BookInstallationOption 1: Install from OPAMOption 2: Pull from Docker HubOption 3: Pin the Dev-RepoOption 4: Compile from SourceCBLAS/LAPACKE DependencyInteracting with OwlUsing ToplevelUsing NotebookUsing Owl-JupyterSummary2. ConventionsPure vs. ImpureNdarray vs. ScalarInfix OperatorsOperator ExtensionModule StructuresNumber and PrecisionPolymorphic FunctionsModule ShortcutsType Casting3. VisualisationCreate PlotsSpecificationSubplotsMultiple LinesLegendDrawing PatternsLine PlotScatter PlotStairs PlotBox PlotStem PlotArea PlotHistogram & CDF PlotLog Plot3D PlotAdvanced Statistical PlotSummaryReferences4. Mathematical FunctionsBasic FunctionsBasic Unary Math FunctionsBasic Binary FunctionsExponential and Logarithmic FunctionsTrigonometric FunctionsOther Math FunctionsSpecial FunctionsAiry FunctionsBessel FunctionsElliptic FunctionsGamma FunctionsBeta FunctionsStruve FunctionsZeta FunctionsError FunctionsIntegral FunctionsFactorialsInterpolation and ExtrapolationIntegrationUtility FunctionsSummary5. Statistical FunctionsRandom VariablesDiscrete Random VariablesContinuous Random VariablesDescriptive StatisticsOrder StatisticsSpecial DistributionGamma DistributionBeta DistributionChi-Square DistributionStudent-t DistributionCauchy DistributionMultiple VariablesSamplingHypothesis TestsTheoryGaussian Distribution in Hypothesis TestingTwo-Sample InferencesGoodness-of-fit TestsNon-parametric StatisticsCovariance and CorrelationsAnalysis of VarianceSummary6. N-Dimensional ArraysNdarray TypesCreation FunctionsProperties FunctionsMap FunctionsFold FunctionsScan FunctionsComparison FunctionsVectorised FunctionsIteration FunctionsManipulation FunctionsSerialisationTensorsSummaryReferences7. Slicing and BroadcastingSlicingBasic SlicingFancy SlicingConventions in DefinitionExtended OperatorsAdvanced UsageBroadcastingWhat Is Broadcasting?Shape ConstraintsSupported OperationsSlicing in NumPy and JuliaInternal MechanismSummary8. Linear AlgebraVectors and MatricesCreating MatricesAccessing ElementsIterate, Map, Fold, and FilterMath OperationsGaussian EliminationLU FactorisationInverse and TransposeVector SpacesRank and BasisOrthogonalitySolving Ax = bMatrix SensitivityDeterminantsEigenvalues and EigenvectorsSolving Ax=λ xComplex MatricesSimilarity Transformation and DiagonalisationPositive Definite MatricesPositive DefinitenessSingular Value DecompositionInternal: CBLAS and LAPACKELow-level Interface to CBLAS & LAPACKESparse MatricesSummaryReferences9. Ordinary Differential EquationsWhat Is An ODEExact SolutionsLinear SystemsSolving An ODE NumericallyOwl-ODEExample: Linear Oscillator SystemSolver StructureSymplectic SolversFeatures and LimitsExamples of using Owl-ODEExplicit ODETwo Body ProblemLorenz AttractorDamped OscillationStiffnessSolve Non-Stiff ODEsSolve Stiff ODEsSummaryReferences10. Signal ProcessingDiscrete Fourier TransformFast Fourier TransformExamplesApplications of FFTFind period of sunspotsDecipher the ToneImage ProcessingFilteringExample: SmoothingGaussian FilterSignal ConvolutionFFT and Image ConvolutionSummaryReferences11. Algorithmic DifferentiationChain RuleDifferentiation MethodsHow Algorithmic Differentiation WorksForward ModeReverse ModeForward or Reverse?A Strawman AD EngineSimple Forward ImplementationSimple Reverse ImplementationUnified ImplementationsForward and Reverse Propagation APIExpressing ComputationExample: Forward ModeExample: Reverse ModeHigh-Level APIsDerivative and GradientJacobianHessian and LaplacianOther APIsInternal of Algorithmic DifferentiationGo Beyond Simple ImplementationExtend AD moduleLazy EvaluationSummaryReferences12. OptimisationIntroductionRoot FindingUnivariate Function OptimisationUse DerivativesGolden Section SearchMultivariate Function OptimisationNelder-Mead Simplex MethodGradient Descent MethodsConjugate Gradient MethodNewton and Quasi-Newton MethodsGlobal Optimisation and Constrained OptimisationSummaryReferences13. RegressionLinear RegressionProblem: Where to locate a new McDonald’s restaurant?Cost FunctionSolving Problem with Gradient DescentMultiple RegressionFeature NormalisationAnalytical SolutionNon-linear regressionsRegularisationOls, Ridge, Lasso, and Elastic_netLogistic RegressionSigmoid FunctionCost FunctionExampleMulti-class classificationSupport Vector MachineKernel and Non-linear BoundaryExampleModel error and selectionError MetricsModel SelectionSummaryReferences14. Deep Neural NetworksPerceptronYet Another RegressionModel RepresentationForward PropagationBack propagationFeed Forward NetworkLayersActivation FunctionsInitialisationTrainingTestNeural Network ModuleModule StructureNeuronsNeural GraphTraining ParametersConvolutional Neural NetworkRecurrent Neural NetworkLong Short Term Memory (LSTM)Generative Adversarial NetworkSummaryReferences15. Natural Language ProcessingIntroductionText CorpusStep-by-step OperationUse the Corpus ModuleVector Space ModelsBag of Words (BOW)Term Frequency–Inverse Document Frequency (TF-IDF)Latent Dirichlet Allocation (LDA)ModelsDirichlet DistributionGibbs SamplingTopic Modelling ExampleLatent Semantic Analysis (LSA)Search Relevant DocumentsEuclidean and Cosine SimilarityLinear SearchingSummaryReferences16. Dataframe for Tabular DataBasic ConceptsCreate FramesManipulate FramesQuery FramesIterate, Map, and FilterRead/Write CSV FilesInfer Type and SeparatorSummary17. Symbolic RepresentationIntroductionDesignCore abstractionEnginesONNX EngineExample 1: Basic operationsExample 2: Variable InitialisationExample 3: Neural networkLaTeX EngineOwl EngineSummary18. Probabilistic ProgrammingGenerative Model vs Discriminative ModelBayesian NetworksSampling TechniquesInferencePART 2: SYSTEM ARCHITECTURE19. Architecture OverviewIntroductionArchitecture OverviewCore ImplementationN-dimensional ArrayInterfaced LibrariesAdvanced FunctionalityComputation GraphAlgorithmic DifferentiationRegressionNeural NetworkParallel ComputingActor EngineGPU ComputingOpenMPCommunity-Driven R&DSummary20. Core OptimisationBackgroundNumerical LibrariesOptimisation of Numerical ComputationInterfacing to C CodeNdarray OperationsFrom OCaml to COptimisation TechniquesMap OperationsConvolution OperationsReduction OperationsRepeat OperationsSummaryReferences21. Automatic Empirical TuningWhat is Parameter TuningWhy Parameter Tuning in OwlHow to Tune OpenMP ParametersMake a DifferenceSummary22. Computation GraphIntroductionWhat is a Computation Graph?From Dynamic to StaticSignificance in ComputingExamplesExample 01: Basic CGraphExample 02: CGraph with ADExample 03: CGraph with DNNDesign RationaleOptimisation of CGraphOptimising memory with pebblesAllocation AlgorithmAs Intermediate RepresentationsSummary23. Scripting and Zoo SystemIntroductionShare Script with ZooTypical ScenarioCreate a ScriptShare via GistImport in Another ScriptSelect a Specific VersionCommand Line ToolMore ExamplesSystem DesignServicesType CheckingBackendDomain Specific LanguageService DiscoveryUse CaseSummaryReferences24. Compiler BackendsBase LibraryBackend: JavaScriptUse Native OCamlUse Facebook ReasonBackend: MirageOSMirageOS and UnikernelExample: Gradient DescentExample: Neural NetworkEvaluationSummary25. Distributed ComputingActor SystemDesignActor EnginesMap-Reduce EngineParameter Server EnginePeer-to-Peer EngineClassic Synchronise ParallelBulk Synchronous ParallelAsynchronous ParallelStale Synchronous ParallelProbabilistic Synchronise ParallelBasic idea: samplingCompatibilityBarrier Trade-off DimensionsConvergenceA Distributed Training ExampleStep ProgressAccuracySummaryReferences26. Testing FrameworkUnit TestExampleWhat Could Go WrongCorner CasesTest CoverageUse FunctorSummary27. Constants and Metric SystemWhat Is a Metric SystemFour Metric SystemsSI PrefixExample: Physics and Math constantsInternational System of UnitsTimeLengthAreaVolumeSpeedMassForceEnergyPowerPressureViscosityLuminanceRadioactivity28. Internal Utility ModulesDataset ModuleMNISTCIFAR-10Graph ModuleStack and Heap ModulesCount-Min SketchSummaryPART 3: CASE STUDIES29. Case - Image RecognitionBackgroundLeNetAlexNetVGGResNetSqueezeNetCapsule NetworkBuilding InceptionV3 NetworkInceptionV1 and InceptionV2FactorisationGrid Size ReductionInceptionV3 ArchitecturePreparing WeightsProcessing ImageRunning InferenceApplicationsSummaryReferences30. Case - Instance SegmentationIntroductionMask R-CNN NetworkBuilding Mask R-CNNFeature ExtractorProposal GenerationClassificationRun the CodeSummaryReferences31. Case - Neural Style TransferContent and StyleContent ReconstructionStyle RecreationCombining Content and StyleRunning NSTExtending NSTFast Style TransferBuilding FST NetworkRunning FSTSummaryReferences32. Case - Recommender SystemIntroductionArchitectureBuild Topic ModelsIndex Text CorpusRandom ProjectionOptimising Vector StorageOptimise Data StructureOptimise Index AlgorithmSearch ArticlesCode ImplementationMake It LiveSummaryReferences33. Case - Applications in FinanceIntroductionBond PricingBlack-Scholes ModelMathematical ModelOption PricingPortfolio OptimisationMathematical ModelEfficient FrontierMaximise Sharpe Ratio

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Book SynopsisLearn how to use PyTorch to build neural network models using code snippets updated for this second edition. This book includes new chapters covering topics such as distributed PyTorch modeling, deploying PyTorch models in production, and developments around PyTorch with updated code. You'll start by learning how to use tensors to develop and fine-tune neural network models and implement deep learning models such as LSTMs, and RNNs. Next, you'll explore probability distribution concepts using PyTorch, as well as supervised and unsupervised algorithms with PyTorch. This is followed by a deep dive on building models with convolutional neural networks, deep neural networks, and recurrent neural networks using PyTorch. This new edition covers also topics such as Scorch, a compatible module equivalent to the Scikit machine learning library, model quantization to reduce parameter size, and preparing a model for deployment within a production system. Distributed parallel processing for balaTrade Review“The book covers all important facets of neural network implementation and modeling, and could definitely be useful to students and developers keen for an in-depth look at how to build models using PyTorch, or how to engineer particular neural network features using this platform.” (Mariana Damova, Computing Reviews, July 24, 2023)Table of ContentsChapter 1: Introduction to PyTorch, Tensors, and Tensor OperationsChapter Goal: This chapter is to understand what is PyTorch and its basic building blocks.Chapter 2: Probability Distributions Using PyTorchChapter Goal: This chapter aims at covering different distributions compatible with PyTorch for data analysis. Chapter 3: Neural Networks Using PyTorchChapter Goal: This chapter explains the use of PyTorch to develop a neural network model and optimize the model.Chapter 4: Deep Learning (CNN and RNN) Using PyTorchChapter Goal: This chapter explains the use of PyTorch to train deep neural networks for complex datasets.Chapter 5: Language Modeling Using PyTorchChapter Goal: In this chapter, we are going to use torch text for natural language processing, pre-processing, and feature engineering. Chapter 6: Supervised Learning Using PyTorchGoal: This chapter explains how supervised learning algorithms implementation with PyTorch. Chapter 7: Fine Tuning Deep Learning Models using PyTorchGoal: This chapter explains how to Fine Tuning Deep Learning Models using the PyTorch framework.Chapter 8: Distributed PyTorch ModelingChapter Goal: This chapter explains the use of parallel processing using the PyTorch framework.Chapter 9: Model Optimization Using Quantization MethodsChapter Goal: This chapter explains the use of quantization methods to optimize the PyTorch models and hyperparameter tuning with ray tune. Chapter 10: Deploying PyTorch Models in ProductionChapter Goal: In this chapter we are going to use torch serve, to deploy the PyTorch models into production. Chapter 11: PyTorch for AudioChapter Goal: In this chapter torch audio will be used for audio resampling, data augmentation, features extractions, model training, and pipeline development. Chapter 12: PyTorch for ImageChapter Goal: This chapter aims at using Torchvision for image transformations, pre-processing, feature engineering, and model training. Chapter 13: Model Explainability using CaptumChapter Goal: In this chapter, we are going to use the captum library for model interpretability to explain the model as if you are explaining the model to a 5-year-old. Chapter 14: Scikit Learn Model compatibility using SkorchChapter Goal: In this chapter, we are going to use skorch which is a high-level library for PyTorch that provides full sci-kit learn compatibility.

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Book SynopsisTable of ContentsIntroduction. Three stories that made me write this bookChapter 1. Data preparation 1.1 Analyzing and transforming the original data 1.2 Preparing the basis for a dashboard 1.3 Making data samples for visualizations 1.4. Setting up an interactivity 1.5. Summary and conclusions of the chapter, quick tricks Chapter 2. Dashboard assembling 2.1 Assembling a dashboard according to the layout 2.2 Creating KPI cards 2.3 Aligning a dashboard, adding a header 2.4 Summary and conclusions of the chapter, quick tricks Chapter 3. Anatomy of diagrams 3.1 Analyzing ready-made design styles 3.2 Setting up data labels 3.3 Working with the text: remove the excess, add the necessary 3.4 Designing bar charts 3.5 Setting up the chart template 3.6 Summary and conclusions of the chapter, quick tricks Chapter 4. Final dashboard design 4.1 Aligning the headers to the grid 4.2 Creating new cards on the top of the cells 4.3 Making interactive slicers 4.4 Working with Excel colors and fonts 4.5 Improving standard Excel themes 4.6. Summary and conclusions of the chapter, quick tricks Chapter 5. Corporate identity 5.1 Creating a theme in accordance with the brandbook 5.2 Adapting the theme according to the checklist 5.3 Creating a dashboard in a dark theme 5.4 Summary and conclusions of the chapter, quick tricks Chapter 6. Data visualization rules 6.1 Types of data analysis 6.2 How to choose charts 6.3 Life hacks for multiple data series 6.4 When you need everything at once 6.5 Funnel and waterfall 6.6 Summary and conclusions of the chapter, quick tricks Conclusion

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Book SynopsisIntermediate user levelTable of Contents1. Getting Started with MySQL in the Cloud2. Oracle Cloud Infrastructure3. A Brief Tutorial of MySQL4. MySQL Database Service5. Backup and Restore6. Point-in-Time Recovery7. Data Import and Export8. High Availability9. OCI Command-Line Interface and Application Programming Interfaces10. Migrating to MDS

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Book SynopsisJourney into the world of Web3-based application development, its related protocols, and its usage in developing decentralized applications. This book will explain how programmable blockchains are revolutionizing the world of web applications, which can be run on decentralized platforms or peer-to-peer networks like IPFS. You'll start with an introduction to decentralization with a focus on blockchain implementations like Ethereum and Bitcoin. You'll then learn to develop simple decentralized applications (dApps) using Solidity, the language used for developing apps with Ethereum as well as smart contracts, wallets, gateways and NFTs. This book also covers how security and scale are addressed by L2 networks for scaling Bitcoin and Ethereum blockchains.A Brief Introduction to Web3is your go-to guide for setting up simple Web3 applications using the Ethereum blockchain programming model. WhatYou Will LearnBuild NFT tokensExamine Web3 differs from Web2-based applicationsUnderstand theTable of ContentsChapter1. Introduction to DecentralizationChapter 2. BlockchainChapter 3.Solidity.Chapter 4. Wallets and Gateways.Chapter 5. Remix IDE.Chapter 6. Truffle.Chapter 7. IPFS and NFTsChapter 8. Hardhat.

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Book SynopsisIntermediate user levelTable of ContentsIntroductionPart I. Introduction to Containers1. Introducing Docker and Oracle2. Understanding the Container Landscape3. Container Foundations4. Oracle Database Quick Start Guide5. Differences in Database Containers6. Customize Container Environments7. Persistence8. Basic Networking9. Container Networks10. Container Creation Quick ReferencePart II. Building and Customizing Images11. Customizing Images12. Dockerfile Syntax13. Dockerfiles for Orcale Databases14. Building Images15. Debugging and Troubleshooting16. Docker Hub and Image Repositories17. ConclusionPart III. AppendixesA. Installing Docker Desktop

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Book SynopsisTake a deep dive into the Azure SQL Database Hyperscale Service Tier and discover a new form of cloud architecture from Microsoft that supports massive databases. The new horizontally scalable architecture, formerly code-named Socrates, allows you to decouple compute nodes from storage layers. This radically different approach dramatically increases the scalability of the service. This book shows you how to leverage Hyperscale to provide next-level scalability, high throughput, and fast performance from large databases in your environment. The book begins by showing how Hyperscale helps you eliminate many of the problems of traditional high-availability and disaster recovery architecture. You''ll learn how Hyperscale overcomes storage capacity limitations and issues with scale-up times and costs. With Hyperscale, your costs do not increase linearly with database size and you can manage more data than ever at a lower cost. The book teaches you how tTable of ContentsIntroductionPart I. Architecture.1. The Journey to Hyperscale Architecture in Azure SQL2. Azure SQL Hyperscale Architecture: Concepts and FoundationsPart II. Planning and Deployment3. Planning an Azure SQL DB Hyperscale Environment 4. Deploying a Highly Available Hyperscale Database into a Virtual Network 5. Administering a Hyperscale Database in a Virtual Network in the Azure Portal6. Configuring Transparent Data Encryption to Bring Your Own Key7. Enabling Geo-replication for Disaster Recovery8. Configuring Security Features and Enabling Diagnostic and Audit Logs9. Deploying Azure SQL DB Hyperscale using PowerShell10. Deploying Azure SQL DB Hyperscale using Bash and Azure CLI11. Deploying Azure SQL DB Hyperscale using Azure Bicep12. Testing Hyperscale Database Performance Against Other Azure SQL Deployment OptionsPart III. Operation and Management13. Monitoring and Scaling 14. Backup, Restore and Disaster Recovery15. Security and Updating16. Managing CostsPart IV. Migration17. Determining whether Hyperscale is Appropriate 18. Migrating to Hyperscale19. Reverse Migrating Away from HyperscaleConclusion

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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.

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Book Synopsis1. An Introduction to Data Analysis .- 2. Introduction to the Python's World.- 3. The NumPy Library .- 4. The pandas Library-- An Introduction.- 5. pandas: Reading and Writing Data .- 6. pandas in Depth: Data Manipulation .- 7. Data Visualization with matplotlib .- 8. Machine Learning with scikit-learn.- 9. Deep Learning with TensorFlow.- 10. An Example - Meteorological Data.- 11. Embedding the JavaScript D3 Library in IPython Notebook.- 12. Recognizing Handwritten Digits.- 13. Textual data Analysis with NLTK.- 14. Image Analysis and Computer Vision with OpenCV.- Appendix A.- Appendix B.Table of ContentsPython Data Analytics1. An Introduction to Data Analysis 2. Introduction to the Python's World3. The NumPy Library 4. The pandas Library-- An Introduction5. pandas: Reading and Writing Data 6. pandas in Depth: Data Manipulation 7. Data Visualization with matplotlib 8. Machine Learning with scikit-learn9. Deep Learning with TensorFlow10. An Example - Meteorological Data11. Embedding the JavaScript D3 Library in IPython Notebook12. Recognizing Handwritten Digits13. Textual data Analysis with NLTK 14. Image Analysis and Computer Vision with OpenCV Appendix A Appendix B

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Book SynopsisThis book provides detailed guidance around architecting and deploying Power BI reporting solutions, including help and best practices for sharing and security. You''ll find chapters on dataflows, shared datasets, composite model and DirectQuery connections to Power BI datasets, deployment pipelines, XMLA endpoints, and many other important features related to the overall Power BI architecture that are new since the first edition. You will gain an understanding of what functionality each of the Power BI components provide (such as Dataflow, Shared Dataset, Datamart, thin reports, and paginated reports), so that you can make an informed decision about what components to use in your solution. You will get to know the pros and cons of each component, and how they all work together within the larger Power BI architecture.Commonly encountered problems you will learn to handle include content unexpectedly changing while users are in the process of creating reports and bTable of ContentsIntroductionPart I. Getting Started1. Power BI Ecosystem and Components2. Tools and PreparationPart II. Development3. Import Data or Schedule Refresh4. DirectQuery 5. Live Connection6. Composite Mode7. Choosing the Right Connection Type8. Dataflows9. Shared Datasets10. Multi-Developer Architecture11. Hybrid Architecture using other Microsoft Services12. DirectQuery to Power BI Dataset13. Dataflow Development Architecture14. Analyze in Excel15. Development Tools16. Power BI Helper for Developers17. Dataset Types18. Realtime Power BI Solution19. Paginated Reports20. Power BI Templates21. Power BI Desktop Development Templates22. Incremental Refresh23. Big Data Considerations, Hybrid Tables, and PerformancePart III. Deployment24. Power BI Service Content25. Power BI Report Server26. Gateway27. Power BI Licensing Guide28. Power BI Premium29. Premium Per User30. Premium Settings and Configuration31. Tenant Settings32. Administrator Reports and Metrics33. Workspace Structure and Architecture34. Workspace Rules35. Deployment Pipeines36. REST API for Deployment and Architecture37. Power BI Helper for Deployment and Administration38. XMLA EndpointPart IV. Sharing and Security39. Governance40. Dashboard and Report Sharing41. Workspaces as Collaborative Environments42. Power BI Apps43. Embed Code and Publish to Web44. Embed in SharePoint Online45. Microsoft Teams Integration46. Power BI Embedded47. SharePoint Online Integration48. Microsoft Office49. Comparing Power BI Sharing Methods50. Usage Metrics Reports51. Usage Metrics using REST API52. Usage Metrics using Power BI Helper53. Row Level Security54. Dynamic Row Level Security55. Object-Level Security

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Book SynopsisThis book will help prepare you for the Microsoft DP-420 exam. Whether you are new to Azure Cosmos DB or have experience working with the platform, Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB is organized to address the specific skills measured in the DP-420 exam. The topics covered include NoSQL models, code, and real-world scenarios aimed at helping you to understand and solve the case studies included in the exam.Beyond the exam, this book will assist you in your journey to adopt Microsoft Azure Cosmos DB for your own projects. You'll learn what makes Azure Cosmos DB such a robust NoSQL service, as well as how NoSQL approaches help enable modern applications. You'll also get practical guidance for your own implementations. The topics covered in this book are essential to knowing how to leverage the Cosmos DB service and provide best practices that will guide you to success both on the exam and in your career. What You Will LearnUnderstand aTable of Contents1. Scheduling and Taking the DP-420 Exam2. Design and Implement a Non-Relational Data Model3. Design a Data Partitioning Strategy4. Plan and implement Sizing and Scaling5. Implement Client Connectivity Options 6. Implement Data Access with Cosmos DB SQL 7. Implement Data Access with SQL API SDKs8. Implement Server-Side Programming9. Design and Implement a Replication Strategy 10. Design and Implement Multi-Region Write11. Enable Analytical Workloads 12. Implement Solutions Across Services13. Optimize Query Performance 14. Design and Implement Change Feeds 15. Define and Implement an Indexing Strategy 16. Monitor and Troubleshoot17. Implement Backup and Restore 18. Implement Security19. Implement Data Movement 20. Implement a DevOps Process

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Book SynopsisCrypto is going to change the world, and for those tired of confusing financial jargon and complicated technical terminology, look no further. This book demystifies the world of cryptocurrencies and blockchain technology and explains in accessible language how it will affect your daily life. In The Quiet Crypto Revolution, Klaas Jung dives beneath the surface of Bitcoin to explore the engine that powers it - blockchain. Far surpassing the confines of cryptocurrencies, blockchain's potential for wide-ranging applications is enormous. It's crucial to understand that cryptocurrencies are merely a single manifestation of blockchain's capabilities. This book casts light on the broader spectrum of blockchain applications and the exciting future of this groundbreaking technology. With a focus on real-world applications, you'll gain a deeper understanding of the key concepts behind the innovative technology of blockchain, equipping you to make informed decisions. Whether you're a tech-savvy iTable of Contents1. Introduction to The Crypto Revolution.- 2. Understanding the Blockchain.- 3. The future of blockchain technology.- 4. Cryptocurrency in Practice.- 5. The Future of Decentralized Finance.- 6. Security and Scams.- 7. Crypto Pioneers: Exploring Entrepreneurial Opportunities.- 8. Final Thoughts: The Future of Crypto.

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Book SynopsisUse JSON theme files to standardize the look of Power BI dashboards and reports. This book shows how you can create theme files using the Power BI Desktop application to define high-level formatting attributes for dashboards as well as how to tailor detailed formatting specifications for individual dashboard elements in JSON files. Standardize the look of your dashboards and apply formatting consistently over all your reports. The techniques in this book provide you with tight control over the presentation of all aspects of the Power BI dashboards and reports that you create.Power BI theme files use JSON (JavaScript Object Notation) as their structure, so the book includes a brief introduction to JSON as well as how it applies to Power BI themes. The book further includes a complete reference to all the current formatting definitions and JSON structures that are at your disposal for creating JSON theme files up to the May 2023 release of Power BI Desktop. Finally, the book includes dozTable of Contents1. Introduction to Power BI Themes2. Create and Customize a Theme In Power BI Desktop3. High-Level Theme Definition4. Default Visual Styles5. Object Visual Styles6. Card and Table Visual Styles7. Classic Chart Visual Styles8. Complex Chart Visual Styles9. Other Chart Visual Styles10. Maps11. Miscellaneous Visual Styles12. Dashboard Styling13. Cascading Styles

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Book SynopsisLearn blockchain in a simple, non-tech way and explore the different emerging technologies that open a world of opportunities in the space of tourism and hospitality. This book showcases examples of blockchain-based solutions implemented in different industries and connects them to use cases in hospitality and tourism (disintermediation, payments, loyalty programs, supply chain management, identity management etc.).Blockchain is one of the disruptive technologies that lays foundations for Web3.0, NFTs, Metaverse and other innovations. Despite many benefits, its adoption in the hospitality industry is very slow. Lack of awareness and connection to clear return-on-investment, coupled with many misconceptions and general perception of complexity is one of the main reasons why hospitality managers are reluctant to embark on the blockchain train. Blockchain for Hospitality and Tourism serves as a practical guide to the world of innovations, from the basics of blockchain to how to start a Table of ContentsChapter 1: Introduction Chapter goal: Intro to the topic of blockchain – why blockchain is a game-changer, what you’ll learn, why is it important to learn about emerging tech · Blockchain potential · Hospitality and Tourism challenges and trends and the correlation with new tech · Challenges with innovation adoption in the hospitality industry Chapter 2: Demystifying Blockchain Chapter goal: explain – in non-tech way with visualizations – what blockchain is and how it works · What is Blockchain · Blockchain characteristics · Smart Contracts · Blockchain ecosystem – platforms with capabilities · Foundational role of blockchain as an enabler for other innovation o Cryptopayments, stablecoins and CBDCs o NFTs o Web3.0 o Metaverse o Industrial Revolution 4.0 Chapter 3: Blockchain applications Chapter goal: explain how blockchain is utilized in different industries today with real-life examples · Banking & Insurance · Healthcare · Public sector/Government services · Supply chain management etc. Chapter 4: Use cases for Hospitality & Tourism Chapter goal: showcase solutions that have been implemented in different geographies, trends, and directions · Identity Management · Customer loyalty programs – NFTs and blockchain-based platforms · Smart contracts and supply chain management o Food security and provenance tracking o Preventive maintenance and Smart Hotel applications o Sustainability · New distribution methods and disintermediation · Payments acceptance · Guest preferences and personalization · Digitization of assets · NFTs · Metaverse opportunities – digital twins, virtual floor plan walkthroughs, virtual trainings, marketing etc. Chapter 5: Risks and Challenges Chapter goal: Discuss blockchain maturity and adoption, interoperability, and state of regulations; address concerns around trust, fraud etc. Chapter 6: Blockchain projects – how to start Chapter goal: a walkthrough of the most important steps and decisions

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