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Course Overview
Is your data science strategy ready for the next disruption? As traditional models buckle under the weight of fragmented, untrusted data, blockchain is emerging as the infrastructure reshaping how data is collected, stored, and analyzed.
Blockchain and Data Science explores why blockchain represents the future of scalable, trustworthy data management and analysis. This course empowers technical teams and data leaders to make informed strategic decisions—both offensively and defensively—about adopting blockchain in their data science workflows.
Learn how blockchain addresses critical challenges in data integrity, access, completeness, and trustworthiness. Through hands-on examples, industry-specific use cases, and strategic planning models, participants will gain practical insight into implementing blockchain-based data science solutions.
Who Should Attend
An individual with prior knowledge or training in Data Analytics or Data Research will gain the most from this course.
Course Objectives
This Blockchain and Data Science course equips participants with the knowledge to evaluate, plan, and implement blockchain-based data science strategies. You'll learn how blockchain can enhance data integrity, trust, and accessibility while overcoming the limitations of centralized data systems.
By the end of the course, you’ll understand the technical and strategic dimensions of blockchain in data workflows, including data engineering and analytics applications, use case modeling, and how to launch a pilot blockchain data science initiative within your organization.
Course Outline
1. The Evolution of Data and Digital 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, and Wearables
- A brave new world of perfect information
2. The Coming Datapocalypse
- Where data science is headed
- 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
3. Blockchain as a Solution for Data Science
- Why blockchain is the solution for data science
- Blockchain as a data engineering solution
- Defined quantification, data completeness, and data trustworthiness
- Blockchain as a data analytics solution
- Data access and preparation
- Data scope and data totality improvements
- New data science frameworks
4. Use Cases
- 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
5. Strategies and Getting Started
- How to get started with your first blockchain-based data science project
- Offensive strategies: data maturity stage audit, prioritizing blockchain data science projects, build or buy blockchain data science solutions
- Defensive strategies: competitive intelligence and secondary research, macro metric correlations for blockchain data science models, game theory and “best response” actions