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