Learn about a 100% online program to master Blockchain technologies and ensure security in cyberspace” 

Technological development and advances in the web have radically transformed the way in which large volumes of data are managed and analyzed, while technologies such as the Blockchain have redefined security and information transfer. This impact is directly reflected in the growing demand from companies for specialists who can combine both disciplines to increase their productivity and protect against cyber threats. In this context, high-level preparation in these areas becomes essential, which is why TECH has designed this complete academic program.

With this approach, the syllabus covers the essential concepts of Big Data and Blockchain, exploring their practical applications in data collection, analysis and protection. It also delves into the most advanced techniques of secure value transfer and information management, taking the student from theory to implementation in real scenarios. This learning includes not only the technical fundamentals, but also the strategic skills needed to lead projects in a highly competitive digital environment. A comprehensive approach that allows professionals not only to keep up to date, but also to stand out in a constantly evolving market.

In addition, this program is developed in a 100% online format, which eliminates the need to travel or comply with rigid schedules. In this way, students have the freedom to organize their own learning pace, allowing them to combine their studies with other daily responsibilities. This flexible modality guarantees that each participant can make the most of the educational experience, adapting it to their personal and professional needs.

You will delve into blockchain configuration and key parameters for PoA and PoW, as well as Besu securitization" 

This Advanced master’s degree in Big Data and Blockchain contains the most complete and up-to-date educational program on the market. The most important features include:

• The development of case studies presented by experts in Big Data and Blockchain
• The graphic, schematic, and practical contents with which they are created, provide scientific and practical information on the disciplines that are essential for professional practice
• Practical exercises where the self-assessment process can be carried out to improve learning
• Its special emphasis on innovative methodologies in the direction of Big Data and Blockchain Theoretical lessons, questions to the expert, discussion forums on controversial topics and individual reflection papers
• Content that is accessible from any fixed or portable device with an Internet connection

Take advantage of the wide variety of practical resources in this program to consolidate your theoretical knowledge and apply it in real situations in the professional environment” 

Its teaching staff includes professionals from the field of computer science, who bring to this program the experience of their work, as well as renowned specialists from reference societies and prestigious universities.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide an immersive learning experience designed to prepare for real-life situations.

This program is designed around Problem-Based Learning, whereby the student must try to solve the different professional practice situations that arise throughout the program. For this purpose, the professional will be assisted by an innovative interactive video system created by renowned and experienced experts.

Enjoy the flexibility of a 100% online program that allows you to study from anywhere and on the schedule that best suits your needs"

TECH uses the most innovative educational methodology in the industry, designed to maximize learning in an effective and dynamic way"

The materials that make up this program have been developed by a team of Big Data and Blockchain experts with extensive experience in the implementation of these technologies in business environments. Thanks to this approach, the curriculum will not only delve into the main data analysis technologies and Blockchain systems, but will also address key aspects such as information security, cryptography and large-scale database management. Graduates will be able to identify specific opportunities and design solutions tailored to current market needs. In addition, the syllabus includes innovative content that fosters the development of disruptive projects in sectors such as logistics, marketing, finance and public administration. 

You will master Big Data and Blockchain technologies to ensure security in cyberspace and boost business competitiveness” 

Module 1. Visual Analytics in the Social and Technological Context

1.1. Technological Waves in Different Societies. Towards a  Data Society
1.2. Globalization. Geopolitical and Social World Context
1.3. VUCA Environment. Always Living in the Past
1.4. Understanding New Technologies: 5G and IoT
1.5. Understanding the New 5G, IoT, Cloud and Edge Computing Technologies
1.6. Critical Thinking in Visual Analytics
1.7. The Know-Mads. Nomads Among Data
1.8. Learning to Engage in Visual Analytics
1.9. Anticipation Theories Applied to Visual Analytics
1.10. The New Business Environment. Digital Transformation

Module 2. Data Analysis and Interpretation

2.1. Introduction to Statistics
2.2. Measures Applicable to the Processing of Information
2.3. Statistical Correlation
2.4. Theory of Conditional Probability
2.5. Random Variable and Probability Distribution
2.6. Bayesian Inference
2.7. Sample Theory
2.8. Confidence Intervals
2.9. Hypothesis Testing
2.10. Regression Analysis

Module 3. Data Analysis and Artificial Intelligence Techniques

3.1. Predictive Analytics
3.2. Evaluation Techniques and Model Selection
3.3. Lineal Optimization Techniques
3.4. Montecarlo Simulations
3.5. Scenario Analysis
3.6. Machine Learning Techniques
3.7. Web Analytics
3.8. Text Mining Techniques
3.9. Methods of Natural Language Processing (NLP)
3.10. Social Network Analytics

Module 4. Data Analysis Tools

4.1. Data Science R Environment
4.2. Data Science Python Environment
4.3. Static and Statistical Graphs
4.4. Data Processing in Different Formats and Different Sources
4.5. Data Cleaning and Preparation
4.6. Exploratory Studies
4.7. Decision Trees
4.8. Classification and Association Rules
4.9. Neural Networks
4.10. Deep Learning

Module 5. Database Management and Data Parallelization Systems

5.1. Conventional Databases
5.2. Non-Conventional Databases
5.3. Cloud Computing: Distributed Data Management
5.4. Tools for the Ingestion of Large Volumes of Data
5.5. Types of Parallels
5.6. Data Processing in Streaming and Real Time
5.7. Parallel Processing: Hadoop
5.8. Parallel Processing: Spark
5.9. Apache Kafka

5.9.1. Introduction to Apache Kafka
5.9.2. Architecture
5.9.3. Data Structure
5.9.4. Kafka APIs
5.9.5. Case Studies

5.10. Cloudera Impala

Module 6. Data-Driven Soft Skills in Strategic Management in Visual Analytics

6.1. Drive Profile for Data-Driven Organizations
6.2. Advanced Management Skills in Data-Driven Organizations
6.3. Using Data to Improve Strategic Communication Performance
6.4. Emotional Intelligence Applied to Management in Visual Analytics
6.5. Effective Presentations
6.6. Improving Performance Through Motivational Management
6.7. Leadership in Data-Driven Organizations
6.8. Digital Talent in Data-Driven Organizations
6.9. Data-Driven Agile Organization I
6.10. Data-Driven Agile Organization II

Module 7. Strategic Management of Visual Analytics and Big Data Projects

7.1. Introduction to Strategic Project Management
7.2. Best Practices in the Description of Big Data Processes (PMI)
7.3. Kimball Methodology
7.4. SQuID Methodology
7.5. Introduction to SQuID Methodology to Approach Big Data Projects

7.5.1. Phase I. Sources
7.5.2. Phase II. Data Quality
7.5.3. Phase III. Impossible Questions
7.5.4. Phase IV. Discovering
7.5.5. Best Practices in the Application of SQuID in Big Data Projects

7.6. Legal Aspects in the World of Data
7.7. Big Data Privacy
7.8. Cyber Security in Big Data
7.9. Identification and De-Identification with Large Volumes of Data
7.10. Data Ethics I
7.11. Data Ethics II

Module 8. Client Analysis. Applying Data Intelligence to Marketing

8.1. Concepts of Marketing. Strategic Marketing
8.2. Relationship Marketing
8.3. CRM as an Organizational Hub for Customer Analysis
8.4. Web Technologies
8.5. Web Data Sources
8.6. Acquisition of Web Data
8.7. Tools for the Extraction of Data from the Web
8.8. Semantic Web
8.9. OSINT: Open-Source Intelligence
8.10. Master Lead or How to Improve Sales Conversion Using Big Data

Module 9. Interactive Visualization of Data

9.1. Introduction to the Art of Making Data Visible
9.2. How to Perform Storytelling with Data
9.3. Data Representation
9.4. Scalability of Visual Representations
9.5. Visual Analytics vs. Information Visualization. Understanding That It Is Not The Same
9.6. Visual Analysis Process (Keim)
9.7. Strategic, Operative and Managerial Reports
9.8. Types of Graphs and Their Application
9.9. Interpretation of Reports and Graphs. Playing the Role of the Receiver
9.10. Evaluation of Visual Analytics Systems

Module 10. Visualization Tools

10.1. Introduction to Data Visualization Tools
10.2. Many Eyes
10.3. Google Charts
10.4. jQuery
10.5. Data-Driven Documents I
10.6. Data-Driven Documents II
10.7. Matlab
10.8. Tableau
10.9. SAS Visual Analytics
10.10. Microsoft Power BI

Module 11. Blockchain Technology. Cryptography and Security

11.1. Cryptography in Blockchain
11.2. A Hash in Blockchain
11.3. Private Sharing Multi-Hasing (PSM Hash)
11.4. Digital Signatures in Blockchain
11.5. Key Management. Wallets
11.6. Encryption
11.7. On-Chain and Off-Chain Data
11.8. Security and Smart Contracts

Module 12. Public Blockchain Development: Ethereum, Stellar and Polkadot

12.1. Ethereum. Public Blockchain

12.1.1. Ethereum
12.1.2. EVM and GAS
12.1.3. Etherescan

12.2. Running Ethereum: Solidity

12.2.1. Solidity
12.2.2. Remix
12.2.3. Compilation and Execution

12.3. Ethereum Framework: Brownie

12.3.1. Brownie
12.3.2. Ganache
12.3.3. Brownie Deployment

12.4. Testing Smart Contracts

12.4.1. Test Driven Development (TDD)
12.4.2. Pytest
12.4.3. Smart Contracts

12.5. Web Connection

12.5.1. Metamask
12.5.2. web3.js
12.5.3. Ether.js

12.6. Real Project: Fungible Token

12.6.1. ERC20
12.6.2. Creating Our Token
12.6.3. Deployment and Validation

12.7. Stellar Blockchain

12.7.1. Stellar Blockchain
12.7.2. Ecosystem
12.7.3. Compared to Ethereum

12.8. Programming in Stellar

12.8.1. Horizon
12.8.2. Stellar SDK
12.8.3. Fungible Token Project

12.9. Polkadot Project

12.9.1. Polkadot Project
12.9.2. Ecosystem
12.9.3. Interaction with Ethereum and Other Blockchain

12.10. Programming Polkadot

12.10.1. Substrate
12.10.2. Creating Parachain on Substrate
12.10.3. Polkadot Integration

Module 13. Corporate Blockchain Development: Hyperledger Besu

13.1. Besu Configuration

13.1.1. Key Configuration Parameters in Production Environments
13.1.2. Finetuning for Connected Services
13.1.3. Good Configuration Practices

13.2. Blockchain Configuration

13.2.1. Key Configuration Parameters for PoA
13.2.2. Key Configuration Parameters for PoW
13.2.3. Genesis Block Configurations

13.3. Securing Besu

13.3.1. Secure the RPC with TLS
13.3.2. RPC Securitization with NGINX
13.3.3. Securitization by Means of a Node Scheme

13.4. Besu in High Availability

13.4.1. Node Redundancy
13.4.2. Balancers for Transactions
13.4.3. Transaction Pool over Messaging Queue

13.5. Offchain Tools

13.5.1. Privacy - Tessera
13.5.2. Identidad - Alastria ID
13.5.3. Data Indexing - Subgraph

13.6. Applications Developed on Besu

13.6.1. ERC20 Token-Based Applications
13.6.2. ERC 721 Token-Based Applications
13.6.3. ERC 1155 Token-Based Applications

13.7. Besu Deployment and Automation

13.7.1. Besu over Docker
13.7.2. Besu over Kubernetes
13.7.3. Besu in Blockchain as a Service

13.8. Besu Interoperability with Other Clients

13.8.1. Interoperability with Geth
13.8.2. Interoperability with Open Ethereum
13.8.3. Interoperability with Other DLTs

13.9. Plugins for Besu

13.9.1. Most Common Plugins
13.9.2. Plugin Development
13.9.3. Installation of Plugins

13.10. Configuration of Development Environments

13.10.1. Creation of a Developing Environment
13.10.2. Creation of a Customer Integration Environment
13.10.3. Creating a Pre-Production Environment for Load Testing

Module 14. Corporate Blockchain Development: Hyperledger Fabric

14.1. Hyperledger

14.1.1. Hyperledger Ecosystem
14.1.2. Hyperledger Tools
14.1.3. Hyperledger Frameworks

14.2. Hyperledger Fabric – Components of its Architecture. State of the Art

14.2.1. State of the Art of Hyperledger Fabric
14.2.2. Nodes
14.2.3. Orderers
14.2.4. CouchDB and LevelDB
14.2.5. CA

14.3. Hyperledger Fabric-Components of Its Architecture. Process of a Transaction

14.3.1. Process of a Transaction
14.3.2. Chain Codes
14.3.3. MSP

14.4. Enabling Technologies

14.4.1. Go
14.4.2. Docker
14.4.3. Docker Compose
14.4.4. Other Technologies

14.5. Pre-Requisite Installation and Environment Preparation

14.5.1. Server Preparation
14.5.2. Download Prerequisites
14.5.3. Download from Official Hyperledger Repository

14.6. First Deployment

14.6.1. Automatic Test-Network Deployment
14.6.2. Guided Test-Network Deployment
14.6.3. Review of Deployed Components

14.7. Second Deployment

14.7.1. Deployment of Private Data Collection
14.7.2. Integration against a Fabric Network
14.7.3. Other Projects

14.8. Chain Codes

14.8.1. Structure of a Chaincode
14.8.2. Deployment and Upgrade of Chaincodes
14.8.3. Other Important Chaincode Functions

14.9. Connection to Other Hyperledger Tools (Caliper and Explorer)

14.9.1. Hyperledger Explorer Installation
14.9.2. Hyperledger Caliper Installation
14.9.3. Other Important Tools

14.10. Certification

14.10.1. Types of Official Certifications
14.10.2. Preparation for CHFA
14.10.3. Developer vs. Administrator Profiles

Module 15. Sovereign Identity Based on Blockchain

15.1. Digital Identity

15.1.1. Personal Data
15.1.2. Social Networks
15.1.3. Control Over Data
15.1.4. Authentication
15.1.5. Identification

15.2. Blockchain Identity

15.2.1. Digital Signature
15.2.2. Public Networks
15.2.3. Permitted Networks

15.3. Sovereign Digital Identity

15.3.1. Requirements
15.3.2. Components
15.3.3. Applications

15.4. Decentralized Identifiers (DIDs)

15.4.1. Layout
15.4.2. DID Methods
15.4.3. DID Documents

15.5. Verifiable Credentials

15.5.1. Components
15.5.2. Flows
15.5.3. Security and Privacy
15.5.4. Blockchain to Register Verifiable Credentials

15.6. Blockchain Technologies for Digital Identity

15.6.1. Hyperledger Indy
15.6.2. Sovrin
15.6.3. uPort
15.6.4. IDAlastria

15.7. European Blockchain and Identity Initiatives

15.7.1. eIDAS
15.7.2. EBSI
15.7.3. ESSIF

15.8. Digital Identity of Things (IoT)

15.8.1. IoT Interactions
15.8.2. Semantic Interoperability
15.8.3. Data Security

15.9. Digital Identity of the Processes

15.9.1. Date:
15.9.2. Codes
15.9.3. Interfaces

15.10. Blockchain Digital Identity Use Cases

15.10.1. Health
15.10.2. Educational
15.10.3. Logistics
15.10.4. Public Administration

Module 16. Blockchain and Its New Applications: DeFi and NFT

16.1. Financial Culture

16.1.1. Evolution of Money
16.1.2. FIAT Money vs. Decentralized Money
16.1.3. Digital Banking vs. Open Finance

16.2. Ethereum

16.2.1. Technology
16.2.2. Decentralized Money
16.2.3. Stable Coins

16.3. Other Technologies

16.3.1. Binance Smart Chain
16.3.2. Polygon
16.3.3. Solana

16.4. DeFi (Decentralized Finance)

16.4.1. DeFi
16.4.2. Challenges
16.4.3. Open Finance vs. DeFi

16.5. Information Tools

16.5.1. Metamask and Decentralized Wallets
16.5.2. CoinMarketCap
16.5.3. DefiPulse

16.6. Stable Coins

16.6.1. Protocol Maker
16.6.2. USDC, USDT, BUSD
16.6.3. Forms of Collateralization and Risks

16.7. Exchanges and Decentralized Exchanges and Platforms (DEX)

16.7.1. Uniswap
16.7.2. SushiSwap
16.7.3. AAVe
16.7.4. dYdX / Synthetix

16.8. NFT Ecosystem (Non-Fungible Tokens)

16.8.1. The NFT
16.8.2. Typology
16.8.3. Features

16.9. Capitulation of Industries

16.9.1. Design Industry
16.9.2. Fan Token Industry
16.9.3. Project Financing

16.10. NFT Markets

16.10.1. Opensea
16.10.2. Rarible
16.10.3. Customized Platforms

Module 17. Blockchain. Legal Implications

17.1. Bitcoin

17.1.1. Bitcoin
17.1.2. Whitepaper Analysis
17.1.3. Operation of the Proof of Work

17.2. Ethereum

17.2.1. Ethereum. Origins
17.2.2. Proof of Stake Operation
17.2.3. DAO Case

17.3. Current Status of the Blockchain

17.3.1. Growth of Cases
17.3.2. Blockchain Adoption by Large Companies

17.4. MiCA (Market in Cryptoassets)

17.4.1. Birth of the Standard
17.4.2. Legal Implications (Obligations, Obligated Parties, etc.)
17.4.3. Summary of the Standard

17.5. Prevention of Money Laundering

17.5.1. Fifth Directive and its Transposition
17.5.2. Obligated Parties
17.5.3. Intrinsic Obligations

17.6. Tokens

17.6.1. Tokens
17.6.2. Types
17.6.3. Applicable Regulations in Each Case

17.7. ICO/STO/IEO: Corporate Financing Systems

17.7.1. Types of Financing
17.7.2. Applicable Regulations
17.7.3. Success Stories

17.8. NFT (Non-Fungible Tokens)

17.8.1. NFT
17.8.2. Applicable Regulations
17.8.3. Use Cases and Success (Play to Earn)

17.9. Taxation and Cryptoassets

17.9.1. Taxation
17.9.2. Income from Work
17.9.3. Income from Economic Activities

17.10. Other Applicable Regulations

17.10.1. General Data Protection Regulation
17.10.2. DORA (Cybersecurity)
17.10.3. EIDAS Regulations

Module 18. Blockchain Architecture Design

18.1. Blockchain Architecture Design

18.1.1. Architecture
18.1.2. Infrastructure Architecture
18.1.3. Software Architecture
18.1.4. Integration Deployment

18.2. Types of Networks

18.2.1. Public Networks
18.2.2. Private Networks
18.2.3. Permitted Networks
18.2.4. Differences

18.3. Participant Analysis

18.3.1. Company Identification
18.3.2. Customer Identification
18.3.3. Consumer Identification
18.3.4. Interaction Between Parties

18.4. Proof-of-Concept Design

18.4.1. Functional Analysis
18.4.2. Implementation Phases

18.5. Infrastructure Requirements

18.5.1. Cloud
18.5.2. Physical
18.5.3. Hybrid

18.6. Security Requirements

18.6.1. Certification
18.6.2. HSM
18.6.3. Encryption

18.7. Communications Requirements

18.7.1. Network Speed Requirements
18.7.2. I/O Requirements
18.7.3. Transaction Requirements Per Second
18.7.4. Affecting Requirements with the Network Infrastructure

18.8. Software Testing, Performance and Stress Testing

18.8.1. Unit Testing in Development and Pre-Production Environments
18.8.2. Infrastructure Performance Testing
18.8.3. Pre-Production Testing
18.8.4. Production Testing
18.8.5. Version Control

18.9. Operation and Maintenance

18.9.1. Support: Alerts
18.9.2. New Versions of Infrastructure Components
18.9.3. Risk Analysis
18.9.4. Incidents and Changes

18.10. Continuity and Resilience

18.10.1. Disaster Recovery
18.10.2. Backup
18.10.3. New Participants

Module 19. Blockchain Applied to Logistics

19.1. Operational AS IS Mapping and Possible Gaps

19.1.1. Identification of Manually Executed Processes
19.1.2. Identification of Participants and their Particularities
19.1.3. Case Studies and Operational Gaps
19.1.4. Presentation and Mapping Executive Staff

19.2. Map of Current Systems

19.2.1. Current Systems
19.2.2. Master Data and Information Flow
19.2.3. Governance Model

19.3. Application of Blockchain to Logistics

19.3.1. Blockchain Applied to Logistics
19.3.2. Traceability-Based Architectures for Business Processes
19.3.3. Critical Success Factors in Implementation
19.3.4. Practical Advice

19.4. TO BE Model

19.4.1. Operational Definition for Supply Chain Control
19.4.2. Structure and Responsibilities of the Systems Plan
19.4.3. Critical Success Factors in Implementation

19.5. Construction of the Business Case

19.5.1. Cost Structure
19.5.2. Projected Benefits
19.5.3. Approval and Acceptance of the Plan by the Owners

19.6. Creation of Proof of Concept (POC)

19.6.1. Importance of a POC for New Technologies
19.6.2. Key Aspects
19.6.3. Examples of POCs with Low Cost and Effort

19.7. Project Management

19.7.1. Agile Methodology
19.7.2. Decision of Methodologies Among all Participants
19.7.3. Strategic Development and Deployment Plan

19.8. Integration of Systems: Opportunities and Needs

19.8.1. Structure and Development of the Systems Planning
19.8.2. Data Master Model
19.8.3. Roles and Responsibilities
19.8.4. Integrated Management and Monitoring Model

19.9. Development and Implementation with Supply Chain Team

19.9.1. Active Participation of the Customer (Business)
19.9.2. Systemic and Operational Risk Analysis
19.9.3. Event Key: Test Models and Post-Production Support

19.10. Change Management: Follow-Up and Updating

19.10.1. Management Implications
19.10.2. Rollout and Education Plan
19.10.3. KPI Tracking and Management Models

Module 20. Blockchain and Business

20.1. Applying Technology throughout the Company

20.1.1. Applying Blockchain
20.1.2. Blockchain Benefits
20.1.3. Common Implementation Mistakes

20.2. Blockchain Implementation Cycle

20.2.1. From P2P to Distributed Systems
20.2.2. Key Aspects for Proper Implementation
20.2.3. Improving Current Implementations

20.3. Blockchain vs. Traditional Technologies. Basics

20.3.1. APIs Data and Flows
20.3.2. Tokenization as a Cornerstone for Projects
20.3.3. Incentives

20.4. Selecting Blockchain Type

20.4.1. Public Blockchain
20.4.2. Private Blockchain
20.4.3. Consortiums

20.5. Blockchain and the Public Sector

20.5.1. Blockchain in the Public Sector
20.5.2. Central Bank Digital Currency (CBDC)
20.5.3. Conclusions

20.6. Blockchain and the Financial Sector. Start

20.6.1. CBDC and Finance
20.6.2. Native Digital Assets
20.6.3. Where It Does Not Fit

20.7. Blockchain and the Pharmaceutical Sector

20.7.1. Searching for Meaning in the Field
20.7.2. Logistics or Pharma
20.7.3. Application

20.8. Pseudo Private Blockchains. Consortiums: Meaning of Consortiums

20.8.1. Reliable Environments
20.8.2. Analysis and Delving Deeper
20.8.3. Valid Implementations

20.9. Blockchain. Use Case in Europe: EBSI

20.9.1. EBSI (European Blockchain Services Infrastructure)
20.9.2. The Business Model
20.9.3. Future

20.10. The Future of Blockchain

20.10.1. Trilemma
20.10.2. Automation
20.10.3. Conclusions

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