Chapter Two: Where Data Science is Headed - The Coming Datapocalypse and Blockchain
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Chapter Three: Why Blockchain is the Solution for Data Science
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Chapter Four: Example of Successful Blockchain Data Science Projects
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Chapter Five: Everything you Need to Start your First Blockchain-Based Data Science Project
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Chapter Six: The Importance of Project Management in Blockchain Data Science
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Chapter Seven: The Blockchain Data Science Process (Step-by-Step)
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Chapter Eight: Code Walkthrough: KYC/AML with the Ethereum Blockchain and Public Data
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Next Steps
How to Navigate this Course
A Message from the Instructor: Luciano Pesci, PhD
Slides
The Legal Stuff...
The origin of data-driven approaches in government, academia and business
Progress in data factor markets (data connectivity, storage and processing)
Quantification of everything (IoT, smart cities and wearables)
A brave new world of perfect information
Most organizations are unprepared for the coming wave of data
Dependency of data science on data centralization
Organizations lack a vision for blockchain data because they don’t understand it
The foundations of a data science team
Building a successful blockchain data driven culture
Blockchain as a data engineering solution
Quantification through consensus
Why blockchain data is more trustworthy
How blockchain provides better data completeness
The unique nature of continuity in blockchain data
Data accessibility and blockchain
Blockchain data preparation
Understanding measurement, origin, scope and totality in blockchain data
New frameworks to guide actionable insights through blockchain analytics
Use cases by organizational type (SMBs, enterprises, government and NGOs)
Use cases by vertical (finance, ecommerce, healthcare, and fintech)
Use cases by organizational department (BI, marketing, CX and procurement/fulfillment)
Adopting blockchain into data science workflows
Data governance with blockchain
Data maturity stage audit
Prioritizing different blockchain data science projects
Build or buy blockchain data science analysis
Understanding the objectives and key results (OKRs)
Defining your SMART goals
Identifying and tracking your key performance indicators
The blockchain analytics treasure map
Visualizing the system with whiteboards and flowcharts
The blockchain data science toolkit
Standing up a data science environment in the cloud
Identity and access management
Extracting, transforming and loading the Ethereum blockchain data
Exploratory blockchain data analysis
Descriptive blockchain data analysis
Graph database analysis
Predictive modeling - KYC / AML (looking ahead to the code walkthrough)
Explaining simply: storytelling with interactive visualizations
Getting the project into production and monitoring it
The critical importance of continued testing and validation
Understanding the objectives and key results (OKRs)
Defining your SMART goals
Data Governance: Where is the data?
Data Governance: What does the data look like?
Data Access: How can we get access to the data?
ETL: How are we going to pull the data locally?
EDA: Manually inspecting the data
EDA: Exploratory data analysis
Descriptives: Descriptive analytics
Graph Database Technologies: Build or buy?
ETL: Importing the data into OrientDB
EDA: Orientdb manual inspection
EDA: Graph analysis
KYC: Next steps
KYC: How this impacts future ventures
Closing remarks
Contacting Blockchain Training Alliance
Course Evaluation
