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Blockchain Data Analytics For Dummies

Author Michael G. Solomon
Release Date: 2020/10/01
ISBN: 9781119651772
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Get ahead of the curve—learn about big data on the blockchain

Blockchain came to prominence as the disruptive technology that made cryptocurrencies work. Now, data pros are using blockchain technology for faster real-time analysis, better data security, and more accurate predictions. Blockchain Data Analytics For Dummies is your quick-start guide to harnessing the potential of blockchain.

Inside this book, technologists, executives, and data managers will find information and inspiration to adopt blockchain as a big data tool. Blockchain expert Michael G. Solomon shares his insight on what the blockchain is and how this new tech is poised to disrupt data. Set your organization on the cutting edge of analytics, before your competitors get there!

  • Learn how blockchain technologies work and how they can integrate with big data
  • Discover the power and potential of blockchain analytics
  • Establish data models and quickly mine for insights and results
  • Create data visualizations from blockchain analysis

Discover how blockchains are disrupting the data world with this exciting title in the trusted For Dummies line!

Table of Contents

  1. Cover
  2. Introduction
    1. About This Book
    2. Foolish Assumptions
    3. Icons Used in This Book
    4. Beyond the Book
    5. Where to Go from Here
  3. Part 1: Intro to Analytics and Blockchain
    1. Chapter 1: Driving Business with Data and Analytics
    2. Deriving Value from Data
    3. Understanding and Satisfying Regulatory Requirements
    4. Predicting Future Outcomes with Data
    5. Changing Business Practices to Create Desired Outcomes
    6. Chapter 2: Digging into Blockchain Technology
    7. Exploring the Blockchain Landscape
    8. Understanding Primary Blockchain Types
    9. Aligning Blockchain Features with Business Requirements
    10. Examining Blockchain Use Cases
    11. Chapter 3: Identifying Blockchain Data with Value
    12. Exploring Blockchain Data
    13. Categorizing Common Data in a Blockchain
    14. Examining Types of Blockchain Data for Value
    15. Aligning Blockchain Data with Real-World Processes
    16. Chapter 4: Implementing Blockchain Analytics in Business
    17. Aligning Analytics with Business Goals
    18. Surveying Options for Your Analytics Lab
    19. Installing the Blockchain Client
    20. Installing the Test Blockchain
    21. Installing the Testing Environment
    22. Installing the IDE
    23. Chapter 5: Interacting with Blockchain Data
    24. Exploring the Blockchain Analytics Ecosystem
    25. Adding Anaconda and Web3.js to Your Lab
    26. Writing a Python Script to Access a Blockchain
    27. Building a Local Blockchain to Analyze
  4. Part 2: Fetching Blockchain Chain
    1. Chapter 6: Parsing Blockchain Data and Building the Analysis Dataset
    2. Comparing On-Chain and External Analysis Options
    3. Integrating External Data
    4. Identifying Features
    5. Building an Analysis Dataset
    6. Chapter 7: Building Basic Blockchain Analysis Models
    7. Identifying Related Data
    8. Making Predictions of Future Outcomes
    9. Analyzing Time-Series Data
    10. Chapter 8: Leveraging Advanced Blockchain Analysis Models
    11. Identifying Participation Incentive Mechanisms
    12. Managing Deployment and Maintenance Costs
    13. Collaborating to Create Better Models
  5. Part 3: Analyzing and Visualizing Blockchain Analysis Data
    1. Chapter 9: Identifying Clustered and Related Data
    2. Analyzing Data Clustering Using Popular Models
    3. Implementing Blockchain Data Clustering Algorithms in Python
    4. Discovering Association Rules in Data
    5. Determining When to Use Clustering and Association Rules
    6. Chapter 10: Classifying Blockchain Data
    7. Analyzing Data Classification Using Popular Models
    8. Implementing Blockchain Classification Algorithms in Python
    9. Determining When Classification Fits Your Analytics Needs
    10. Chapter 11: Predicting the Future with Regression
    11. Analyzing Predictions and Relationships Using Popular Models
    12. Implementing Regression Algorithms in Python
    13. Determining When Regression Fits Your Analytics Needs
    14. Chapter 12: Analyzing Blockchain Data over Time
    15. Analyzing Time Series Data Using Popular Models
    16. Implementing Time Series Algorithms in Python
    17. Determining When Time Series Fits Your Analytics Needs
  6. Part 4: Implementing Blockchain Analysis Models
    1. Chapter 13: Writing Models from Scratch
    2. Interacting with Blockchains
    3. Connecting to a Blockchain
    4. Examining Blockchain Client Languages and Approaches
    5. Chapter 14: Calling on Existing Frameworks
    6. Benefitting from Standardization
    7. Focusing on Analytics, Not Utilities
    8. Leveraging the Efforts of Others
    9. Chapter 15: Using Third-Party Toolsets and Frameworks
    10. Surveying Toolsets and Frameworks
    11. Comparing Toolsets and Frameworks
    12. Chapter 16: Putting It All Together
    13. Assessing Your Analytics Needs
    14. Choosing the Best Fit
    15. Managing the Blockchain Project
  7. Part 5: The Part of Tens
    1. Chapter 17: Ten Tools for Developing Blockchain Analytics Models
    2. Developing Analytics Models with Anaconda
    3. Writing Code in Visual Studio Code
    4. Prototyping Analytics Models with Jupyter
    5. Developing Models in the R Language with RStudio
    6. Interacting with Blockchain Data with web3.py
    7. Extract Blockchain Data to a Database
    8. Accessing Ethereum Networks at Scale with Infura
    9. Analyzing Very Large Datasets in Python with Vaex
    10. Examining Blockchain Data
    11. Preserving Privacy in Blockchain Analytics with MADANA
    12. Chapter 18: Ten Tips for Visualizing Data
    13. Checking the Landscape around You
    14. Leveraging the Community
    15. Making Friends with Network Visualizations
    16. Recognizing Subjectivity
    17. Using Scale, Text, and the Information You Need
    18. Considering Frequent Updates for Volatile Blockchain Data
    19. Getting Ready for Big Data
    20. Protecting Privacy
    21. Telling Your Story
    22. Challenging Yourself!
    23. Chapter 19: Ten Uses for Blockchain Analytics
    24. Accessing Public Financial Transaction Data
    25. Connecting with the Internet of Things (IoT)
    26. Ensuring Data and Document Authenticity
    27. Controlling Secure Document Integrity
    28. Tracking Supply Chain Items
    29. Empowering Predictive Analytics
    30. Analyzing Real-Time Data
    31. Supercharging Business Strategy
    32. Managing Data Sharing
    33. Standardizing Collaboration Forms
  8. Index
  9. About the Author
  10. Advertisement Page
  11. Connect with Dummies
  12. End User License Agreement
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