Blockchain Data Analytics for Dummies:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ
John Wiley & Sons, Inc.
[2020]
|
| Schriftenreihe: | Learning made easy
|
| Schlagworte: | |
| Online-Zugang: | HWR01 |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (xiii, 330 Seiten) Illustrationen, Diagramme |
| ISBN: | 9781119651789 |
Internformat
MARC
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| 100 | 1 | |a Solomon, Michael G. |d 1963- |e Verfasser |0 (DE-588)1044663928 |4 aut | |
| 245 | 1 | 0 | |a Blockchain Data Analytics for Dummies |c by Michael Solomon |
| 264 | 1 | |a Hoboken, NJ |b John Wiley & Sons, Inc. |c [2020] | |
| 264 | 4 | |c ©2020 | |
| 300 | |a 1 Online-Ressource (xiii, 330 Seiten) |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a Learning made easy | |
| 500 | |a Description based on publisher supplied metadata and other sources | ||
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 -- 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 -- 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a Extracting blockchain data with EthereumDB. | |
| 650 | 4 | |a Blockchains (Databases) | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Solomon, Michael G. |t Blockchain Data Analytics for Dummies |d Newark : John Wiley & Sons, Incorporated,c2020 |z 9781119651772 |
| 912 | |a ZDB-30-PQE | ||
| 999 | |a oai:aleph.bib-bvb.de:BVB01-032844101 | ||
| 966 | e | |u https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=6336506 |l HWR01 |p ZDB-30-PQE |x Aggregator |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1804182734725185536 |
|---|---|
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Solomon, Michael G. 1963- |
| author_GND | (DE-588)1044663928 |
| author_facet | Solomon, Michael G. 1963- |
| author_role | aut |
| author_sort | Solomon, Michael G. 1963- |
| author_variant | m g s mg mgs |
| building | Verbundindex |
| bvnumber | BV047441949 |
| collection | ZDB-30-PQE |
| 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 -- 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 -- 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 -- 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 -- 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. |
| ctrlnum | (ZDB-30-PQE)EBC6336506 (ZDB-30-PAD)EBC6336506 (ZDB-89-EBL)EBL6336506 (OCoLC)1197573364 (DE-599)BVBBV047441949 |
| dewey-full | 005.824 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.824 |
| dewey-search | 005.824 |
| dewey-sort | 15.824 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| format | Electronic eBook |
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| id | DE-604.BV047441949 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T18:01:23Z |
| indexdate | 2024-07-10T09:12:16Z |
| institution | BVB |
| isbn | 9781119651789 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032844101 |
| oclc_num | 1197573364 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (xiii, 330 Seiten) Illustrationen, Diagramme |
| psigel | ZDB-30-PQE |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | John Wiley & Sons, Inc. |
| record_format | marc |
| series2 | Learning made easy |
| spelling | Solomon, Michael G. 1963- Verfasser (DE-588)1044663928 aut Blockchain Data Analytics for Dummies by Michael Solomon Hoboken, NJ John Wiley & Sons, Inc. [2020] ©2020 1 Online-Ressource (xiii, 330 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier Learning made easy Description based on publisher supplied metadata and other sources 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 -- 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 -- 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 -- 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 -- 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. Blockchains (Databases) Erscheint auch als Druck-Ausgabe Solomon, Michael G. Blockchain Data Analytics for Dummies Newark : John Wiley & Sons, Incorporated,c2020 9781119651772 |
| spellingShingle | Solomon, Michael G. 1963- Blockchain Data Analytics for Dummies 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 -- 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 -- 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 -- 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 -- 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. Blockchains (Databases) |
| title | Blockchain Data Analytics for Dummies |
| title_auth | Blockchain Data Analytics for Dummies |
| title_exact_search | Blockchain Data Analytics for Dummies |
| title_exact_search_txtP | Blockchain Data Analytics for Dummies |
| title_full | Blockchain Data Analytics for Dummies by Michael Solomon |
| title_fullStr | Blockchain Data Analytics for Dummies by Michael Solomon |
| title_full_unstemmed | Blockchain Data Analytics for Dummies by Michael Solomon |
| title_short | Blockchain Data Analytics for Dummies |
| title_sort | blockchain data analytics for dummies |
| topic | Blockchains (Databases) |
| topic_facet | Blockchains (Databases) |
| work_keys_str_mv | AT solomonmichaelg blockchaindataanalyticsfordummies |