Thanks to this Hybrid Master's Degree MBA, you will master Data Ingestion technologies for your company to make the most informed decisions and develop innovative processes"

Digital Transformation and Industry 4.0 allows experts to acquire competitive advantages to ensure their professional performance for the future. In this context, by effectively handling enabling technologies (such as the Internet of Things or Deep Learning), specialists become an important asset for companies. This is because they are qualified to carry out their digital transition and thus adapt to today's needs.  These tools also offer other advantages, such as process automation. In this way, they serve to increase operational efficiency, reduce production times and optimize organizational resources. However, as these are emerging tools, professionals require constant updating to stay at the technological forefront. 

For this reason, TECH creates a revolutionary Hybrid Master's Degree MBA in MBA in Digital Transformation and Industry 4.0. Through this curriculum, students will nurture their professional practice with the most innovative techniques and tools of Artificial Intelligence. To achieve this, the syllabus will delve into issues such as Neural Networks, Natural Language Processing or the architectures behind a Smart Factory.

It should be noted that the program is based on a disruptive educational modality, composed of 2 stages. The first is taught 100% online, so that students can study the concepts and working techniques. To facilitate the learning process, the educational cycle is supported by the Relearning methodology, which will offer students the assimilation of content in a faster and more flexible way. 

On the other hand, after this, the graduates will carry out a practical stay in a prestigious company dedicated to technology to apply all the knowledge acquired. With a duration of 3 weeks, students will work alongside leading experts in Digital Transformation processes. In addition, they will have the support of an assistant tutor who will be in charge of including dynamic tasks in the program to formalize their academic update.  

TECH offers you the revolutionary Relearning methodology, with which you will achieve a much more effective and situated learning"

This Hybrid Master's Degree in MBA in Digital Transformation and Industry 4.0 contains the most complete and up-to-date program on the market. The most important features include:

• Development of more than 100 case studies presented by Digital Transformation and Industry 4.0 professionals
• Its graphic, schematic and eminently practical contents, with which they are conceived, gather essential information on those technological disciplines essential for professional practice
• Practice guides to properly build immersive virtual environments
• Reports on the current market situation and growth by different industries
• Innovative strategies for the implementation of an API to interact with platforms
• All of this will be complemented by theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection
• Furthermore, you will be able to carry out an internship in one of the best companies

An intensive university degree that will lay the foundations for your professional progress and will place you at the pinnacle of Industry 4.0"

In this Professional Master's Degree proposal, of a professionalizing nature and blended mode, the program is aimed at updating professionals who develop Digital Transformation tasks in companies. The contents are based on the latest scientific evidence, and oriented in a didactic way to integrate theoretical knowledge in the practical reality of the labor market. 

Thanks to its multimedia content elaborated with the latest educational technology, they will allow the Digital Transformation and Industry 4.0 professional a situated and contextual learning, that is, a simulated environment that will provide immersive learning programmed to specialize in real situations. The design of this program is based on Problem-Based Learning, by means of which they will have to try to solve the different situations of professional practice that will be presented to them throughout the program. For this purpose, the students will be assisted by an innovative interactive video system created by renowned and experienced experts.

Take an intensive 3-week internship at a prestigious technology company and acquire all the knowledge you need to grow professionally"

The interactive summaries of each topic will allow you to consolidate the concepts of Lean Manufacturing in a more dynamic way"

This Hybrid Master's Degree MBA is designed by a multidisciplinary teaching team, which has put all its efforts in pouring into the teaching materials its deep knowledge and years of professional experience in the field of MBA in Digital Transformation and Industry 4.0. Therefore, TECH provides students with a Virtual Campus full of high quality multimedia teaching resources, available 24 hours a day. In addition, graduates will culminate their academic journey with a practical stay in a prestigious technology company, where they will deploy their technical and analytical skills in Deep Learning.

You will have at your disposal the most modern educational resources, with free access to the Virtual Campus 24 hours a day. Enroll now!”

Module 1. Blockchain and Quantum Computing

1.1. Aspects of Decentralization

1.1.1. Market Size, Growth, Companies and Ecosystem
1.1.2. Fundamentals of Blockchain

1.2. Background: Bitcoin, Ethereum, etc.

1.2.1. Popularity of Decentralized Systems
1.2.2. Evolution of Decentralized Systems

1.3. Blockchain Operation and Examples

1.3.1. Types of Blockchain and Protocols
1.3.2. Wallets, Mining and More

1.4. Characteristics of Blockchain Networks

1.4.1. Functions and Properties of Blockchain Networks
1.4.2. Applications: Cryptocurrencies, Reliability, Chain of Custody, etc

1.5. Types of Blockchain

1.5.1. Public and Private Blockchains
1.5.2. Hard And Soft Forks

1.6. Smart Contracts

1.6.1. Intelligent Contracts and Their Potential
1.6.2. Smart Contract Applications

1.7. Industry Use Models

1.7.1. Blockchain Applications by Industry
1.7.2. Blockchain Success Stories by Industry

1.8. Security and Cryptography

1.8.1. Objectives of Cryptography
1.8.2. Digital Signatures and Hash Functions

1.9. Cryptocurrencies and Uses

1.9.1. Types of Cryptocurrencies Bitcoin, Hyperledger, Ethereum, Litecoin, etc.
1.9.2. Current and Future Impact of Cryptocurrencies
1.9.3. Risks and Regulations

1.10. Quantum Computing

1.10.1. Definition and Keys
1.10.2. Uses of Quantum Computing

Module 2. Big Data and Artificial Intelligence

2.1. Fundamental Principles of Big Data

2.1.1. Big Data
2.1.2. Tools to Work With Big Data

2.2. Data Mining and Warehousing

2.2.1. Data Mining Cleaning and Standardization
2.2.2. Information Extraction, Machine Translation, Sentiment Analysis, etc
2.2.3. Types of Data Storage

2.3. Data Intake Applications

2.3.1. Principles of Data intake
2.3.2. Data Ingestion Technologies to Serve Business Needs

2.4. Data Visualization

2.4.1. The Importance of Data Visualization
2.4.2. Tools to Carry It Out Tableau, D3, matplotlib (Python), Shiny®

2.5. Machine Learning

2.5.1. Understanding Machine Learning
2.5.2. Supervised and Unsupervised Learning
2.5.3. Types of Algorithms

2.6. Neural Networks (Deep Learning)

2.6.1. Neural Network: Parts and Operation
2.6.2. Types of Networks CNN, RNN
2.6.3. Applications of Neural Networks; Image Recognition and Natural Language Interpretation
2.6.4. Generative Text Networks: LSTM

2.7. Natural Language Recognition

2.7.1. PLN (Processing Natural Language)
2.7.2. Advanced PLN Techniques: Word2vec, Doc2vec

2.8. Chatbots and Virtual Assistants

2.8.1. Types of Assistants: Voice and Text Assistants
2.8.2. Fundamental Parts for the Development of an Assistant: Intents, Entities and Dialog Flow
2.8.3. Integrations: Web, Slack, WhatsApp, Facebook
2.8.4. Assistant Development Tools: Dialogflow, Watson Assistant

2.9. Emotions, Creativity and Personality in IA

2.9.1. Understand How to Detect Emotions Using Algorithms
2.9.2. Creating a Personality: Language, Expressions and Content

2.10. Future of Artificial Intelligence
2.11. Reflections

Module 3. Virtual, Augmented and Mixed Reality

3.1. Market and Tendencies

3.1.1. Current Market Situation
3.1.2. Reports and Growth by Different Industries

3.2. Differences Between Virtual, Augmented and Mixed Reality

3.2.1. Differences Between Immersive Realities
3.2.2. Immersive Reality Typology

3.3. Virtual Reality Cases and Uses

3.3.1. Origin and Fundamentals of Virtual Reality
3.3.2. Cases Applied to Different Sectors and Industries

3.4. Augmented Reality Cases and Uses

3.4.1. Origin and Fundamentals of Augmented Reality
3.4.2. Cases Applied to Different Sectors and Industries

3.5. Mixed and Holographic Reality

3.5.1. Origin, History and Fundamentals of Mixed and Holographic Reality
3.5.2. Cases Applied to Different Sectors and Industries

3.6. 360º Photography and Video

3.6.1. Camera Typology
3.6.2. Uses of 360 Images
3.6.3. Creating a Virtual Space in 360 Degrees

3.7. Virtual World Creation

3.7.1. Platforms for the Creation of Virtual Environments
3.7.2. Strategies for the Creation of Virtual Environments

3.8. User Experience (UX)

3.8.1. Components in the User Experience
3.8.2. Tools for the Creation of User Experiences

3.9. Devices and Glasses for Immersive Technologies

3.9.1. Device Typology on the Market
3.9.2. Glasses and Wearables: Operation, Models and Uses
3.9.3. Smart Glasses Applications and Evolution

3.10. Future Immersive Technologies

3.10.1. Tendencies and Evolution
3.10.2. Challenges and Opportunities

Module 4. Industry 4.0

4.1. Definition of 4.0 Industry

4.1.1. Features

4.2. Benefits of the 4.0 Industry

4.2.1. Key Factors
4.2.2. Main Advantages

4.3. Industrial Revolutions and Vision of the Future

4.3.1. Industrial Revolutions
4.3.2. Keys Factors in Each Revolution
4.3.3. Technological Principles as a Basis for Possible New Revolutions

4.4. The Digital Transformation of the Industry

4.4.1. Characteristics of the Digitization of the Industry
4.4.2. Disruptive Technologies
4.4.3. Applications in the Industry

4.5. Forth Industrial Revolution. Key Principles of Industry 4.0

4.5.1. Definitions
4.5.2. Key Principles and Applications

4.6. 4.0 Industry and Industrial Internet

4.6.1. Origin of IoT
4.6.2. Operation
4.6.3. Steps to Follow for its Implementation
4.6.4. Benefits

4.7. Smart Factory Principles

4.7.1. The Smart Factory
4.7.2. Elements that Define a Smart Factory
4.7.3. Steps to Deploy a Smart Factory

4.8. Status of the 4.0 Industry

4.8.1. Status of the 4.0 Industry in Different Sectors
4.8.2. Barriers to the Implementation of 4.0 Industry

4.9. Challenges and Risks

4.9.1. DAFO Analysis
4.9.2. Challenges

4.10. Role of Technological Capabilities and the Human Factor

4.10.1. Disruptive Technologies in Industry 4.0
4.10.2. The Importance of the Human Factor Key Factor

Module 5. Leading Industry 4.0

5.1. Leadership Abilities

5.1.1. Leadership Factors in the Human Factor
5.1.2. Leadership and Technology

5.2. Industry 4.0 and the Future of Production

5.2.1. Definitions
5.2.2. Production Systems
5.2.3. Future of Digital Production Systems

5.3. Effects of Industry 4.0

5.3.1. Effects and Challenges

5.4. Essential Technologies in Industry 4.0

5.4.1. Definition of Technologies
5.4.2. Characteristics of Technologies
5.4.3. Applications and Impacts

5.5. Digitization of Manufacturing

5.5.1. Definitions
5.5.2. Benefits of the Digitization of Manufacturing
5.5.3. Digital Twins

5.6. Digital Capabilities in an Organization

5.6.1. Development Digital Capabilities
5.6.2. Understanding the Digital Ecosystem
5.6.3. Digital Vision of the Business

5.7. Architecture Behind a Smart Factory

5.7.1. Areas and Operations
5.7.2. Connectivity and Security
5.7.3. Case Uses

5.8. Technology Markers in the Post-Covid Era

5.8.1. Technological Challenges in the Post-Covid Era
5.8.2. New Case Uses

5.9. The Era of Absolute Virtualization

5.9.1. Virtualization
5.9.2. The New Era of Virtualization
5.9.3. Advantages

5.10. Current Situation in Digital Transformation Gartner Hype

5.10.1. Gartner Hype
5.10.2. Analysis of Technologies and Their Status
5.10.3. Data Exploitation

Module 6. Robotics, Drones and Augmented Workers

6.1. Robotics

6.1.1. Robotics, Societies and Cinema
6.1.2. Components and Parts of Robot

6.2. Robotics and Advanced Automation: Simulators, Cobots

6.2.1. Transfer of Learning
6.2.2. Cobots and Case Uses

6.3. RPA (Robotic Process Automatization)

6.3.1. Understanding RPA and its Functioning
6.3.2. RPA Platforms, Projects and Roles

6.4. Robot as a Service (RaaS)

6.4.1. Challenges and Opportunities for Implementing Raas Services and Robotics in Enterprises
6.4.2. Operation of a Raas System

6.5. Drones and Automated Vehicles

6.5.1. Components and Drones Operation
6.5.2. Uses, Types and Applications of Drones
6.5.3. Evolution of Drones and Autonomous Vehicles

6.6. The Impact of 5G

6.6.1. Evolution of Communications and Implications
6.6.2. Uses of 5G Technology

6.7. Augmented Workers

6.7.1. Human-Machine Integration in Industrial Environments
6.7.2. Challenges in Worker-Robot Collaboration

6.8. Transparency, Ethics and Traceability

6.8.1. Ethical Challenges in Robotics and Artificial Intelligence
6.8.2. Monitoring, Transparency and Traceability Methods

6.9. Prototyping, Components and Evolution

6.9.1. Prototyping Platforms
6.9.2. Phases to Make a Prototype

6.10. Future of Robotics

6.10.1. Trends in Robotization
6.10.2. New Types of Robots

Module 7. Industry 4.0 Automation Systems

7.1. Industrial Automation

7.1.1. Automization
7.1.2. Architecture and Components
7.1.3. Safety

7.2. Industrial Robotics

7.2.1. Fundamentals of Industrial Robotics
7.2.2. Models and Impact on Industrial Processes

7.3. PLC Systems and Industrial Control

7.3.1. PLC Evolution and Status
7.3.2. Evolution of Programming Languages
7.3.3. Computer Integrated Automation CIM

7.4. Sensors and Actuators

7.4.1. Classification of Transducers
7.4.2. Types of Sensors
7.4.3. Standardization of Signals

7.5. Monitor and Manage

7.5.1. Types of Actuators
7.5.2. Feedback Control Systems

7.6. Industrial Connectivity

7.6.1. Standardized Fieldbuses
7.6.2. Connectivity

7.7. Proactive / Predictive Maintenance

7.7.1. Predictive Maintenance
7.7.2. Fault Identification and Analysis
7.7.3. Proactive Actions Based on Predictive Maintenance

7.8. Continuous Monitoring and Prescriptive Maintenance

7.8.1. Prescriptive Maintenance Concept in Industrial Environments
7.8.2. Selection and Exploitation of Data for Self-Diagnostics

7.9. Lean Manufacturing

7.9.1. Lean Manufacturing
7.9.2. Benefits Lean Implementation in Industrial Processes

7.10. Industrialized Processes in Industry 4.0. Use Case

7.10.1. Project definition
7.10.2. Technological Selection
7.10.3. Connectivity
7.10.4. Data Exploitation

Module 8. Industry 4.0- Services and Solutions I

8.1. Industry 4.0 and Business Strategies

8.1.1. Factors of Business Digitalization
8.1.2. Roadmap for Business Digitalization

8.2. Digitalization of Processes and the Value Chain

8.2.1. Value Chain
8.2.2. Key Steps in the Digitization of Processes

8.3. Sector Solutions Primary Sector

8.3.1. The Primary Economic Sector
8.3.2. Characteristics of Each Subsector

8.4. Digitization of the Primary Sector: Smart Farms

8.4.1. Main Characteristics
8.4.2. Keys Factors of Digitization

8.5. Digitization of the Primary Sector: Digital and Intelligent Agriculture

8.5.1. Main Characteristics
8.5.2. Keys Factors of Digitization

8.6. Sector Solutions Secondary Sector

8.6.1. The Secondary Economic Sector
8.6.2. Characteristics of Each Subsector

8.7. Digitization of the Secondary Sector: Smart Factory

8.7.1. Main Characteristics
8.7.2. Keys Factors of Digitization

8.8. Digitization of the Secondary Sector: Energy

8.8.1. Main Characteristics
8.8.2. Keys Factors of Digitization

8.9. Digitization of the Secondary Sector: Construction

8.9.1. Main Characteristics
8.9.2. Keys Factors of Digitization

8.10. Digitization of the Secondary Sector: Mining

8.10.1. Main Characteristics
8.10.2. Keys Factors of Digitization

Module 9. Industry 4.0 Services and Solutions II

9.1. Tertiary Sector Solutions

9.1.1. Tertiary Economic Sector
9.1.2. Characteristics of Each Subsector

9.2. Digitalization of the Tertiary Sector: Transportation

9.2.1. Main Characteristics
9.2.2. Keys Factors of Digitization

9.3. Digitization of the Tertiary Sector: e-Health

9.3.1. Main Characteristics
9.3.2. Keys Factors of Digitization

9.4. Digitization of the Tertiary Sector: Smart Hospitals

9.4.1. Main Characteristics
9.4.2. Keys Factors of Digitization

9.5. Digitization of the Tertiary Sector: Smart Cities

9.5.1. Main Characteristics
9.5.2. Keys Factors of Digitization

9.6. Digitalization of the Tertiary Sector: Logistics

9.6.1. Main Characteristics
9.6.2. Keys Factors of Digitization

9.7. Digitalization of the Tertiary Sector: Tourism

9.7.1. Main Characteristics
9.7.2. Keys Factors of Digitization

9.8. Digitization of the Tertiary Sector: Fintech

9.8.1. Main Characteristics
9.8.2. Keys Factors of Digitization

9.9. Digitalization of the Tertiary Sector: Mobility

9.9.1. Main Characteristics
9.9.2. Keys Factors of Digitization

9.10. Future Technological Tendencies

9.10.1. New Technological Innovations
9.10.2. Application Trends

Module 10. Internet of Things (IoT)

10.1. Cyber-Physical Systems (CPS) in the Industry 4.0 Vision

10.1.1. Internet of Things (IoT)
10.1.2. Components Involved in IoT
10.1.3. Cases and Applications of IoT

10.2. Internet of Things and Cyber-Physical Systems

10.2.1. Computing and Communication Capabilities to Physical Objects
10.2.2. Sensors, Data and Elements in Cyber-Physical Systems

10.3. Device Ecosystem

10.3.1. Typologies, Examples and Uses
10.3.2. Applications of the Different Devices

10.4. IoT Platforms and their Architecture

10.4.1. IoT Market Typologies and Platforms
10.4.2. Operation of an IoT Platform

10.5. Digital Twins

10.5.1. Digital Twins
10.5.2. Uses and Applications the Digital Twin

10.6. Indoor & outdoor Geolocation (Real Time Geospatial)

10.6.1. Indoor and Outdoor Geolocation Platforms
10.6.2. Implications and Challenges of Geolocation in an IoT Project

10.7. Security Intelligence Systems

10.7.1. Typologies and Platforms for Security Systems Implementation
10.7.2. Components and Architectures in Intelligent Safety Systems

10.8. IoT and IIoT Platform Security

10.8.1. Security Components in an IoT System
10.8.2. IoT Security Implementation Strategies

10.9. Wearables at Work

10.9.1. Types of Wearables in Industrial Environments
10.9.2. Lessons Learned and Challenges in Implementing Wearables in the Workplace

10.10. Implementing an API to Interact with a Platform

10.10.1. Types of APIs Involved in an IoT Platform
10.10.2. API Market
10.10.3. Strategies and Systems to Implement API Integrations

Thanks to this university degree you will be up to date with the most cutting-edge trends in Big Data, Machine Learning and Natural Language Processing"