“…Provides a background in data science fundamentals before moving on to working with relational databases and unstructured data and preparing your data for analysis Details different data visualization techniques that can be used to showcase and summarize your data Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark It's a big, big data world out there - let Data Science For Dummies help you harness its power and gain a competitive edge for your organization…”

“…Report topics include: The swing between centralized and distributed computing Machine learning as a service Personal digital assistants and cognitive augmentation Graph databases and analytics Regulating complex algorithms The pace of real-time data and automation Solving dire problems with big data Implications of having sensors everywhere This report contains many more examples of how big data is starting to reshape business and change behavior, and it's just a small sample of the in-depth information Strata + Hadoop World provides. …”

“…By the end of this book, you'll be able to analyse big data, and build and manage high-performance databases for your application. …”

“…Chaudhri is an Independent Consultant, specializing in big data, NoSQL, and NewSQL database technologies. …”

“…About Microsoft Certification Passing this exam as well as Exam 70-773: Analyzing Big Data with Microsoft R earns your MCSA: Machine Learning certification, demonstrating your expertise in operationalizing Microsoft Azure machine learning and Big Data with R Server and SQL R Services. …”

“…With its ability to process all kinds of data in real time, Storm is an important addition to your big data ?bag of tricks.? At the same time, Python is one of the fastest-growing programming languages today. …”

Tabla de Contenidos: “…8.4.1 LTE Security 254 -- 8.4.2 Network Attack Types in LTE/SAE 255 -- 8.4.3 Preparation for the Attacks 256 -- 8.5 Errors in Equipment Manufacturing 259 -- 8.5.1 Equipment Ordering 259 -- 8.5.2 Early Testing 260 -- 8.6 Self‐Organizing Network Techniques for Test and Measurement 264 -- 8.6.1 Principle 264 -- 8.6.2 Self‐configuration 265 -- 8.6.3 Self‐optimizing 266 -- 8.6.4 Self‐healing 266 -- 8.6.5 Technical Issues and Impact on Network Planning 266 -- 8.6.6 Effects on Network Installation, Commissioning and Optimization 267 -- 8.6.7 SON and Security 268 -- References 268 -- 9 Monitoring and Protection Techniques 270 -- 9.1 Overview 270 -- 9.2 Personal Devices 271 -- 9.2.1 Wi‐Fi Connectivity 271 -- 9.2.2 Firewalls 271 -- 9.3 IP Core Protection Techniques 272 -- 9.3.1 General Principles 272 -- 9.3.2 LTE Packet Core Protection 272 -- 9.3.3 Protection against Roaming Threats 275 -- 9.4 HW Fault and Performance Monitoring 276 -- 9.4.1 Network Monitoring 277 -- 9.4.2 Protection against DoS/DDoS 277 -- 9.4.3 Memory Wearing 277 -- 9.5 Security Analysis 278 -- 9.5.1 Post‐processing 278 -- 9.5.2 Real‐time Security Analysis 278 -- 9.6 Virus Protection 279 -- 9.7 Legal Interception 281 -- 9.8 Personal Safety and Privacy 283 -- 9.8.1 CMAS 283 -- 9.8.2 Location Privacy 285 -- 9.8.3 Bio‐effects 286 -- References 287 -- 10 Future of Wireless Solutions and Security 288 -- 10.1 Overview 288 -- 10.2 IoT as a Driving Force 288 -- 10.3 Evolution of 4G 289 -- 10.4 Development of Devices 291 -- 10.4.1 Security Aspects of Smartcards 291 -- 10.4.2 Mobile Device Considerations 291 -- 10.4.3 IoT Device Considerations 292 -- 10.4.4 Sensor Networks and Big Data 293 -- 10.5 5G Mobile Communications 294 -- 10.5.1 Standardization 294 -- 10.5.2 Concept 295 -- 10.5.3 Industry and Investigation Initiatives 297 -- 10.5.4 Role of 5G in IoT 297 -- References 297 -- Index 299.…”

Tabla de Contenidos: “…Motivation xviii About the Authors xxv 1 Globalization: Economies in Collision 1 Motivation 1 1.1 Introduction 1 1.2 Trend and Challenges in Global Finance 5 1.2.1 Global Finance and the 2007-2008 Financial Crisis 5 1.2.2 Geopolitics and Finance or Geo-Finance 7 1.2.3 Population Growth 9 1.2.4 Geo-Finance and Gating 9 1.2.5 Culture and Social Norms 10 1.2.6 Emerging Economies, Equalization, and Risks 11 1.2.7 Regulation and Politics 12 1.3 Global Finance and Trade in the Media and News 12 1.3.1 Taxing Foreign Returns When They Are Repatriated 12 1.3.2 Sovereign Gating and Protective Tariffs, Trade Defense 12 1.3.3 Global Insurance and Dependence 13 1.3.4 Finance and Politics 13 1.3.5 EU-US Regulation Risks in a Trans-Atlantic Market 13 1.3.6 Rare Earth and Strategic Trades 14 1.4 Global Risks 15 1.4.1 Factors Affecting Global Risks 15 1.4.2 Global Finance and Risk Theories 17 1.5 Global Finance and Swaps and Financial Products 18 1.6 Currencies and Liquidity 20 1.7 Foreign Exchange Regime and Markets, Global Payment, and Reserve Currency 23 1.7.1 Foreign Exchange Markets and Global Trade Currency 24 1.7.2 Exchange Rate Regimes 28 1.7.3 Foreign Exchange Reserve Currencies 29 1.7.4 Exchange Rate in Emerging Markets 30 1.8 Trade Incentives and Repressions 33 1.9 Historical Evolution 36 1.9.1 The Gold Standard System 36 1.9.2 The Bretton Woods Agreement 37 1.9.3 The Yuan Exchange Rate: An Example 38 1.10 Archaic and Modern Globalization: A Time Path 40 1.11 Discussions on Global Issues 44 References 46 2 Data, Measurements, and Global Finance 51 Motivation 51 2.1 Data and Models 51 2.1.1 National Accounts and Country-Specific Data 52 2.1.2 Financial Data 53 2.1.3 Corporate and Statistical Measurements 53 2.1.4 Big Data and Model-Less Finance 56 2.1.5 Technology and Financial Data 59 2.1.6 Transforming Data Management in Global Finance 60 2.2 Global Finance, Data Reduction, and Statistical Measurements 61 2.2.1 Moments 61 2.2.2 Skew and Kurtosis (Tails) 63 2.2.3 Outliers, Extreme Statistics, and Fat Tails 65 2.2.4 Extreme Risks 65 2.2.5 Time Series and Filtration 66 2.2.6 Dependence 67 2.2.7 Risk Exposure 75 2.3 Volatility and Implied Models Estimates 76 2.3.1 Simple Volatility Estimates 76 2.3.2 Implied Volatility 77 2.3.3 Moving Average and Autoregressive Moving Average Models 79 2.3.4 ARCH-GARCH Volatility Models 80 2.3.5 ARCH-GARCH Model: Empirical Evidence 82 2.4 Stochastic Models 86 2.4.1 The Constant Rate of Return Hypothesis 88 2.4.2 Autoregressive and Mean Reverting Models 90 2.5 Multivariate Probability Models 91 2.5.1 Bernoulli and Codependence: Qualitative Data 92 2.5.2 Multivariate Qualitative Data 93 2.5.3 Copulas and Multivariate Models 96 2.6 Statistical Data Reduction 100 2.7 Complexity: The Global Risk Finance Scourge 102 2.8 Discussion 103 References 104 3 Global Finance: Utility, Financial Consumption, and Asset Pricing 110 Motivation 110 3.1 Introduction: Financial Models and Pricing 110 3.1.1 The Walras-Arrow-Debreu Preference-Free Q Framework 111 3.1.2 Utility Finance 113 3.1.3 Utility Preferences and Pricing 114 3.2 Review: Elements of Utility and Risk 116 3.2.1 Utility and Risk Preferences 117 3.2.2 Selected Utility Functions 119 3.3 The Utility of Consumption and its Price 119 3.3.1 Example: The Financial Utility of Consumption 120 3.3.2 The Utility of Financial Consumption 121 3.4 The Capital Assets Pricing Model 123 3.4.1 Discussion 125 3.4.2 The Jensen Alpha Case: η = α−Rf 0 125 3.4.3 The Arbitrage Pricing Theory 126 3.4.4 The Capital Assets Pricing Model and Foreign Exchange 127 3.4.5 Pricing Foreign Exchange Rates and a Basket of Foreign Exchange Rates 128 3.4.6 Thought Experiment 130 3.4.7 Linear Regressions, Capital Assets Pricing Model, and Global Finance 130 3.5 Review of Elements of Arrow-Debreu and Q Pricing 131 3.5.1 Applications of the Arrow-Debreu Framework 134 3.5.2 Pricing a Foreign Bond and Foreign Exchange Rates 134 3.5.3 Global Investment Pricing: A Binomial Model 137 3.5.4 Global Investment Pricing: A Portfolio Problem 139 3.5.5 Foreign Exchange, Consumer Price Index, and Purchasing Power Parity 141 3.5.6 Relative Purchasing Power Parity 141 3.6 The Multiperiod Consumption-Based Capital Asset Pricing Model 142 3.6.1 Example: Pricing Exports 144 3.6.2 Example: The Multiagent Consumption-Based Capital Asset Pricing Model 145 3.6.3 Example: Portfolio Investment 146 3.6.4 The Capital Assets Pricing Model as a Special Case of the Consumption-Based Capital Asset Pricing Model 149 3.6.5 Foreign Exchange Rates, Bonds, and Equity 150 3.6.6 Example: Rate of Returns in Foreign Equity Markets and the Foreign Exchange Rate 153 3.7 Thought Experiments 154 References 155 4 Macroeconomics, Foreign Exchange, and Global Finance 157 Motivation 157 4.1 Introduction 157 4.2 Fundamental Macroeconomic Models 158 4.2.1 The Keynes Model: Goods and Money Markets in a Closed Economy 159 4.2.2 Macroeconomic Accounting in Open Economies 162 4.3 Balance of Payments Accounts 165 4.3.1 The Structure of Balance of Payments 165 4.3.2 Principles of Balance of Payments 167 4.3.3 Global External Imbalances 168 4.4 The Mundell and Fleming Model: IS-LM-BOP Model 171 4.4.1 Equilibrium Exchange Rate in the Foreign Exchange Market 171 4.4.2 IS-LM-BOP Model 173 4.4.3 Two-Country Model with Mundell-Fleming 175 4.4.4 Extension of the Mundell-Fleming Model: Trade 176 4.5 Macroeconomic Factors Reshaping Global Outlook 178 4.5.1 Foreign Direct Investment 178 4.5.2 External Debt and its Sustainability 182 4.5.3 Government Bonds and other Macroeconomic Factors 184 4.5.4 The Impact of a Strong US Dollar 185 4.5.5 The US Dollar and Commodity Prices 186 4.5.6 Globalization and Economic Inequalities 187 4.6 Conclusion 190 References 191 5 Foreign Exchange Models and Prices 195 Motivation 195 5.1 Introduction 195 5.2 Macroeconomic Models of Foreign Exchange Rates 196 5.2.1 Underlying Factors for Modeling Foreign Exchange Prices 196 5.2.2 Classification of Foreign Exchange Models 197 5.3 Exchange Rates and Purchasing Power Parity 198 5.3.1 Purchasing Power Parity and the Law of One Price 198 5.3.2 Application of the Law of One Price and Purchasing Power Parity 200 5.4 Foreign Exchange Rates and Interest Rates: The Assets Approach 204 5.4.1 Covered Interest Arbitrage 204 5.4.2 Uncovered Interest Parities 205 5.4.3 Real Interest Parity 206 5.4.4 Testing Uncovered Interest Parity 206 5.5 Demand and Supply of Money and Exchange Rates: Monetary Approach 207 5.5.1 The Frenkel-Mussa Model 208 5.5.2 Foreign Exchange Prices and Inflation 209 5.5.3 The Unified Asset/Monetary Approaches 211 5.5.4 Empirical Evidence on the Monetary Model of Exchange Rate 211 5.6 Extensions of the Models 212 5.6.1 The Balassa-Samuelson Model 212 5.6.2 The Dornbusch Overshooting Model 215 5.6.3 The Real Interest Differential Theory 219 5.6.4 Present-Value Models: Complete and Incomplete Markets 219 5.7 Conclusions 221 Appendix 5.A: Suggested Empirical Work for Econometric and Statistical Analysis 222 References 223 6 Asia: Financial Environment and Risks 226 Motivation 226 6.1 Introduction 226 6.2 Driving Engines of Economic Growth in Asia 228 6.2.1 International Trade 229 6.2.2 Exchange Rate 229 6.2.3 Foreign Investments Expansion 234 6.3 Financial Sector Development in Asia 235 6.3.1 Fast-Growing Banking Sector 237 6.3.2 Bond Market Development 239 6.3.3 Stock Market Development 242 6.4 Risks of Investing in Asia 245 6.4.1 Foreign Exchange Risk 245 6.4.2 Foreign Exchange Option 248 6.4.3 Portfolio with Asian Assets 249 6.5 Financial Development in China 251 6.5.1 Importance of the Banking Sector in China 252 6.5.2 State-Owned Enterprises 254 6.5.3 Challenges in Equity Markets in China 257 6.5.4 Stock Market Development in China 257 6.6 Finance in Japan 260 6.6.1 Banking Sector in Japan 260 6.6.2 Effectiveness of Corporate Governance 260 6.6.3 The Importance of Industry 261 6.6.4 Bond Market Volatility in Japan 263 6.7 Conclusion 265 References 266 7 Financial Currency Pricing, Swaps, Derivatives, and Complete Markets 269 Motivation 269 7.1 Introduction 269 7.2 Pricing Consumption Price Index and Foreign Exchange in Complete Financial Markets 272 7.3 A Generalized Consumption Price Index Foreign Exchange Pricing Model 276 7.4 Relative and Foreign Exchange Basket Price Tracking 278 7.4.1 Currency Basket Design 281 7.4.2 A Global Index Consumption Price Index Price Reversion and Foreign Exchange Prices 284 7.4.3 Global Index Reversion…”

“…In this course, you will cover an end to end architecture on AWS Big data ecosystem from importing data to S3 storage using Lambda, preparing data using Athena, connecting Athena and QuickSight SPICE and finally visualizing the data using QuickSight. …”

“…At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. …”

“…By the end of this course, you will be able to design and develop big data engineering projects. You will be able to create real-time stream processing applications with Apache Spark. …”

“…Sie erläutern auch neuere Entwicklungen wie NoSQL-Datenbanksysteme, spaltenorientierte Speicherungsformen und die Analyse von Big Data. Das Buch richtet sich vor allem an Schüler und Studenten außerhalb des Fachbereichs Informatik, die schnell und dennoch fundiert die Grundlagen zur Entwicklung und zum Einsatz von Datenbanken lernen wollen. …”

“…And it ponders big questions, such as how to build capacity, engage with the private sector, participate in the big data revolution, and foster strong governance and leadership throughout agriculture and nutrition…”

“…With focus public health informatics, global health, social media and big data analytics, pandemics preparedness, precision medicine and emergency medicine, in 2018 we achieved the widest topical outreach. …”

“…Additionally, you'll also learn advanced concepts relating to big data, machine learning, and the Internet of Things (IoT). …”

“…These are illustrated through a series of case studies in cloud computing, telecommunications, big data analytics, and smart cities. …”

“…Data management and integration is an integral part of this book that discusses options for implementing OLTP solutions using Azure SQL, Big Data solutions using Azure Data factory and Data Lake Storage, eventing solutions using stream analytics, and Event Hubs. …”

“…Toward the final chapters, we'll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems. Style and approach This book is packed with intu..…”

“…The program covers traditional ICPE topics such as performance modeling, prediction, optimization, monitoring, profiling, load testing, benchmarking, and runtime adaptation for fields such as cloud and high performance computing, big data, energy, and enterprise applications…”

“…Finally, you will also see how you can integrate your Java, Scala, Python, and Big Data applications such as Apache Spark and Pig with Elasticsearch, and add enhanced functionalities with custom plugins. …”