Blockchain data analytics for dummies

Michael Solomon, 1963-

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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2nd Floor 005.824/Solomon Due Mar 21, 2024
Subjects
Genres
Handbooks and manuals
Published
Hoboken, New Jersey : John Wiley & Sons [2020]
Language
English
Main Author
Michael Solomon, 1963- (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