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