Blockchain data analytics for dummies / Michael G. Solomon.

Format:

Book

Author/Creator:

Solomon, Michael G., author.

Series:

--For dummies

Language:

English

Subjects (All):

Blockchains (Databases).

Physical Description:

1 online resource (355 pages) : illustrations

Edition:

First edition.

Place of Publication:

Indianapolis, Indiana : John Wiley and Sons, [2020]

Summary:

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!

Contents:

Intro

Title Page

Copyright Page

Table of Contents

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

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

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 naïve Bayes algorithm works

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

Implementing Regression Algorithms in Python

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

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

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.

Notes:

Includes index

Description based on print version record.

Description based on publisher supplied metadata and other sources.

ISBN:

9781119651789

1119651786

9781119651758

1119651751

OCLC:

1197573364