Blockchain Bootcamp for Data Scientists

Cieľová skupina

Táto časť nie je lokalizovaná

This course is intended for technical leaders who make technical decisions about their data science architecture, analytical environments, and organizational insights delivery.

Štruktúra kurzu

Táto časť nie je lokalizovaná

a

• 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