Blockchain for Data Scientists

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Course Overview
Duration: 3 Days
Format: Instructor-led Training
After Class Access: 1 year access to recorded training
What You’ll Learn
- How we got to now: the digital data transformation
- Where data science is headed: the coming datapocalypse
- Why blockchain is the solution for data science
- Examples of successful blockchain data science projects
- How to get started with your first blockchain-based data science project
Audience
This course is intended for technical leaders who make technical decisions about their data science architecture, analytical environments, and organizational insights delivery.
Outline
- How we got to now: the digital data transformation
- The origin of data-driven approaches in government, academia and business
- Progress in data factor markets
- Data connectivity
- Data storage
- Data processing
- Quantification of everything
- IoT
- Smart Cities
- Wearables
- A brave new world of perfect information
- Where data science is headed: the coming datapocalypse
- The dependency of data science on the theory of variance
- Why data centralization will kill traditional data science
- Why most organizations are ill prepared for the coming wave of data
- Why most organizations are totally unprepared for blockchain data
- Why blockchain is the solution for data science
- Blockchain as a data engineering solution
- Defined quantification
- Data completeness
- Data trustworthiness
- Blockchain as a data analytics solution
- Data access and preparation
- Data scope and data totality improvements
- New data science frameworks
- Examples of successful blockchain data science projects
- Use cases by vertical
- Finance
- Ecommerce
- Healthcare
- Fintech & SaaS
- Use cases by organizational type
- SMBs
- Enterprises
- Government
- NGOs
- Use cases by organizational department
- Business Intelligence
- Marketing
- Customer Experience Management
- Procurement & Fulfillment
- How to get started with your first blockchain-based data science project
- Offensive strategies for adopting blockchain into data science workflows
- Data maturity stage audit
- Prioritizing blockchain data science projects
- Build or buy blockchain data science solutions
- Defensive strategies for adopting blockchain into data science workflows
- Competitive intelligence and secondary research
- Macro metric correlations for blockchain data science models
- Game theory and “best response” actions
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