Blockchain data analytics for dummies
Book - 2020
Blockchain is about to upend the world of data analytics just as it did financial record-keeping. Here's what you need to become an early adopted of blockchain as a big-data tool! Explore how blockchains store data and learn how this rich new source of data can enhance predictive analytics and real-time data analysis. You'll also find out how blockchains can help you manage your data and keep shared data more secure. Learn to implement blockchain analysis models, use third-party toolsets, assess your analysis needs, and more. -- From publisher's description.
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- Subjects
- Genres
- Handbooks and manuals
- Published
-
Hoboken, New Jersey :
John Wiley & Sons
[2020]
- Language
- English
- Main Author
- Physical Description
- xiii, 330 pages : illustrations ; 24 cm
- Bibliography
- Includes index.
- ISBN
- 9781119651772
- Introduction
- About This Book
- Foolish Assumptions
- Icons Used in This Book
- Beyond the Book
- Where to Go from Here
- Part 1. Intro to Analytics and Blockchain
- Chapter 1. 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
- Chapter 2. Digging into Blockchain Technology
- Exploring the Blockchain Landscape
- Managing ownership transfer
- Doing more with blockchain
- Understanding blockchain technology
- Reviewing blockchain's family tree
- Fitting blockchain into today's businesses
- Understanding Primary Blockchain Types
- Categorizing blockchain implementations
- Describing basic blockchain type features
- Contrasting popular enterprise blockchain implementations
- Aligning Blockchain Features with Business Requirements
- Reviewing blockchain core features
- 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
- Chapter 3. 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
- Chapter 4. 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
- Chapter 5. Interacting with Blockchain Data
- Exploring the Blockchain Analytics Ecosystem
- Reviewing your blockchain lab
- Identifying analytics client options 8i
- Choosing the best blockchain analytics client
- Adding Anaconda and Web3.js to Your Lab
- Verifying platform prerequisites
- 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
- Part 2. Fetching Blockchain Chain
- Chapter 6. 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
- Building an Analysis Dataset
- Connecting to multiple data sources
- Building a cross-referenced dataset
- Cleaning your data
- Chapter 7. 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
- Beating the best guess
- Building confidence
- Analyzing Time-Series Data
- Exploring growth and maturity
- Identifying seasonal trends
- Describing cycles of results
- Chapter 8. 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
- Part 3. Analyzing and Visualizing Blockchain Analysis Data
- Chapter 9. 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
- Evaluating model effectiveness with diagnostics
- Determining When to Use Clustering and Association Rules
- Chapter 10. 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 naive Bayes algorithm works
- Evaluating model effectiveness with diagnostics
- Implementing Blockchain Classification Algorithms in Python
- Defining model input data requirements
- Building your classification model dataset
- Developing your classification model code
- Determining When Classification Fits Your Analytics Needs
- Chapter 11. Predicting the Future with Regression
- Analyzing Predictions and Relationships Using Popular Models
- Delivering valuable knowledge with regression analysis
- Examining popular regression techniques
- Describing how linear regression works
- Describing how logistic regression works
- Evaluating model effectiveness with diagnostics
- Implementing Regression Algorithms in Python
- Defining model input data requirements
- Building your regression model dataset
- Developing your regression model code
- Determining When Regression Fits Your Analytics Needs
- Chapter 12. 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
- Part 4. Implementing Blockchain Analysis Models
- Chapter 13. Writing Models from Scratch
- Interacting with Blockchains
- Connecting to a Blockchain
- Using an application programming interface to interact with a blockchain
- Reading from a blockchain
- 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
- Chapter 14. 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
- Chapter 15. 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
- Chapter 16. 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
- Part 5. The Part of Tens
- Chapter 17. Ten Tools for Developing Blockchain Analytics Models 28i
- 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 Biockchain.com
- Viewing cryptocurrency details with ColossusXT
- Preserving Privacy in Blockchain Analytics with MADANA
- Chapter 18. 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!
- Chapter 19. 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