About this course
Course Overview
This course reviews why blockchain is the future of data science. There are many decisions and issues that face the technical team and data leadership, and this class will enable participants to effectively make those decisions both offensively and defensively.
Target Audience
Technical Leaders, Data Scientist, and Analysts
Course Objectives
- 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
Course Content
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 and SaaS
- Use cases by organizational type
- SMBs
- Enterprises
- Government
- NGOs
- Use cases by organizational department
- Business Intelligence
- Marketing
- Customer Experience Management
- Procurement and 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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