Оглавление
Michael G. Solomon. Blockchain Data Analytics For Dummies
Blockchain Data Analytics For Dummies® To view this book's Cheat Sheet, simply go to www.dummies.com and search for “Blockchain Data Analytics For Dummies Cheat Sheet” in the Search box. Table of Contents
List of Tables
List of Illustrations
Guide
Pages
Introduction
About This Book
Foolish Assumptions
Icons Used in This Book
Beyond the Book
Where to Go from Here
Intro to Analytics and Blockchain
Driving Business with Data and Analytics
Deriving Value from Data
Monetizing data
Exchanging data
Verifying data
Understanding and Satisfying Regulatory Requirements
Classifying individuals
Identifying criminals
Examining common privacy laws
Predicting Future Outcomes with Data
Classifying entities
Predicting behavior
Making decisions based on models
Changing Business Practices to Create Desired Outcomes
Defining the desired outcome
Building models for simulation
Aligning operations and assessing results
Digging into Blockchain Technology
Exploring the Blockchain Landscape
Managing ownership transfer
Doing more with blockchain
Understanding blockchain technology
Comparing blockchain to something you know
Using cryptography with blockchain
Achieving consensus among network nodes
Reviewing blockchain’s family tree
Introducing blockchain’s first generation
Adding blockchain features in the second generation
Scaling to the enterprise in blockchain’s third generation
Looking to the future
Fitting blockchain into today’s businesses
Finding a good fit
Integrating with legacy artifacts
Scaling to the enterprise
Understanding Primary Blockchain Types
Categorizing blockchain implementations
Opening blockchain to everyone
Limiting blockchain access
Combining the best of both worlds
Describing basic blockchain type features
Contrasting popular enterprise blockchain implementations
Aligning Blockchain Features with Business Requirements
Reviewing blockchain core features
Transferring value without trust
Reducing transaction costs by eliminating middlemen
Increasing efficiency through direct interaction
Maintaining a complete transaction history
Increasing resilience through replication
Providing transparency
Examining primary common business requirements
Matching blockchain features to business requirements
Examining Blockchain Use Cases
Managing physical items in cyberspace
Handling sensitive information
Conducting financial transactions
Identifying Blockchain Data with Value
Exploring Blockchain Data
Understanding what's stored in blockchain blocks
Recording transaction data
Dissecting the parts of a block
Decoding block data
Categorizing Common Data in a Blockchain
Serializing transaction data
Logging events on the blockchain
Storing value with smart contracts
Examining Types of Blockchain Data for Value
Exploring basic transaction data
Associating real-world meaning to events
Aligning Blockchain Data with Real-World Processes
Understanding smart contract functions
Assessing smart contract event logs
Ranking transaction and event data by its effect
Implementing Blockchain Analytics in Business
Aligning Analytics with Business Goals
Leveraging newly accessible decentralized tools
Monetizing data
Exchanging and integrating data effectively
Surveying Options for Your Analytics Lab
Installing the Blockchain Client
Installing the Test Blockchain
Installing the Testing Environment
Getting ready to install Truffle
Downloading and installing Truffle
Installing the IDE
Interacting with Blockchain Data
Exploring the Blockchain Analytics Ecosystem
Reviewing your blockchain lab
Identifying analytics client options
Choosing the best blockchain analytics client
Adding Anaconda and Web3.js to Your Lab
Verifying platform prerequisites
Checking the installed Python version
Installing Python (if needed)
Installing the Anaconda platform
Installing the Web3.py library
Setting up your blockchain analytics project
Writing a Python Script to Access a Blockchain
Interfacing with smart contracts
Finding a smart contract’s ABI
Building a Local Blockchain to Analyze
Connecting to your blockchain
Invoking smart contract functions
Fetching blockchain data
Fetching Blockchain Chain
Parsing Blockchain Data and Building the Analysis Dataset
Comparing On-Chain and External Analysis Options
Considering access speed
Comparing one-off versus repeated analysis
Assessing data completeness
Integrating External Data
Determining what data you need
Extending identities to off-chain data
Finding external data
Identifying Features
Describing how features affect outcomes
Comparing filtering and wrapping methods
Filtering features
Wrapping features
Building an Analysis Dataset
Connecting to multiple data sources
Building a cross-referenced dataset
Cleaning your data
Building Basic Blockchain Analysis Models
Identifying Related Data
Grouping data based on features (attributes)
Determining group membership
Discovering relationships among items
Making Predictions of Future Outcomes
Selecting features that affect outcome
Filtering features quickly
Wrapping feature selection for high accuracy
Beating the best guess
Building confidence
Analyzing Time-Series Data
Exploring growth and maturity
Identifying seasonal trends
Describing cycles of results
Leveraging Advanced Blockchain Analysis Models
Identifying Participation Incentive Mechanisms
Complying with mandates
Playing games with partners
Rewarding and punishing participants
Managing Deployment and Maintenance Costs
Lowering the cost of admission
Leveraging participation value
Aligning ROI with analytics currency
Collaborating to Create Better Models
Collecting data from a cohort
Building models collaboratively
Assessing model quality as a team
Analyzing and Visualizing Blockchain Analysis Data
Identifying Clustered and Related Data
Analyzing Data Clustering Using Popular Models
Delivering valuable knowledge with cluster analysis
Examining popular clustering techniques
Understanding k-means analysis
Evaluating model effectiveness with diagnostics
Implementing Blockchain Data Clustering Algorithms in Python
Discovering Association Rules in Data
Delivering valuable knowledge with association rules analysis
Describing the apriori association rules algorithm
Apriori step 1: Count the frequency of single-item itemsets
Apriori step 2: Prune itemsets based on support threshold
Apriori step 3: Count the frequency of two-item itemsets
Apriori step 4: Prune itemsets based on support (again)
Apriori step 5: Count the frequency of three-item itemsets
Evaluating model effectiveness with diagnostics
Determining When to Use Clustering and Association Rules
Classifying Blockchain Data
Analyzing Data Classification Using Popular Models
Delivering valuable knowledge with classification analysis
Examining popular classification techniques
Understanding how the decision tree algorithm works
Understanding how the naïve Bayes algorithm works
Evaluating model effectiveness with diagnostics
Implementing Blockchain Classification Algorithms in Python
Defining model input data requirements
Building your classification model dataset
Reading and cleaning input data
Splitting data into training and testing partitions
Developing your classification model code
Coding the decision tree model
Coding the naïve Bayes model
Determining When Classification Fits Your Analytics Needs
Predicting the Future with Regression
Analyzing Predictions and Relationships Using Popular Models
Delivering valuable knowledge with regression analysis
Examining popular regression techniques
Visualizing linear data
Visualizing categorical data
Describing how linear regression works
Describing how logistic regression works
Evaluating model effectiveness with diagnostics
Determining effectiveness of linear regression models
Determining effectiveness of logistic regression models
Implementing Regression Algorithms in Python
Defining model input data requirements
Building your regression model dataset
Developing your regression model code
Coding the linear regression model
Coding the logistic regression model
Determining When Regression Fits Your Analytics Needs
Analyzing Blockchain Data over Time
Analyzing Time Series Data Using Popular Models
Delivering valuable knowledge with time series analysis
Examining popular time series techniques
Visualizing time series results
Implementing Time Series Algorithms in Python
Defining model input data requirements
Developing your time series model code
Determining When Time Series Fits Your Analytics Needs
Implementing Blockchain Analysis Models
Writing Models from Scratch
Interacting with Blockchains
Connecting to a Blockchain
Connecting directly to a blockchain node
Using an application programming interface to interact with a blockchain
Letting a library do most of the work
Reading from a blockchain
Fetching blockchain state data
Parsing logs of events
Examining contract storage without the ABI
Updating previously read blockchain data
Examining Blockchain Client Languages and Approaches
Introducing popular blockchain client programming languages
Comparing popular language pros and cons
Deciding on the right language
Calling on Existing Frameworks
Benefitting from Standardization
Easing the burden of compliance
Avoiding inefficient code
Raising the bar on quality
Focusing on Analytics, Not Utilities
Avoiding feature bloat
Setting granular goals
Managing post-operational models
Leveraging the Efforts of Others
Deciding between make or buy
Scoping your testing efforts
Aligning personnel expertise with tasks
Using Third-Party Toolsets and Frameworks
Surveying Toolsets and Frameworks
Describing TensorFlow
Examining Keras
Looking at PyTorch
Supercharging PyTorch with fast.ai
Presenting Apache MXNet
Introducing Caffe
Describing Deeplearning4j
Comparing Toolsets and Frameworks
Putting It All Together
Assessing Your Analytics Needs
Describing the project's purpose
Defining the process
Taking inventory of resources
Choosing the Best Fit
Understanding personnel skills and affinity
Leveraging infrastructure
Integrating into organizational culture
Embracing iteration
Managing the Blockchain Project
The Part of Tens
Ten Tools for Developing Blockchain Analytics Models
Developing Analytics Models with Anaconda
Writing Code in Visual Studio Code
Prototyping Analytics Models with Jupyter
Developing Models in the R Language with RStudio
Interacting with Blockchain Data with web3.py
Extract Blockchain Data to a Database
Extracting blockchain data with EthereumDB
Storing blockchain data in a database using Ethereum-etl
Accessing Ethereum Networks at Scale with Infura
Analyzing Very Large Datasets in Python with Vaex
Examining Blockchain Data
Exploring Ethereum with Etherscan.io
Perusing multiple blockchains with Blockchain.com
Viewing cryptocurrency details with ColossusXT
Preserving Privacy in Blockchain Analytics with MADANA
Ten Tips for Visualizing Data
Checking the Landscape around You
Leveraging the Community
Making Friends with Network Visualizations
Recognizing Subjectivity
Using Scale, Text, and the Information You Need
Considering Frequent Updates for Volatile Blockchain Data
Getting Ready for Big Data
Protecting Privacy
Telling Your Story
Challenging Yourself!
Ten Uses for Blockchain Analytics
Accessing Public Financial Transaction Data
Connecting with the Internet of Things (IoT)
Ensuring Data and Document Authenticity
Controlling Secure Document Integrity
Tracking Supply Chain Items
Empowering Predictive Analytics
Analyzing Real-Time Data
Supercharging Business Strategy
Managing Data Sharing
Standardizing Collaboration Forms
Index. A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
Z
About the Author
Dedication
Author’s Acknowledgments
WILEY END USER LICENSE AGREEMENT
