Blockchain Data Analytics For Dummies

Michael G. Solomon, PhD, is a professor at the University of the Cumberlands who specializes in courses on blockchain and distributed computing systems as well as computer security. He holds numerous security and project management certifications and has written several books on security and project management, including Ethereum For Dummies.

• Introduction 1About This Book 1Foolish Assumptions 2Icons Used in This Book 2Beyond the Book 2Where to Go from Here 3Part 1: Intro to Analytics and Blockchain 5Chapter 1: Driving Business with Data and Analytics 7Deriving Value from Data 8Monetizing data 8Exchanging data 9Verifying data 10Understanding and Satisfying Regulatory Requirements 11Classifying individuals 11Identifying criminals 11Examining common privacy laws 12Predicting Future Outcomes with Data 13Classifying entities 13Predicting behavior 14Making decisions based on models 16Changing Business Practices to Create Desired Outcomes 16Defining the desired outcome 17Building models for simulation 17Aligning operations and assessing results 18Chapter 2: Digging into Blockchain Technology 19Exploring the Blockchain Landscape 20Managing ownership transfer 20Doing more with blockchain 21Understanding blockchain technology 21Reviewing blockchain’s family tree 22Fitting blockchain into today’s businesses 25Understanding Primary Blockchain Types 27Categorizing blockchain implementations 27Describing basic blockchain type features 29Contrasting popular enterprise blockchain implementations 30Aligning Blockchain Features with Business Requirements 31Reviewing blockchain core features 31Examining primary common business requirements 33Matching blockchain features to business requirements 34Examining Blockchain Use Cases 35Managing physical items in cyberspace 35Handling sensitive information 36Conducting financial transactions 37Chapter 3: Identifying Blockchain Data with Value 39Exploring Blockchain Data 40Understanding what’s stored in blockchain blocks 40Recording transaction data 41Dissecting the parts of a block 43Decoding block data 47Categorizing Common Data in a Blockchain 49Serializing transaction data 49Logging events on the blockchain 50Storing value with smart contracts 52Examining Types of Blockchain Data for Value 52Exploring basic transaction data 53Associating real-world meaning to events 53Aligning Blockchain Data with Real-World Processes 54Understanding smart contract functions 55Assessing smart contract event logs 55Ranking transaction and event data by its effect 55Chapter 4: Implementing Blockchain Analytics in Business 57Aligning Analytics with Business Goals 58Leveraging newly accessible decentralized tools 58Monetizing data 59Exchanging and integrating data effectively 59Surveying Options for Your Analytics Lab 60Installing the Blockchain Client 61Installing the Test Blockchain 65Installing the Testing Environment 68Getting ready to install Truffle 69Downloading and installing Truffle 72Installing the IDE 74Chapter 5: Interacting with Blockchain Data 79Exploring the Blockchain Analytics Ecosystem 80Reviewing your blockchain lab 80Identifying analytics client options 81Choosing the best blockchain analytics client 83Adding Anaconda and Web3.js to Your Lab 84Verifying platform prerequisites 84Installing the Anaconda platform 86Installing the Web3.py library 89Setting up your blockchain analytics project 90Writing a Python Script to Access a Blockchain 92Interfacing with smart contracts 93Finding a smart contract’s ABI 94Building a Local Blockchain to Analyze 100Connecting to your blockchain 101Invoking smart contract functions 101Fetching blockchain data 102Part 2: Fetching Blockchain Chain 105Chapter 6: Parsing Blockchain Data and Building the Analysis Dataset 107Comparing On-Chain and External Analysis Options 108Considering access speed 108Comparing one-off versus repeated analysis 109Assessing data completeness 110Integrating External Data 111Determining what data you need 112Extending identities to off-chain data 113Finding external data 114Identifying Features 115Describing how features affect outcomes 116Comparing filtering and wrapping methods 116Building an Analysis Dataset 117Connecting to multiple data sources 118Building a cross-referenced dataset 118Cleaning your data 118Chapter 7: Building Basic Blockchain Analysis Models 121Identifying Related Data 122Grouping data based on features (attributes) 123Determining group membership 126Discovering relationships among items 129Making Predictions of Future Outcomes 130Selecting features that affect outcome 131Beating the best guess 133Building confidence 134Analyzing Time-Series Data 135Exploring growth and maturity 137Identifying seasonal trends 138Describing cycles of results 138Chapter 8: Leveraging Advanced Blockchain Analysis Models 139Identifying Participation Incentive Mechanisms 140Complying with mandates 141Playing games with partners 141Rewarding and punishing participants 142Managing Deployment and Maintenance Costs 143Lowering the cost of admission 143Leveraging participation value 145Aligning ROI with analytics currency 146Collaborating to Create Better Models 147Collecting data from a cohort 148Building models collaboratively 148Assessing model quality as a team 149Part 3: Analyzing and Visualizing Blockchain Analysis Data 151Chapter 9: Identifying Clustered and Related Data 153Analyzing Data Clustering Using Popular Models 154Delivering valuable knowledge with cluster analysis 154Examining popular clustering techniques 155Understanding k-means analysis 155Evaluating model effectiveness with diagnostics 160Implementing Blockchain Data Clustering Algorithms in Python 160Discovering Association Rules in Data 163Delivering valuable knowledge with association rules analysis 163Describing the apriori association rules algorithm 164Evaluating model effectiveness with diagnostics 167Determining When to Use Clustering and Association Rules 168Chapter 10: Classifying Blockchain Data 171Analyzing Data Classification Using Popular Models 172Delivering valuable knowledge with classification analysis 172Examining popular classification techniques 173Understanding how the decision tree algorithm works 173Understanding how the naïve Bayes algorithm works 176Evaluating model effectiveness with diagnostics 178Implementing Blockchain Classification Algorithms in Python 179Defining model input data requirements 179Building your classification model dataset 181Developing your classification model code 184Determining When Classification Fits Your Analytics Needs 188Chapter 11: Predicting the Future with Regression 189Analyzing Predictions and Relationships Using Popular Models 190Delivering valuable knowledge with regression analysis 190Examining popular regression techniques 191Describing how linear regression works 195Describing how logistic regression works 198Evaluating model effectiveness with diagnostics 201Implementing Regression Algorithms in Python 203Defining model input data requirements 203Building your regression model dataset 203Developing your regression model code 204Determining When Regression Fits Your Analytics Needs 207Chapter 12: Analyzing Blockchain Data over Time 209Analyzing Time Series Data Using Popular Models 210Delivering valuable knowledge with time series analysis 211Examining popular time series techniques 211Visualizing time series results 214Implementing Time Series Algorithms in Python 216Defining model input data requirements 217Developing your time series model code 219Determining When Time Series Fits Your Analytics Needs 221Part 4: Implementing Blockchain Analysis Models 223Chapter 13: Writing Models from Scratch 225Interacting with Blockchains 226Connecting to a Blockchain 226Using an application programming interface to interact with a blockchain 228Reading from a blockchain 230Updating previously read blockchain data 234Examining Blockchain Client Languages and Approaches 236Introducing popular blockchain client programming languages 237Comparing popular language pros and cons 238Deciding on the right language 238Chapter 14: Calling on Existing Frameworks 239Benefitting from Standardization 240Easing the burden of compliance 240Avoiding inefficient code 242Raising the bar on quality 244Focusing on Analytics, Not Utilities 245Avoiding feature bloat 245Setting granular goals 246Managing post-operational models 247Leveraging the Efforts of Others 248Deciding between make or buy 248Scoping your testing efforts 249Aligning personnel expertise with tasks 250Chapter 15: Using Third-Party Toolsets and Frameworks 251Surveying Toolsets and Frameworks 252Describing TensorFlow 253Examining Keras 255Looking at PyTorch 256Supercharging PyTorch with fast.ai 258Presenting Apache MXNet 260Introducing Caffe 261Describing Deeplearning4j 262Comparing Toolsets and Frameworks 264Chapter 16: Putting It All Together 267Assessing Your Analytics Needs 268Describing the project’s purpose 268Defining the process 270Taking inventory of resources 271Choosing the Best Fit 273Understanding personnel skills and affinity 273Leveraging infrastructure 275Integrating into organizational culture 276Embracing iteration 276Managing the Blockchain Project 277Part 5: The Part of Tens 279Chapter 17: Ten Tools for Developing Blockchain Analytics Models 281Developing Analytics Models with Anaconda 282Writing Code in Visual Studio Code 283Prototyping Analytics Models with Jupyter 284Developing Models in the R Language with RStudio 285Interacting with Blockchain Data with web3.py 287Extract Blockchain Data to a Database 288Extracting blockchain data with EthereumDB 288Storing blockchain data in a database using Ethereum-etl 288Accessing Ethereum Networks at Scale with Infura 289Analyzing Very Large Datasets in Python with Vaex 290Examining Blockchain Data 291Exploring Ethereum with Etherscan.io 291Perusing multiple blockchains with Blockchain.com 292Viewing cryptocurrency details with ColossusXT 293Preserving Privacy in Blockchain Analytics with MADANA 293Chapter 18: Ten Tips for Visualizing Data 295Checking the Landscape around You 296Leveraging the Community 297Making Friends with Network Visualizations 298Recognizing Subjectivity 299Using Scale, Text, and the Information You Need 300Considering Frequent Updates for Volatile Blockchain Data 301Getting Ready for Big Data 302Protecting Privacy 302Telling Your Story 303Challenging Yourself! 303Chapter 19: Ten Uses for Blockchain Analytics 305Accessing Public Financial Transaction Data 306Connecting with the Internet of Things (IoT) 307Ensuring Data and Document Authenticity 308Controlling Secure Document Integrity 308Tracking Supply Chain Items 310Empowering Predictive Analytics 310Analyzing Real-Time Data 311Supercharging Business Strategy 312Managing Data Sharing 312Standardizing Collaboration Forms 312Index 315