A university program with which you will become the most outstanding Clinical Researcher in your environment. You will lead projects that will contribute to the advancement of Medicine!”  

 

Why study at TECH?

TECH is the world's largest 100% online business school. It is an elite business school, with a model based on the highest academic standards. A world-class centre for intensive managerial skills training.   

TECH is a university at the forefront of technology, and puts all its resources at the student's disposal to help them achieve entrepreneurial success"     

At TECH Global University

Innovation

The university offers an online learning model that combines the latest educational technology with the most rigorous teaching methods. A unique method with the highest international recognition that will provide students with the keys to develop in a rapidly-evolving world, where innovation must be every entrepreneur’s focus. 

"Microsoft Europe Success Story", for integrating the innovative, interactive multi-video system.  

The Highest Standards

Admissions criteria at TECH are not economic. Students don't need to make a large investment to study at this university. However, in order to obtain a qualification from TECH, the student's intelligence and ability will be tested to their limits. The institution's academic standards are exceptionally high... 

95% of TECH students successfully complete their studies.

Networking

Professionals from countries all over the world attend TECH, allowing students to establish a large network of contacts that may prove useful to them in the future.

100,000+ executives trained each year, 200+ different nationalities.

Empowerment

Students will grow hand in hand with the best companies and highly regarded and influential professionals. TECH has developed strategic partnerships and a valuable network of contacts with major economic players in 7 continents.    

500+ collaborative agreements with leading companies.

Talent

This program is a unique initiative to allow students to showcase their talent in the business world. An opportunity that will allow them to voice their concerns and share their business vision. 

After completing this program, TECH helps students show the world their talent. 

 

Show the world your talent after completing this program. 

Multicultural Context

While studying at TECH, students will enjoy a unique experience. Study in a multicultural context. In a program with a global vision, through which students can learn about the operating methods in different parts of the world, and gather the latest information that best adapts to their business idea. 

TECH students represent more than 200 different nationalities. 

 

Learn with the best

In the classroom, TECH’s teaching staff discuss how they have achieved success in their companies, working in a real, lively, and dynamic context. Teachers who are fully committed to offering a quality specialization that will allow students to advance in their career and stand out in the business world. 

Teachers representing 20 different nationalities. 

TECH strives for excellence and, to this end, boasts a series of characteristics that make this university unique: 

Analysis

TECH explores the student’s critical side, their ability to question things, their problem-solving skills, as well as their interpersonal skills.    

Academic Excellence

TECH offers students the best online learning methodology. The university combines the Relearning method (a postgraduate learning methodology with the highest international rating) with the Case Study. A complex balance between tradition and state-of-the-art, within the context of the most demanding academic itinerary.  

Economy of Scale

TECH is the world’s largest online university. It currently boasts a portfolio of more than 10,000 university postgraduate programs. And in today's new economy, volume + technology = a ground-breaking price. This way, TECH ensures that studying is not as expensive for students as it would be at another university.   

At TECH, you will have access to the most rigorous and up-to-date case studies in the academic community”

Through this university specialization, students will have a solid understanding of the principles of Artificial Intelligence and will be able to effectively integrate its tools into their Clinical Research projects. To this end, the syllabus will include topics such as Intelligent Systems, Algorithmics and Machine Learning. This will enable graduates to analyze large amounts of medical data, which will be used to make highly informed decisions. In addition, the program will include disruptive modules that will delve into Neural Networks, Model Personalization or Natural Language Processing. 

This university program will allow you to exercise in simulated environments, which provide immersive learning programmed to specialize in front of real situations”   

Syllabus

The Advanced master’s degree in MBA in Artificial Intelligence in Clinical Research at TECH Global University is an intensive program that prepares students to face business challenges and decisions internationally. Its content is designed to promote the development of managerial skills that enable more rigorous decision-making in uncertain environments.   

Throughout 3,600 hours of study, students will analyze a multitude of practical cases through individual work, achieving high quality learning that can be applied to their daily practice. It is, therefore, an authentic immersion in real business situations.

This program deals in depth with the main areas of Artificial Intelligence and is designed for managers to understand its application in Clinical Research from a strategic, international and innovative perspective.   

A plan designed for students, focused on their professional improvement and that prepares them to achieve excellence in the field of Artificial Intelligence in Clinical Research. A program that understands your needs and those of your company through innovative content based on the latest trends, and supported by the best educational methodology and an exceptional faculty, which will provide you with the competencies to solve critical situations in a creative and efficient way. 

This program is developed over 2 years and is divided into 30 modules: 

Module 1. Leadership, Ethics and Social Responsibility in Companies
Module 2. Strategic Managementand Executive Management 
Module 3. People and Talent Management
Module 4. Economic and Financial Management
Module 5. Operations and Logistics Management
Module 6. Information Systems Management
Module 7. Commercial Management, Strategic Marketing and Corporate Communications
Module 8. Market Research, Advertising and Commercial Management
Module 9. Innovation and Project Management
Module 10. Executive  Management
Module 11. Fundamentals of Artificial Intelligence
Module 12. Data Types and Life Cycle
Module 13. Data in Artificial Intelligence
Module 14. Data Mining: Selection, Pre-Processing and Transformation
Module 15. Algorithm and Complexity in Artificial Intelligence
Module 16. Intelligent Systems
Module 17. Machine Learning and Data Mining
Module 18. Neural networks, the basis of Deep Learning 
Module 19. Deep Neural Networks Training
Module 20. Model Customization and Training with TensorFlow 
Module 21. Deep Computer Vision with Convolutional Neural Networks
Module 22. Natural Language Processing (NLP) with Recurrent Neural Networks (RNN) and Attention 
Module 23. Autoencoders, GANs, and Diffusion Models 
Module 24. Bio-Inspired Computing
Module 25. Artificial Intelligence: Strategies and Applications
Module 26. Artificial Intelligence Methods and Tools for Clinical Research
Module 27. Biomedical Research with AI
Module 28. Practical Application of Artificial Intelligence in Clinical Research
Module 29.  Big Data Analytics and Machine Learning in Clinical Research
Module 30. Ethical, Legal and Future Aspects of Artificial Intelligence in Clinical Research

Where, When and How is it Taught?

TECH offers the possibility of developing this MBA in Artificial Intelligence in Clinical Research completely online. During the 2 years of the program, students will be able to access all the contents in this program at any time, which will allow them to manage their own study time 

Module 1. Leadership, Ethics and Social Responsibility in Companies

1.1. Globalization and Governance 

1.1.1. Governance and Corporate Governance 
1.1.2. The Fundamentals of Corporate Governance in Companies 
1.1.3. The Role of the Board of Directors in the Corporate Governance Framework 

1.2. Leadership 

1.2.1. Leadership A Conceptual Approach 
1.2.2. Leadership in Companies 
1.2.3. The Importance of Leaders in Business Management 

1.3. Cross Cultural Management 

1.3.1. Cross Cultural Management Concept
1.3.2. Contributions to Knowledge of National Cultures 
1.3.3. Diversity Management 

1.4. Management and Leadership Development 

1.4.1. Concept of Management Development 
1.4.2. Concept of Leadership 
1.4.3. Leadership Theories 
1.4.4. Leadership Styles 
1.4.5. Intelligence in Leadership 
1.4.6. The Challenges of Today's Leader 

1.5. Business Ethics 

1.5.1. Ethics and Morality 
1.5.2. Business Ethics 
1.5.3. Leadership and Ethics in Companies 

1.6. Sustainability 

1.6.1. Sustainability and Sustainable Development 
1.6.2. The 2030 Agenda 
1.6.3. Sustainable Companies 

1.7. Corporate Social Responsibility 

1.7.1. International Dimensions of Corporate Social Responsibility 
1.7.2. Implementing Corporate Social Responsibility 
1.7.3. The Impact and Measurement of Corporate Social Responsibility 

1.8. Responsible Management Systems and Tools 

1.8.1. CSR: Corporate Social Responsibility 
1.8.2. Essential Aspects for Implementing a Responsible Management Strategy 
1.8.3. Steps for the Implementation of a Corporate Social Responsibility Management System 
1.8.4. CSR Tools and Standards 

1.9. Multinationals and Human Rights 

1.9.1. Globalization, Multinational Companies and Human Rights 
1.9.2. Multinational Companies vs. International Law 
1.9.3. Legal Instruments for Multinationals in the Area of Human Rights 

1.10. Legal Environment and Corporate Governance 

1.10.1. International Rules on Importation and Exportation 
1.10.2. Intellectual and Industrial Property 
1.10.3. International Labor Law

Module 2. Strategic Management and Executive Management 

2.1. Organizational Analysis and Design 

2.1.1. Conceptual Framework
2.1.2. Key Elements in Organizational Design
2.1.3. Basic Organizational Models
2.1.4. Organizational Design: Typologies 

2.2. Corporate Strategy 

2.2.1. Competitive Corporate Strategy
2.2.2. Types of Growth Strategies
2.2.3. Conceptual Framework 

2.3. Strategic Planning and Strategy Formulation 

2.3.1. Conceptual Framework
2.3.2. Elements of Strategic Planning
2.3.3. Strategy Formulation: Strategic Planning Process 

2.4. Strategic Thinking 

2.4.1. The Company as a System
2.4.2. Organization Concept

2.5. Financial Diagnosis 

2.5.1. Concept of Financial Diagnosis
2.5.2. Stages of Financial Diagnosis
2.5.3. Assessment Methods for Financial Diagnosis 

2.6. Planning and Strategy 

2.6.1. The Plan from a Strategy
2.6.2. Strategic Positioning
2.6.3. Strategy in Companies 

2.7. Strategy Models and Patterns 

2.7.1. Conceptual Framework
2.7.2. Strategic Models
2.7.3. Strategic Patterns: The Five P’s of Strategy 

2.8. Competitive Strategy 

2.8.1. The Competitive Advantage
2.8.2. Choosing a Competitive Strategy
2.8.3. Strategies Based on the Strategic Clock Model
2.8.4. Types of Strategies According to the Industrial Sector Life Cycle 

2.9. Strategic Management 

2.9.1. The Concept of Strategy
2.9.2. The Process of Strategic Management
2.9.3. Approaches in Strategic Management 

2.10. Strategy Implementation 

2.10.1. Indicator Systems and Process Approach
2.10.2. Strategic Map
2.10.3. Strategic Alignment 

2.11. Executive Management 

2.11.1. Conceptual Framework of Executive Management
2.11.2. Executive Management The Role of the Board of Directors and Corporate Management Tools 

2.12. Strategic Communication 

2.12.1. Interpersonal Communication 
2.12.2. Communication Skills and Influence 
2.12.3. Internal Communication 
2.12.4. Barriers to Business Communication

Module 3. People and Talent Management 

3.1. Organizational Behavior 

3.1.1. Organizational Behavior Conceptual Framework
3.1.2. Main Factors of Organizational Behavior 

3.2. People in Organizations 

3.2.1. Quality of Work Life and Psychological Well-Being
3.2.2. Work Teams and Meeting Management
3.2.3. Coaching and Team Management
3.2.4. Managing Equality and Diversity 

3.3. Strategic People Management 

3.3.1. Strategic Human Resources Management
3.3.2. Strategic People Management 

3.4. Evolution of Resources An Integrated Vision 

3.4.1. The Importance of HR
3.4.2. A New Environment for People Management and Leadership
3.4.3. Strategic HR Management

3.5. Selection, Group Dynamics and HR Recruitment 

3.5.1. Approach to Recruitment and Selection
3.5.2. Recruitment.
3.5.3. The Selection Process 

3.6. Human Resources Management by Competencies 

3.6.1. Analysis of the Potential
3.6.2. Remuneration Policy
3.6.3. Career/Succession Planning 

3.7. Performance Evaluation and Compliance Management 

3.7.1. Performance Management
3.7.2. Performance Management: Objectives and Process 

3.8. Training Management 

3.8.1. Learning Theories
3.8.2. Talent Detection and Retention
3.8.3. Gamification and Talent Management
3.8.4. Training and Professional Obsolescence 

3.9. Talent Management 

3.9.1. Keys for Positive Management
3.9.2. Conceptual Origin of Talent and its Implication in the Company
3.9.3. Map of Talent in the Organization 
3.9.4. Cost and Added Value 

3.10. Innovation in Talent and People Management 

3.10.1. Strategic Talent Management Models
3.10.2. Identification, Training and Development of Talent
3.10.3. Loyalty and Retention 
3.10.4. Proactivity and Innovation 

3.11. Motivation 

3.11.1. The Nature of Motivation 
3.11.2. Expectations Theory
3.11.3. Needs Theory
3.11.4. Motivation and Financial Compensation 

3.12. Employer Branding 

3.12.1. Employer Branding in HR
3.12.2. Personal Branding for HR Professionals 

3.13. Developing High Performance Teams

3.13.1. High-Performance Teams: Self-Managed Teams 
3.13.2. Methodologies for the Management of High Performance Self-Managed Teams 

3.14. Management Skills Development 

3.14.1. What are Manager Competencies? 
3.14.2. Elements of Competencies 
3.14.3. Knowledge 
3.14.4. Management Skills 
3.14.5. Attitudes and Values in Managers 
3.14.6. Managerial Skills 

3.15. Time Management 

3.15.1. Benefits
3.15.2. What Can be the Causes of Poor Time Management? 
3.15.3. Time 
3.15.4. Time Illusions 
3.15.5. Attention and Memory
3.15.6. State of Mind
3.15.7. Time Management
3.15.8. Being Proactive
3.15.9. Be Clear About the Objective
3.15.10. Order 
3.15.11. Planning 

3.16. Change Management 

3.16.1. Change Management
3.16.2. Type of Change Management Processes
3.16.3. Stages or Phases in the Change Management Process 

3.17. Negotiation and Conflict Management 

3.17.1 Negotiation 
3.17.2 Conflicts Management 
3.17.3 Crisis Management 

3.18. Executive Communication 

3.18.1. Internal and External Communication in the Corporate Environment 
3.18.2. Communication Departments 
3.18.3. The Person in Charge of Communication of the Company. The Profile of the Dircom 

3.19. Human Resources Management and PRL Teams 

3.19.1. Management of Human Resources and Teams
3.19.2. Prevention of Occupational Hazards

3.20. Productivity, Attraction, Retention and Activation of Talent 

3.20.1. Productivity 
3.20.2. Talent Attraction and Retention Levers 

3.21. Monetary Compensation Vs. Non-Cash

3.21.1. Monetary Compensation Vs. Non-Cash 
3.21.2. Wage Band Models 
3.21.3. Non-cash Compensation Models 
3.21.4. Working Model 
3.21.5. Corporate Community 
3.21.6. Company Image 
3.21.7. Emotional Salary 

3.22. Innovation in Talent and People Management II

3.22.1. Innovation in Organizations 
3.22.2. New Challenges in the Human Resources Department 
3.22.3. Innovation Management
3.22.4. Tools for Innovation

3.23. Knowledge and Talent Management 

3.23.1. Knowledge and Talent Management
3.23.2. Knowledge Management Implementation 

3.24. Transforming Human Resources in the Digital Era 

3.24.1. The Socioeconomic Context
3.24.2. New Forms of Corporate Organization
3.24.3. New Methodologies

Module 4.  Economic and Financial Management 

4.1. Economic Environment 

4.1.1. Macroeconomic Environment and the National Financial System 
4.1.2. Financial Institutions 
4.1.3. Financial Markets 
4.1.4. Financial Assets 
4.1.5. Other Financial Sector Entities 

4.2. Company Financing 

4.2.1. Sources of Financing 
4.2.2. Types of Financing Costs 

4.3. Executive Accounting 

4.3.1. Basic Concepts  
4.3.2. The Company's Assets  
4.3.3. The Company's Liabilities  
4.3.4. The Company's Net Worth  
4.3.5. The Income Statement  

4.4. From General Accounting to Cost Accounting 

4.4.1. Elements of Cost Calculation 
4.4.2. Expenses in General Accounting and Cost Accounting 
4.4.3. Costs Classification 

4.5. Information Systems and Business Intelligence 

4.5.1. Fundamentals and Classification 
4.5.2. Cost Allocation Phases and Methods 
4.5.3. Choice of Cost Center and Impact 

4.6. Budget and Management Control 

4.6.1. The Budget Model  
4.6.2. The Capital Budget 
4.6.3. The Operating Budget  
4.6.4. Treasury Budget  
4.6.5. Budget Monitoring  

4.7. Treasury Management 

4.7.1. Accounting Working Capital and Necessary Working Capital 
4.7.2. Calculation of Operating Requirementsof Funds 
4.7.3. Credit Management 

4.8. Corporate Tax Responsibility 

4.8.1. Basic Tax Concepts  
4.8.2. Corporate Income Tax  
4.8.3. Value Added Tax  
4.8.4. Other Taxes Related to Commercial with the Mercantile Activity  
4.8.5. The Company as a Facilitator of the Work of the of the State  

4.9. Systems of Controlof Enterprises 

4.9.1. Analysis of Financial Statements  
4.9.2. The Company's Balance Sheet  
4.9.3. The Profit and Loss Statement  
4.9.4. The Statement of Cash Flows  
4.9.5. Ratio Analysis  

4.10. Financial Management 

4.10.1. The Company's Financial Decisions  
4.10.2. Financial Department  
4.10.3. Cash Surpluses  
4.10.4. Risks Associated with Financial Management  
4.10.5. Financial Administration Risk Management  

4.11. Financial Planning 

4.11.1. Definition of Financial Planning 
4.11.2. Actions to be Taken in Financial Planning 
4.11.3. Creation and Establishment of the Business Strategy  
4.11.4. The Cash Flow Table 
4.11.5. The Working Capital Table 

4.12. Corporate Financial Strategy 

4.12.1. Corporate Strategy and Sources of Financing 
4.12.2. Financial Products for Corporate Financing  

4.13. Macroeconomic Context 

4.13.1. Macroeconomic Context 
4.13.2. Relevant Economic Indicators 
4.13.3. Mechanisms for Monitoring of Macroeconomic Magnitudes 
4.13.4. Economic Cycles  

4.14. Strategic Financing 

4.14.1. Self-Financing  
4.14.2. Increase in Equity  
4.14.3. Hybrid Resources  
4.14.4. Financing Through Intermediaries  

4.15. Money and Capital  Markets 

4.15.1. The Money Market  
4.15.2. The Fixed Income Market  
4.15.3. The Equity Market  
4.15.4. The Foreign Exchange Market  
4.15.5. The Derivatives Market  

4.16. Financial Analysis and Planning 

4.16.1. Analysis of the Balance Sheet 
4.16.2. Analysis of the Income Statement 
4.16.3. Profitability Analysis 

4.17. Analysis and Resolution of Cases/Problems 

4.17.1. Financial Information on Industria de Diseño y Textil, S.A. (INDITEX)

Module 5. Operations and Logistics Management 

5.1. Operations Direction and Management

5.1.1. The Role of Operations 
5.1.2. The Impact of Operations on the Management of Companies.  
5.1.3. Introduction to Operations Strategy 
5.1.4. Operations Management

5.2. Industrial Organization and Logistics 

5.2.1. Industrial Organization Department
5.2.2. Logistics Department

5.3. Structure and Types of Production (MTS, MTO, ATO, ETO, etc)  

5.3.1. Production System 
5.3.2. Production Strategy  
5.3.3. Inventory Management System
5.3.4. Production Indicators 

5.4. Structure and Types of Procurement  

5.4.1. Function of Procurement 
5.4.2. Procurement Management
5.4.3. Types of Purchases 
5.4.4. Efficient Purchasing Management of a Company 
5.4.5. Stages of the Purchase Decision Process 

5.5. Economic Control of Purchasing 

5.5.1. Economic Influence of Purchases
5.5.2. Cost Centers 
5.5.3. Budget
5.5.4. Budgeting vs. Actual Expenditure
5.5.5. Budgetary Control Tools

5.6. Warehouse Operations Control 

5.6.1. Inventory Control
5.6.2. Location Systems
5.6.3. Stock Management Techniques
5.6.4. Storage Systems

5.7. Strategic Purchasing Management

5.7.1. Business Strategy
5.7.2. Strategic Planning
5.7.3. Purchasing Strategies

5.8. Typologies of the Supply Chain (SCM) 

5.8.1. Supply Chain
5.8.2. Benefits of Supply Chain Management
5.8.3. Logistical Management in the Supply Chain

5.9. Supply Chain Management 

5.9.1. The Concept of Management of the Supply Chain (SCM)
5.9.2. Supply Chain Costs and Efficiency
5.9.3. Demand Patterns
5.9.4. Operations Strategy and Change

5.10. Interactions Between the SCM and All Other Departments

5.10.1. Interaction of the Supply Chain 
5.10.2. Interaction of the Supply Chain. Integration by Parts
5.10.3. Supply Chain Integration Problems 
5.10.4. Supply Chain

5.11. Logistics Costs 

5.11.1. Logistics Costs
5.11.2. Problems with Logistics Costs
5.11.3. Optimizing Logistic Costs  

5.12. Profitability and Efficiency of Logistics Chains: KPIS 

5.12.1. Logistics Chain
5.12.2. Profitability and Efficiency of the Logistics Chain
5.12.3. Indicators of Profitability and Efficiency of the Supply Chain

5.13. Process Management

5.13.1. Process Management 
5.13.2. Process-Based Approach: Process Mapping 
5.13.3. Improvements in Process Management 

5.14. Distribution and Transportation and Logistics

5.14.1. Distribution in the Supply Chain 
5.14.2. Transportation Logistics 
5.14.3. Geographic Information Systems as a Support to Logistics 

5.15. Logistics and Customers

5.15.1. Demand Analysis 
5.15.2. Demand and Sales Forecast 
5.15.3. Sales and Operations Planning
5.15.4. Participatory Planning, Forecasting and and Replenishment Planning (CPFR) 

5.16. International Logistics 

5.16.1. Export and Import Processes 
5.16.2. Customs 
5.16.3. Methods and Means of International Payment
5.16.4. International Logistics Platforms

5.17. Outsourcing of Operations

5.17.1. Operations Management and Outsourcing 
5.17.2. Outsourcing Implementation in Logistics Environments 

5.18. Competitiveness in Operations

5.18.1. Operations Management 
5.18.2. Operational Competitiveness 
5.18.3. Operations Strategy and Competitive Advantages 

5.19. Quality Management

5.19.1. Internal and External Customers
5.19.2. Quality Costs
5.19.3. Ongoing Improvement and the Deming Philosophy

Module 6. Information Systems Management

6.1. Technological Environment

6.1.1. Technology and Globalization 
6.1.2. Economic Environment and Technology 
6.1.3. Technological Environment and its Impact on Companies 

6.2. Information Systems and Technologies in the Enterprise 

6.2.1. The Evolution of the IT Model
6.2.2. Organization and IT Departments
6.2.3. Information Technology and Economic Environment

6.3. Corporate Strategy and Technology Strategy 

6.3.1. Creating Value for Customers and Shareholders
6.3.2. Strategic IS/IT Decisions
6.3.3. Corporate Strategy Vs. Technology and Digital Strategy

6.4. Information Systems Management

6.4.1. Corporate Governance of Technology and Information Systems 
6.4.2. Management of Information Systems in Companies 
6.4.3. Expert Managers in Information Systems: Roles and Functions 

6.5. Information Technology Strategic Planning

6.5.1. Information Systems and Corporate Strategy
6.5.2. Strategic Planning of Information Systems  
6.5.3. Phases of Information Systems Strategic Planning 

6.6. Information Systems for Decision-Making

6.6.1. Business Intelligence
6.6.2. Data Warehouse
6.6.3. BSC or Balanced Scorecard

6.7. Exploring the Information

6.7.1. SQL: Relational Databases Basic Concepts
6.7.2. Networks and Communications
6.7.3. Operational System: Standardized Data Models 
6.7.4. Strategic System: OLAP, Multidimensional Model and Graphical Dashboards 
6. 7.5. Strategic DB Analysis and Report Composition 

6.8. Enterprise Business Intelligence

6.8.1. The World of Data
6.8.2. Relevant Concepts
6.8.3. Main Characteristics
6.8.4. Solutions in Today's Market
6.8.5. Overall Architecture of a BI Solution
6.8.6. Cybersecurity in BI and Data Science

6.9. New Business Concept  

6.9.1. Why BI
6.9.2. Obtaining Information
6.9.3. BI in the Different Departments of the Company
6.9.4. Reasons to Invest in BI

6.10. BI Tools and Solutions 

6.10.1. How to Choose the Best Tool?
6.10.2. Microsoft Power BI, MicroStrategy y Tableau
6.10.3. SAP BI, SAS BI and Qlikview
6.10.4. Prometheus

6.11. BI Project Planning and Management  

6.11.1. First Steps to Define a BI Project
6.11.2. BI Solution for the Company
6.11.3. Requirements and Objectives 

6.12. Corporate Management Applications 

6.12.1. Information Systems and Corporate Management 
6.12.2. Applications for Corporate Management 
6.12.3. Enterprise Resource Planning or ERP Systems 

6.13. Digital Transformation

6.13.1. Conceptual Framework of Digital Transformation 
6.13.2. Digital Transformation; Key Elements, Benefits and Drawbacks 
6.13.3. Digital Transformation in Companies 

6.14. Technology and Trends

6.14.1. Main Trends in the Field of Technology that are Changing Business Models 
6.14.2. Analysis of the Main Emerging Technologies 

6.15. IT Outsourcing

6.15.1. Conceptual Framework of Outsourcing 
6.15.2. IT Outsourcing and its Impact on the Business 
6.15.3. Keys to Implement Corporate IT Outsourcing Projects

Module 7. Commercial Management, Strategic Marketing and Corporate Communication

7.1. Commercial Management

7.1.1. Conceptual Framework of Commercial Management
7.1.2. Business Strategy and Planning
7.1.3. The Role of Sales Managers

7.2. Marketing 

7.2.1. The Concept of Marketing
7.2.2. Basic Elements of Marketing
7.2.3. Marketing Activities of the Company

7.3. Strategic Marketing Management

7.3.1. The Concept of Strategic Marketing
7.3.2. Concept of Strategic Marketing Planning
7.3.3. Stages in the Process of Strategic Marketing Planning

7.4. Digital Marketing and E-Commerce

7.4.1. Digital Marketing and E-Commerce Objectives  
7.4.2. Digital Marketing and Media Used 
7.4.3. E-Commerce General Context 
7.4.4. Categories of E-Commerce 
7.4.5. Advantages and Disadvantages of E-Commerce Versus Traditional Commerce. 

7.5. Managing Digital Business

7.5.1. Competitive Strategy in the Face of the Growing Digitalization of the Media 
7.5.2. Design and Creation of a Digital Marketing Plan 
7.5.3. ROI Analysis in a Digital Marketing Plan 

7.6. Digital Marketing to Reinforce the Brand

7.6.1. Online Strategies to Improve Your Brand's Reputation
7.6.2. Branded Content and Storytelling

7.7. Digital Marketing Strategy

7.7.1. Defining the Digital Marketing Strategy 
7.7.2. Digital Marketing Strategy Tools 

7.8. Digital Marketing to Attract and Retain Customers 

7.8.1. Loyalty and Engagement Strategies Through the Internet
7.8.2. Visitor Relationship Management
7.8.3. Hypersegmentation

7.9. Managing Digital Campaigns

7.9.1. What is a Digital Advertising Campaign?
7.9.2. Steps to Launch an Online Marketing Campaign
7.9.3. Mistakes in Digital Advertising Campaigns

7.10. Online Marketing Plan

7.10.1. What is an Online Marketing Plan?
7.10.2. Steps to Create an Online Marketing Plan
7.10.3. Advantages of Having an Online Marketing Plan

7.11. Blended Marketing

7.11.1. What is Blended Marketing?
7.11.2. Differences Between Online and Offline Marketing
7.11.3. Aspects to be Taken into Account in the Blended Marketing Strategy 
7.11.4. Characteristics of a Blended Marketing Strategy
7.11.5. Recommendations in Blended Marketing
7.11.6. Benefits of Blended Marketing

7.12. Sales Strategy 

7.12.1. Sales Strategy 
7.12.2. Sales Methods

7.13. Corporate Communication 

7.13.1. Concept
7.13.2. The Importance of Communication in the Organization
7.13.3. Type of Communication in the Organization
7.13.4. Functions of Communication in the Organization
7.13.5. Elements of Communication
7.13.6. Communication Problems
7.13.7. Communication Scenarios

7.14. Corporate Communication Strategy 

7.14.1. Motivational Programs, Social Action, Participation and Training with HR 
7.14.2. Internal Communication Tools and Supports
7.14.3. Internal Communication Plan

7.15. Digital Communication and Reputation

7.15.1. Online Reputation 
7.15.2. How to Measure Digital Reputation? 
7.15.3. Online Reputation Tools 
7.15.4. Online Reputation Report 
7.15.5. Online Branding

Module 8. Market Research, Advertising and Commercial Management

8.1. Market Research 

8.1.1. Marketing Research: Historical Origin  
8.1.2. Analysis and Evolution of the Conceptual Framework of Marketing Research  
8.1.3. Key Elements and Value Contribution of Market Research  

8.2. Quantitative Research Methods and Techniques 

8.2.1. Sample Size  
8.2.2. Sampling  
8.2.3. Types of Quantitative Techniques   

8.3. Qualitative Research Methods and Techniques 

8.3.1. Types of Qualitative Research 
8.3.2. Qualitative Research Techniques 

8.4. Market Segmentation 

8.4.1. Market Segmentation Concept  
8.4.2. Utility and Segmentation Requirements  
8.4.3. Consumer Market Segmentation  
8.4.4. Industrial Market Segmentation  
8.4.5. Segmentation Strategies  
8.4.6. Segmentation Based on Marketing - Mix Criteria  
8.4.7. Market Segmentation Methodology 

8.5. Research Project Management 

8.5.1. Market Research as a Process 
8.5.2. Planning Stages in Market Research 
8.5.3. Stages of Market Research Implementation 
8.5.4. Managing a Research Project  

8.6. International Market Research  

8.6.1. International Market Research 
8.6.2. International Market Research Process 
8.6.3. The Importance of Secondary Sources in International Market Research 

8.7. Feasibility Studies    

8.7.1. Concept and Usefulness 
8.7.2. Outline of a Feasibility Study 
8.7.3. Development of a Feasibility Study 

8.8. Publicity 

8.8.1. Historical Background of Advertising  
8.8.2. Conceptual Framework of Advertising; Principles, Concept of Briefing and Positioning  
8.8.3. Advertising Agencies, Media Agencies and Advertising Professionals  
8.8.4. Importance of Advertising in Business 
8.8.5. Advertising Trends and Challenges  

8.9. Developing the Marketing Plan  

8.9.1. Marketing Plan Concept 
8.9.2. Situation Analysis and Diagnosis 
8.9.3. Strategic Marketing Decisions 
8.9.4. Operational Marketing Decisions 

8.10. Promotion and Merchandising Strategies 

8.10.1. Integrated Marketing Communication 
8.10.2. Advertising Communication Plan 
8.10.3. Merchandising as a Communication Technique 

8.11. Media Planning  

8.11.1. Origin and Evolution of Media Planning   
8.11.2. Media  
8.11.3. Media Plan  

8.12. Fundamentals of Commercial Management  

8.12.1. The Role of Commercial Management 
8.12.2. Systems of Analysis of the Company/Market Commercial Competitive Situation 
8.12.3. Commercial Planning Systems of the Company 
8.12.4. Main Competitive Strategies 

8.13. Commercial Negotiation 

8.13.1. Commercial Negotiation   
8.13.2. Psychological Issues in Negotiation 
8.13.3. Main Negotiation Methods 
8.13.4. The Negotiation Process 

8.14. Decision-Making in Commercial Management 

8.14.1. Commercial Strategy and Competitive Strategy 
8.14.2. Decision Making Models 
8.14.3. Decision-Making Analytics and Tools 
8.14.4. Human Behavior in Decision Making 

8.15. Leadership and Management of the Sales Network 

8.15.1. Sales Management Sales Management 
8.15.2. Networks Serving Commercial Activity 
8.15.3. Salesperson Recruitment and Training Policies 
8.15.4. Remuneration Systems for Own and External Commercial Networks 
8.15.5. Management of the Commercial Process Control and Assistance to the Work of the Sales Representatives Based on the Information 

8.16. Implementing the Commercial Function 

8.16.1. Recruitment of Own Sales Representatives and Sales Agents 
8.16.2. Controlling Commercial Activity  
8.16.3. The Code of Ethics of Sales Personnel 
8.16.4. Compliance with Legislation 
8.16.5. Generally Accepted Standards of Business Conduct 

8.17. Key Account Management 

8.17.1. Concept of Key Account Management   
8.17.2. The Key Account Manager 
8.17.3. Key Account Management Strategy  

8.18. Financial and Budgetary Management  

8.18.1. The Break-Even Point 
8.18.2. The Sales Budget Control of Management and of the Annual Sales Plan 
8.18.3. Financial Impact of Strategic Sales Decisions 
8.18.4. Cycle Management, Turnover, Profitability and Liquidity
8.18.5. Income Statement

Module 9. Innovation and Project Management

9.1. Innovation

9.1.1. Introduction to Innovation 
9.1.2. Innovation in the Entrepreneurial Ecosystem 
9.1.3. Instruments and Tools for the Business Innovation Process 

9.2. Innovation Strategy

9.2.1. Strategic Intelligence and Innovation 
9.2.2. Innovation from Strategy 

9.3. Project Management for Startups

9.3.1. Startup Concept
9.3.2. Lean Startup Philosophy
9.3.3. Stages of Startup Development
9.3.4. The Role of a Project Manager in a Startup

9.4. Business Model Design and Validation

9.4.1. Conceptual Framework of a Business Model 
9.4.2. Business Model Design and Validation 

9.5. Project Management

9.5.1. Project Management: Identification of Opportunities to Develop Corporate Innovation Projects 
9.5.2. Main stages or Phases in the Direction and Management of Innovation Projects 

9.6. Project Change Management: Training Management 

9.6.1. Concept of Change Management
9.6.2. The Change Management Process
9.6.3. Change Implementation

9.7. Project Communication Management

9.7.1. Project Communications Management
9.7.2. Key Concepts for Project Communications Management
9.7.3. Emerging Trends
9.7.4. Adaptations to Equipment
9.7.5. Planning Communications Management
9.7.6. Manage Communications
9.7.7. Monitoring Communications

9.8. Traditional and Innovative Methodologies

9.8.1. Innovative Methodologies
9.8.2. Basic Principles of Scrum
9.8.3. Differences between the Main Aspects of Scrum and Traditional Methodologies

9.9. Creation of a Startup

9.9.1. Creation of a Startup 
9.9.2. Organization and Culture
9.9.3. Top Ten Reasons Why Startups Fail 
9.9.4. Legal Aspects

9.10. Project Risk Management Planning

9.10.1. Risk Planning
9.10.2. Elements for Creating a Risk Management Plan
9.10.3. Tools for Creating a Risk Management Plan
9.10.4. Content of the Risk Management Plan

Module 10. Executive Management

10.1. General Management

10.1.1. The Concept of General Management  
10.1.2. The General Manager's Action 
10.1.3. The CEO and their Responsibilities 
10.1.4. Transforming the Work of Management 

10.2. Manager Functions: Organizational Culture and Approaches 

10.2.1. Manager Functions: Organizational Culture and Approaches 

10.3. Operations Management  

10.3.1. The Importance of Management 
10.3.2. Value Chain 
10.3.3. Quality Management 

10.4. Public Speaking and Spokesperson Education 

10.4.1. Interpersonal Communication 
10.4.2. Communication Skills and Influence 
10.4.3. Communication Barriers 

10.5. Personal and Organizational Communications Tools 

10.5.1. Interpersonal Communication 
10.5.2. Interpersonal Communication Tools 
10.5.3. Communication in the Organization 
10.5.4. Tools in the Organization 

10.6. Communication in Crisis Situations 

10.6.1. Crisis 
10.6.2. Phases of the Crisis 
10.6.3. Messages: Contents and Moments 

10.7. Preparation of a Crisis Plan 

10.7.1. Analysis of Possible Problems 
10.7.2. Planning 
10.7.3. Adequacy of Personnel 

10.8. Emotional Intelligence  

10.8.1. Emotional Intelligence and Communication 
10.8.2. Assertiveness, Empathy, and Active Listening 
10.8.3. Self-Esteem and Emotional Communication 

10.9. Personal Branding 

10.9.1. Strategies to Develop Personal Branding 
10.9.2. Personal Branding Laws 
10.9.3. Tools for Creating Personal Brands 

10.10. Leadership and Team Management 

10.10.1. Leadership and Leadership Styles 
10.10.2. Leader Capabilities and Challenges 
10.10.3. Managing Change Processes 
10.10.4. Managing Multicultural Teams

Module 11. Fundamentals of Artificial Intelligence

11.1. History of Artificial Intelligence 

11.1.1. When Do We Start Talking About Artificial Intelligence?  
11.1.2. References in Film 
11.1.3. Importance of Artificial Intelligence 
11.1.4. Technologies that Enable and Support Artificial Intelligence 

11.2. Artificial Intelligence in Games 

11.2.1. Game Theory 
11.2.2. Minimax and Alpha-Beta Pruning 
11.2.3. Simulation: Monte Carlo 

11.3. Neural Networks 

11.3.1. Biological Fundamentals 
11.3.2. Computational Model 
11.3.3. Supervised and Unsupervised Neural Networks 
11.3.4. Simple Perceptron 
11.3.5. Multilayer Perceptron 

11.4. Genetic Algorithms 

11.4.1. History 
11.4.2. Biological Basis 
11.4.3. Problem Coding 
11.4.4. Generation of the Initial Population 
11.4.5. Main Algorithm and Genetic Operators 
11.4.6. Evaluation of Individuals: Fitness 

11.5. Thesauri, Vocabularies, Taxonomies 

11.5.1. Vocabulary 
11.5.2. Taxonomy 
11.5.3. Thesauri 
11.5.4. Ontologies 
11.5.5. Knowledge Representation Semantic Web 

11.6. Semantic Web 

11.6.1. Specifications RDF, RDFS and OWL 
11.6.2. Inference/ Reasoning 
11.6.3. Linked Data 

11.7. Expert Systems and DSS 

11.7.1. Expert Systems 
11.7.2. Decision Support Systems 

11.8. Chatbots and Virtual Assistants  

11.8.1. Types of Assistants: Voice and Text Assistants  
11.8.2. Fundamental Parts for the Development of an Assistant: Intents, Entities and Dialogue Flow 
11.8.3. Integrations: Web, Slack, WhatsApp, Facebook 
11.8.4. Assistant Development Tools: Dialog Flow, Watson Assistant  

11.9. AI Implementation Strategy 
11.10. Future of Artificial Intelligence  

11.10.1. Understand How to Detect Emotions Using Algorithms  
11.10.2. Creating a Personality: Language, Expressions and Content  
11.10.3. Trends of Artificial Intelligence  
11.10.4. Reflections 

Module 12. Data Types and Life Cycle

12.1. Statistics  

12.1.1. Statistics: Descriptive Statistics, Statistical Inferences  
12.1.2. Population, Sample, Individual  
12.1.3. Variables: Definition, Measurement Scales  

12.2. Types of Data Statistics  

12.2.1. According to Type  

12.2.1.1. Quantitative: Continuous Data and Discrete Data  
12.2.1.2. Qualitative. Binomial Data, Nominal Data and Ordinal Data 

12.2.2. According to their Shape   

12.2.2.1. Numeric  
12.2.2.2. Text:   
12.2.2.3. Logical  

12.2.3. According to its Source  

12.2.3.1. Primary  
12.2.3.2. Secondary  

12.3. Life Cycle of Data  

12.3.1. Stages of the Cycle  
12.3.2. Milestones of the Cycle  
12.3.3. FAIR Principles  

12.4. Initial Stages of the Cycle  

12.4.1. Definition of Goals  
12.4.2. Determination of Resource Requirements  
12.4.3. Gantt Chart  
12.4.4. Data Structure  

12.5. Data Collection  

12.5.1. Methodology of Data Collection  
12.5.2. Data Collection Tools  
12.5.3. Data Collection Channels  

12.6. Data Cleaning  

12.6.1. Phases of Data Cleansing  
12.6.2. Data Quality  
12.6.3. Data Manipulation (with R)  

12.7. Data Analysis, Interpretation and Result Evaluation  

12.7.1. Statistical Measures  
12.7.2. Relationship Indexes  
12.7.3. Data Mining  

12.8. Datawarehouse  

12.8.1. Elements that Comprise it  
12.8.2. Design  
12.8.3. Aspects to Consider  

12.9. Data Availability  

12.9.1. Access  
12.9.2. Uses  
12.9.3. Security  

12.10. Regulatory Framework 

12.10.1. Data Protection Law  
12.10.2. Good Practices  
12.10.3. Other Regulatory Aspects 

Module 13. Data in Artificial Intelligence

13.1. Data Science 

13.1.1. Data Science 
13.1.2. Advanced Tools for the Data Scientist 

13.2. Data, Information and Knowledge 

13.2.1. Data, Information and Knowledge  
13.2.2. Types of Data 
13.2.3. Data Sources 

13.3. From Data to Information  

13.3.1. Data Analysis 
13.3.2. Types of Analysis 
13.3.3. Extraction of Information from a Dataset 

13.4. Extraction of Information Through Visualization 

13.4.1. Visualization as an Analysis Tool 
13.4.2. Visualization Methods  
13.4.3. Visualization of a Data Set 

13.5. Data Quality 

13.5.1. Quality Data 
13.5.2. Data Cleaning  
13.5.3. Basic Data Pre-Processing 

13.6. Dataset 

13.6.1. Dataset Enrichment 
13.6.2. The Curse of Dimensionality 
13.6.3. Modification of Our Data Set 

13.7. Unbalance  

13.7.1. Classes of Unbalance 
13.7.2. Unbalance Mitigation Techniques 
13.7.3. Balancing a Dataset 

13.8. Unsupervised Models  

13.8.1. Unsupervised Model 
13.8.2. Methods 
13.8.3. Classification with Unsupervised Models 

13.9. Supervised Models 

13.9.1. Supervised Model 
13.9.2. Methods 
13.9.3. Classification with Supervised Models 

13.10. Tools and Good Practices 

13.10.1. Good Practices for Data Scientists 
13.10.2. The Best Model  
13.10.3. Useful Tools 

Module 14. Data Mining Selection, Pre-Processing and Transformation

14.1. Statistical Inference 

14.1.1. Descriptive Statistics vs. Statistical Inference 
14.1.2. Parametric Procedures 
14.1.3. Non-Parametric Procedures 

14.2. Exploratory Analysis 

14.2.1. Descriptive Analysis  
14.2.2. Visualization 
14.2.3. Data Preparation 

14.3. Data Preparation 

14.3.1. Integration and Data Cleaning  
14.3.2. Normalization of Data 
14.3.3. Transforming Attributes  

14.4. Missing Values 

14.4.1. Treatment of Missing Values 
14.4.2. Maximum Likelihood Imputation Methods 
14.4.3. Missing Value Imputation Using Machine Learning 

14.5. Noise in the Data  

14.5.1. Noise Classes and Attributes 
14.5.2. Noise Filtering  
14.5.3. The Effect of Noise 

14.6. The Curse of Dimensionality 

14.6.1. Oversampling 
14.6.2. Undersampling 
14.6.3. Multidimensional Data Reduction 

14.7. From Continuous to Discrete Attributes 

14.7.1. Continuous Data Vs. Discreet Data 
14.7.2. Discretization Process 

14.8. The Data  

14.8.1. Data Selection  
14.8.2. Prospects and Selection Criteria 
14.8.3. Selection Methods  

14.9. Instance Selection 

14.9.1. Methods for Instance Selection 
14.9.2. Prototype Selection 
14.9.3. Advanced Methods for Instance Selection 

14.10. Data Pre-Processing in Big Data Environments 

Module 15. Algorithm and Complexity in Artificial Intelligence

15.1. Introduction to Algorithm Design Strategies 

15.1.1. Recursion 
15.1.2. Divide and Conquer 
15.1.3. Other Strategies 

15.2. Efficiency and Analysis of Algorithms 

15.2.1. Efficiency Measures 
15.2.2. Measuring the Size of the Input 
15.2.3. Measuring Execution Time 
15.2.4. Worst, Best and Average Case 
15.2.5. Asymptotic Notation 
15.2.6. Criteria for Mathematical Analysis of Non-Recursive Algorithms 
15.2.7. Mathematical Analysis of Recursive Algorithms 
15.2.8. Empirical Analysis of Algorithms 

15.3. Sorting Algorithms 

15.3.1. Concept of Sorting 
15.3.2. Bubble Sorting 
15.3.3. Sorting by Selection 
15.3.4. Sorting by Insertion 
15.3.5. Merge Sort 
15.3.6. Quick Sort 

15.4. Algorithms with Trees 

15.4.1. Tree Concept 
15.4.2. Binary Trees 
15.4.3. Tree Paths 
15.4.4. Representing Expressions 
15.4.5. Ordered Binary Trees 
15.4.6. Balanced Binary Trees 

15.5. Algorithms Using Heaps 

15.5.1. Heaps 
15.5.2. The Heapsort Algorithm 
15.5.3. Priority Queues 

15.6. Graph Algorithms 

15.6.1. Representation 
15.6.2. Traversal in Width 
15.6.3. Depth Travel 
15.6.4. Topological Sorting 

15.7. Greedy Algorithms 

15.7.1. Greedy Strategy 
15.7.2. Elements of the Greedy Strategy 
15.7.3. Currency Exchange 
15.7.4. Traveler’s Problem 
15.7.5. Backpack Problem 

15.8. Minimal Path Finding 

15.8.1. The Minimum Path Problem 
15.8.2. Negative Arcs and Cycles 
15.8.3. Dijkstra's Algorithm 

15.9. Greedy Algorithms on Graphs 

15.9.1. The Minimum Covering Tree 
15.9.2. Prim's Algorithm 
15.9.3. Kruskal’s Algorithm 
15.9.4. Complexity Analysis 

15.10. Backtracking 

15.10.1. Backtracking 
15.10.2. Alternative Techniques 

Module 16. Intelligent Systems

16.1. Agent Theory 

16.1.1. Concept History 
16.1.2. Agent Definition 
16.1.3. Agents in Artificial Intelligence 
16.1.4. Agents in Software Engineering 

16.2. Agent Architectures 

16.2.1. The Reasoning Process of an Agent 
16.2.2. Reactive Agents 
16.2.3. Deductive Agents 
16.2.4. Hybrid Agents 
16.2.5. Comparison 

16.3. Information and Knowledge 

16.3.1. Difference between Data, Information and Knowledge 
16.3.2. Data Quality Assessment 
16.3.3. Data Collection Methods 
16.3.4. Information Acquisition Methods 
16.3.5. Knowledge Acquisition Methods 

16.4. Knowledge Representation 

16.4.1. The Importance of Knowledge Representation 
16.4.2. Definition of Knowledge Representation According to Roles 
16.4.3. Knowledge Representation Features 

16.5. Ontologies 

16.5.1. Introduction to Metadata 
16.5.2. Philosophical Concept of Ontology 
16.5.3. Computing Concept of Ontology 
16.5.4. Domain Ontologies and Higher-Level Ontologies 
16.5.5. How to Build an Ontology? 

16.6. Ontology Languages and Ontology Creation Software 

16.6.1. Triple RDF, Turtle and N 
16.6.2. RDF Schema 
16.6.3. OWL 
16.6.4. SPARQL 
16.6.5. Introduction to Ontology Creation Tools 
16.6.6. Installing and Using Protégé 

16.7. Semantic Web 

16.7.1. Current and Future Status of the Semantic Web 
16.7.2. Semantic Web Applications 

16.8. Other Knowledge Representation Models 

16.8.1. Vocabulary 
16.8.2. Global Vision 
16.8.3. Taxonomy 
16.8.4. Thesauri 
16.8.5. Folksonomy 
16.8.6. Comparison 
16.8.7. Mind Maps 

16.9. Knowledge Representation Assessment and Integration 

16.9.1. Zero-Order Logic 
16.9.2. First-Order Logic 
16.9.3. Descriptive Logic 
16.9.4. Relationship between Different Types of Logic 
16.9.5. Prolog: Programming Based on First-Order Logic 

16.10. Semantic Reasoners, Knowledge-Based Systems and Expert Systems 

16.10.1. Concept of Reasoner 
16.10.2. Reasoner Applications 
16.10.3. Knowledge-Based Systems 
16.10.4. MYCIN: History of Expert Systems 
16.10.5. Expert Systems Elements and Architecture 
16.10.6. Creating Expert Systems 

Module 17. Machine Learning and Data Mining

17.1. Introduction to Knowledge Discovery Processes and Basic Concepts of Machine Learning 

17.1.1. Key Concepts of Knowledge Discovery Processes 
17.1.2. Historical Perspective of Knowledge Discovery Processes 
17.1.3. Stages of the Knowledge Discovery Processes 
17.1.4. Techniques Used in Knowledge Discovery Processes 
17.1.5. Characteristics of Good Machine Learning Models 
17.1.6. Types of Machine Learning Information 
17.1.7. Basic Learning Concepts 
17.1.8. Basic Concepts of Unsupervised Learning 

17.2. Data Exploration and Pre-Processing 

17.2.1. Data Processing 
17.2.2. Data Processing in the Data Analysis Flow 
17.2.3. Types of Data 
17.2.4. Data Transformations 
17.2.5. Visualization and Exploration of Continuous Variables 
17.2.6. Visualization and Exploration of Categorical Variables 
17.2.7. Correlation Measures 
17.2.8. Most Common Graphic Representations 
17.2.9. Introduction to Multivariate Analysis and Dimensionality Reduction 

17.3. Decision Trees 

17.3.1. ID Algorithm 
17.3.2. Algorithm C 
17.3.3. Overtraining and Pruning 
17.3.4. Result Analysis 

17.4. Evaluation of Classifiers 

17.4.1. Confusion Matrixes 
17.4.2. Numerical Evaluation Matrixes 
17.4.3. Kappa Statistic 
17.4.4. ROC Curves 

17.5. Classification Rules 

17.5.1. Rule Evaluation Measures 
17.5.2. Introduction to Graphic Representation 
17.5.3. Sequential Overlay Algorithm 

17.6. Neural Networks 

17.6.1. Basic Concepts 
17.6.2. Simple Neural Networks 
17.6.3. Backpropagation Algorithm 
17.6.4. Introduction to Recurrent Neural Networks 

17.7. Bayesian Methods 

17.7.1. Basic Probability Concepts 
17.7.2. Bayes' Theorem 
17.7.3. Naive Bayes 
17.7.4. Introduction to Bayesian Networks 

17.8. Regression and Continuous Response Models 

17.8.1. Simple Linear Regression 
17.8.2. Multiple Linear Regression 
17.8.3. Logistic Regression 
17.8.4. Regression Trees 
17.8.5. Introduction to Support Vector Machines (SVM) 
17.8.6. Goodness-of-Fit Measures 

17.9. Clustering 

17.9.1. Basic Concepts 
17.9.2. Hierarchical Clustering 
17.9.3. Probabilistic Methods 
17.9.4. EM Algorithm 
17.9.5. B-Cubed Method 
17.9.6. Implicit Methods 

17.10. Text Mining and Natural Language Processing (NLP) 

17.10.1. Basic Concepts 
17.10.2. Corpus Creation 
17.10.3. Descriptive Analysis 
17.10.4. Introduction to Feelings Analysis 

Module 18. Neural Networks, the Basis of Deep Learning

18.1. Deep Learning 

18.1.1. Types of Deep Learning 
18.1.2. Applications of Deep Learning 
18.1.3. Advantages and Disadvantages of Deep Learning 

18.2. Surgery 

18.2.1. Sum 
18.2.2. Product 
18.2.3. Transfer 

18.3. Layers 

18.3.1. Input Layer 
18.3.2. Cloak 
18.3.3. Output Layer 

18.4. Layer Bonding and Operations 

18.4.1. Architecture Design 
18.4.2. Connection between Layers 
18.4.3. Forward Propagation 

18.5. Construction of the First Neural Network 

18.5.1. Network Design 
18.5.2. Establish the Weights 
18.5.3. Network Training 

18.6. Trainer and Optimizer 

18.6.1. Optimizer Selection 
18.6.2. Establishment of a Loss Function 
18.6.3. Establishing a Metric 

18.7. Application of the Principles of Neural Networks 

18.7.1. Activation Functions 
18.7.2. Backward Propagation 
18.7.3. Parameter Adjustment 

18.8. From Biological to Artificial Neurons 

18.8.1. Functioning of a Biological Neuron 
18.8.2. Transfer of Knowledge to Artificial Neurons 
18.8.3. Establish Relations Between the Two 

18.9. Implementation of MLP (Multilayer Perceptron) with Keras 

18.9.1. Definition of the Network Structure 
18.9.2. Model Compilation 
18.9.3. Model Training 

18.10. Fine Tuning  Hyperparameters of Neural Networks 

18.10.1. Selection of the Activation Function 
18.10.2. Set the Learning Rate 
18.10.3. Adjustment of Weights 

Module 19. Deep Neural Networks Training

19.1. Gradient Problems 

19.1.1. Gradient Optimization Techniques 
19.1.2. Stochastic Gradients 
19.1.3. Weight Initialization Techniques 

19.2. Reuse of Pre-Trained Layers 

19.2.1. Learning Transfer Training 
19.2.2. Feature Extraction 
19.2.3. Deep Learning 

19.3. Optimizers 

19.3.1. Stochastic Gradient Descent Optimizers 
19.3.2. Optimizers Adam and RMSprop 
19.3.3. Moment Optimizers 

19.4. Programming of the Learning Rate 

19.4.1. Automatic Learning Rate Control 
19.4.2. Learning Cycles 
19.4.3. Smoothing Terms 

19.5. Overfitting 

19.5.1. Cross Validation 
19.5.2. Regularization 
19.5.3. Evaluation Metrics 

19.6. Practical Guidelines 

19.6.1. Model Design 
19.6.2. Selection of Metrics and Evaluation Parameters 
19.6.3. Hypothesis Testing 

19.7. Transfer Learning 

19.7.1. Learning Transfer Training 
19.7.2. Feature Extraction 
19.7.3. Deep Learning 

19.8. Data Augmentation 

19.8.1. Image Transformations 
19.8.2. Synthetic Data Generation 
19.8.3. Text Transformation 

19.9. Practical Application of Transfer Learning 

19.9.1. Learning Transfer Training 
19.9.2. Feature Extraction 
19.9.3. Deep Learning 

19.10. Regularization 

19.10.1. L and L 
19.10.2. Regularization by Maximum Entropy 
19.10.3. Dropout 

Module 20. Model Customization and Training with TensorFlow

20.1. TensorFlow 

20.1.1. Use of the TensorFlow Library 
20.1.2. Model Training with TensorFlow 
20.1.3. Operations with Graphs in TensorFlow 

20.2. TensorFlow and NumPy 

20.2.1. NumPy Computing Environment for TensorFlow 
20.2.2. Using NumPy Arrays with TensorFlow 
20.2.3. NumPy Operations for TensorFlow Graphs 

20.3. Model Customization and Training Algorithms 

20.3.1. Building Custom Models with TensorFlow 
20.3.2. Management of Training Parameters 
20.3.3. Use of Optimization Techniques for Training 

20.4. TensorFlow Features and Graphs 

20.4.1. Functions with TensorFlow 
20.4.2. Use of Graphs for Model Training 
20.4.3. Grap Optimization with TensorFlow Operations 

20.5. Loading and Preprocessing Data with TensorFlow 

20.5.1. Loading Data Sets with TensorFlow 
20.5.2. Preprocessing Data with TensorFlow 
20.5.3. Using TensorFlow Tools for Data Manipulation 

20.6. The Tf.data API 

20.6.1. Using the Tf.data API for Data Processing 
20.6.2. Construction of Data Streams with Tf.data 
20.6.3. Using the Tf.data API for Model Training 

20.7. The TFRecord Format 

20.7.1. Using the TFRecord API for Data Serialization 
20.7.2. TFRecord File Upload with TensorFlow 
20.7.3. Using TFRecord Files for Model Training 

20.8. Keras Preprocessing Layers 

20.8.1. Using the Keras Preprocessing API 
20.8.2. Preprocessing Pipelined Construction with Keras 
20.8.3. Using the Keras Preprocessing API for Model Training 

20.9. The TensorFlow Datasets Project 

20.9.1. Using TensorFlow Datasets for Data Loading 
20.9.2. Preprocessing Data with TensorFlow Datasets 
20.9.3. Using TensorFlow Datasets for Model Training 

20.10. Building a Deep Learning App with TensorFlow 

20.10.1. Practical Applications 
20.10.2. Building a Deep Learning App with TensorFlow 
20.10.3. Model Training with TensorFlow 
20.10.4. Use of the Application for the Prediction of Results 

Module 21. Deep Computer Vision with Convolutional Neural Networks 

21.1. The Visual Cortex Architecture 

21.1.1. Functions of the Visual Cortex 
21.1.2. Theories of Computational Vision 
21.1.3. Models of Image Processing 

21.2. Convolutional Layers 

21.2.1. Reuse of Weights in Convolution 
21.2.2. Convolution D 
21.2.3. Activation Functions 

21.3. Grouping Layers and Implementation of Grouping Layers with Keras 

21.3.1. Pooling and Striding 
21.3.2. Flattening 
21.3.3. Types of Pooling 

21.4. CNN Architecture 

21.4.1. VGG Architecture 
21.4.2. AlexNet Architecture 
21.4.3. ResNet Architecture 

21.5. Implementing a CNN ResNet using Keras 

21.5.1. Weight Initialization 
21.5.2. Input Layer Definition 
21.5.3. Output Definition 

21.6. Use of Pre-Trained Keras Models 

21.6.1. Characteristics of Pre-Trained Models 
21.6.2. Uses of Pre-Trained Models 
21.6.3. Advantages of Pre-Trained Models 

21.7. Pre-Trained Models for Transfer Learning 

21.7.1. Learning by Transfer 
21.7.2. Transfer Learning Process 
21.7.3. Advantages of Transfer Learning 

21.8. Deep Computer Vision Classification and Localization 

21.8.1. Image Classification 
21.8.2. Localization of Objects in Images 
21.8.3. Object Detection 

21.9. Object Detection and Object Tracking 

21.9.1. Object Detection Methods 
21.9.2. Object Tracking Algorithms 
21.9.3. Tracking and Localization Techniques 

21.10. Semantic Segmentation 

21.10.1. Deep Learning for Semantic Segmentation 
21.10.1. Edge Detection 
21.10.1. Rule-Based Segmentation Methods 

Module 22. Natural Language Processing (NLP) with Recurrent Neural Networks (RNN) and Attention

22.1. Text Generation using RNN 

22.1.1. Training an RNN for Text Generation 
22.1.2. Natural Language Generation with RNN 
22.1.3. Text Generation Applications with RNN 

22.2. Training Data Set Creation 

22.2.1. Preparation of the Data for Training an RNN 
22.2.2. Storage of the Training Dataset 
22.2.3. Data Cleaning and Transformation 
22.2.4. Sentiment Analysis 

22.3. Classification of Opinions with RNN 

22.3.1. Detection of Themes in Comments 
22.3.2. Sentiment Analysis with Deep Learning Algorithms 

22.4. Encoder-Decoder Network for Neural Machine Translation 

22.4.1. Training an RNN for Machine Translation 
22.4.2. Use of an Encoder-Decoder Network for Machine Translation 
22.4.3. Improving the Accuracy of Machine Translation with RNNs 

22.5. Attention Mechanisms 

22.5.1. Application of Care Mechanisms in RNN 
22.5.2. Use of Care Mechanisms to Improve the Accuracy of the Models 
22.5.3. Advantages of Attention Mechanisms in Neural Networks 

22.6. Transformer Models 

22.6.1. Using Transformers Models for Natural Language Processing 
22.6.2. Application of Transformers Models for Vision 
22.6.3. Advantages of Transformers Models 

22.7. Transformers for Vision 

22.7.1. Use of Transformers Models for Vision 
22.7.2. Image Data Preprocessing 
22.7.3. Training a Transformers Model for Vision 

22.8. Hugging Face Transformer Library 

22.8.1. Using the Hugging Face's Transformers Library 
22.8.2. Hugging Face´s Transformers Library Application 
22.8.3. Advantages of Hugging Face´s Transformers Library 

22.9. Other Transformers Libraries Comparison 

22.9.1. Comparison Between Different Transformers Libraries 
22.9.2. Use of the Other Transformers Libraries 
22.9.3. Advantages of the Other Transformers Libraries 

22.10. Development of an NLP Application with RNN and Attention Practical Applications 

22.10.1. Development of a Natural Language Processing Application with RNN and Attention. 
22.10.2. Use of RNN, Attention Mechanisms and Transformers Models in the Application 
22.10.3. Evaluation of the Practical Application 

Module 23. Autoencoders, GANs and Diffusion Models

23.1. Representation of Efficient Data 

23.1.1. Dimensionality Reduction 
23.1.2. Deep Learning 
23.1.3. Compact Representations 

23.2. PCA Realization with an Incomplete Linear Automatic Encoder 

23.2.1. Training Process 
23.2.2. Implementation in Python 
23.2.3. Use of Test Data 

23.3. Stacked Automatic Encoders 

23.3.1. Deep Neural Networks 
23.3.2. Construction of Coding Architectures 
23.3.3. Use of Regularization 

23.4. Convolutional Autoencoders 

23.4.1. Design of Convolutional Models 
23.4.2. Convolutional Model Training 
23.4.3. Results Evaluation 

23.5. Noise Suppression of Automatic Encoders 

23.5.1. Filter Application 
23.5.2. Design of Coding Models 
23.5.3. Use of Regularization Techniques 

23.6. Sparse Automatic Encoders 

23.6.1. Increasing Coding Efficiency 
23.6.2. Minimizing the Number of Parameters 
23.6.3. Using Regularization Techniques 

23.7. Variational Automatic Encoders 

23.7.1. Use of Variational Optimization 
23.7.2. Unsupervised Deep Learning 
23.7.3. Deep Latent Representations 

23.8. Generation of Fashion MNIST Images 

23.8.1. Pattern Recognition 
23.8.2. Image Generation 
23.8.3. Deep Neural Networks Training 

23.9. Generative Adversarial Networks and Diffusion Models 

23.9.1. Content Generation from Images 
23.9.2. Modeling of Data Distributions 
23.9.3. Use of Adversarial Networks 

23.10. Implementation of the Models 

23.10.1. Practical Application 
23.10.2. Implementation of the Models 
23.10.3. Use of Real Data 
23.10.4. Results Evaluation 

Module 24. Natural Language Processing (NLP) with Recurrent Neural Networks (RNN) and Attention

24.1. Introduction to Bio-Inspired Computing 

24.1.1. Introduction to Bio-Inspired Computing 

24.2. Social Adaptation Algorithms 

24.2.1. Bio-Inspired Computation Based on Ant Colonies 
24.2.2. Variants of Ant Colony Algorithms 
24.2.3. Particle Cloud Computing 

24.3. Genetic Algorithms 

24.3.1. General Structure 
24.3.2. Implementations of the Major Operators 

24.4. Space Exploration-Exploitation Strategies for Genetic Algorithms 

24.4.1. CHC Algorithm 
24.4.2. Multimodal Problems 

24.5. Evolutionary Computing Models (I) 

24.5.1. Evolutionary Strategies 
24.5.2. Evolutionary Programming 
24.5.3. Algorithms Based on Differential Evolution 

24.6. Evolutionary Computation Models (II) 

24.6.1. Evolutionary Models Based on Estimation of Distributions (EDA) 
24.6.2. Genetic Programming 

24.7. Evolutionary Programming Applied to Learning Problems 

24.7.1. Rules-Based Learning 
24.7.2. Evolutionary Methods in Instance Selection Problems 

24.8. Multi-Objective Problems 

24.8.1. Concept of Dominance 
24.8.2. Application of Evolutionary Algorithms to Multi-Objective Problems 

24.9. Neural Networks (I) 

24.9.1. Introduction to Neural Networks 
24.9.2. Practical Example with Neural Networks 

24.10. Neural Networks (II) 

24.10.1. Use Cases of Neural Networks in Medical Research 
24.10.2. Use Cases of Neural Networks in Economics 
24.10.3. Use Cases of Neural Networks in Artificial Vision 

Module 25. Artificial Intelligence: Strategies and Applications 

25.1. Financial Services 

25.1.1. The Implications of Artificial Intelligence (AI) in Financial Services  Opportunities and Challenges  
25.1.2. Case Uses  
25.1.3. Potential Risks Related to the Use of AI 
25.1.4. Potential Future Developments/Uses of AI 

25.2. Implications of Artificial Intelligence in the Healthcare Service  

25.2.1. Implications of AI in the Healthcare Sector Opportunities and Challenges  
25.2.2. Case Uses 

25.3. Risks Related to the Use of AI in the Health Service 

25.3.1. Potential Risks Related to the Use of AI 
25.3.2. Potential Future Developments/Uses of AI  

25.4. Retail  

25.4.1. Implications of AI in Retail Opportunities and Challenges  
25.4.2. Case Uses  
25.4.3. Potential Risks Related to the Use of AI  
25.4.4. Potential Future Developments/Uses of AI 

25.5. Industry   

25.5.1. Implications of AI in Industry Opportunities and Challenges 
25.5.2. Case Uses 

25.6. Potential Risks Related to the Use of AI in Industry   

25.6.1. Case Uses 
25.6.2. Potential Risks Related to the Use of AI 
25.6.3. Potential Future Developments/Uses of AI  

25.7. Public Administration  

25.7.1. AI Implications for Public Administration Opportunities and Challenges 
25.7.2. Case Uses  
25.7.3. Potential Risks Related to the Use of AI  
25.7.4. Potential Future Developments/Uses of AI  

25.8. Educational  

25.8.1. AI Implications for Education Opportunities and Challenges 
25.8.2. Case Uses  
25.8.3. Potential Risks Related to the Use of AI  
25.8.4. Potential Future Developments/Uses of AI 

25.9. Forestry and Agriculture  

25.9.1. Implications of AI in Forestry and Agriculture Opportunities and Challenges  
25.9.2. Case Uses 
25.9.3. Potential Risks Related to the Use of AI 
25.9.4. Potential Future Developments/Uses of AI  

25.10 Human Resources  

25.10.1. Implications of AI for Human Resources Opportunities and Challenges 
25.10.2. Case Uses  
25.10.3. Potential Risks Related to the Use of AI  
25.10.4. Potential Future Developments/Uses of AI 

Module 26. Artificial Intelligence Methods and Tools for Clinical Research

26.1. AI Technologies and Tools in Clinical Research 

26.1.1. Using Machine Learning to Identify Patterns in Clinical Data 
26.1.2. Development of Predictive Algorithms for Clinical Trials 
26.1.3. Implementation of AI Systems to Improve Patient Recruitment 
26.1.4. AI Tools for Real-Time Analysis of Research Data with Tableau 

26.2. Statistical Methods and Algorithms in Clinical Trials 

26.2.1. Application of Advanced Statistical Techniques for Clinical Data Analysis 
26.2.2. Use of Algorithms for the Validation and Verification of Trial Results 
26.2.3. Implementation of Regression and Classification Models in Clinical Studies 
26.2.4. Analysis of Large Data Sets using Computational Statistical Methods 

26.3. Design of Experiments and Analysis of Results 

26.3.1. Strategies for the Efficient Design of Clinical Trials Using IA, with IBM Watson Health 
26.3.2. AI Techniques for Analysis and Interpretation of Experimental Data 
26.3.3. Optimization of Research Protocols Using AI Simulations 
26.3.4. Evaluation of Efficacy and Safety of Treatments Using AI Models 

26.4. Interpretation of Medical Images in Research Using AI through Aidoc 

26.4.1. Development of AI Systems for the Automatic Detection of Pathologies in Images 
26.4.2. Use of Deep Learning for Classification and Segmentation in Medical Images 
26.4.3. AI Tools to Improve Accuracy in Image Diagnostics 
26.4.4. Analysis of Radiological and Magnetic Resonance Imaging Using AI 

26.5. Clinical Analysis and Biomedical Data Analysis 

26.5.1. AI in Genomics and Proteomics Data Processing and Analysis DeepGenomics 
26.5.2. Tools for the Integrated Analysis of Clinical and Biomedical Data 
26.5.3. Use of AI to Identify Biomarkers in Clinical Research 
26.5.4. Predictive Analysis of Clinical Outcomes Based on Biomedical Data 

26.6. Advanced Data Visualization in Clinical Research 

26.6.1. Development of Interactive Visualization Tools for Clinical Data 
26.6.2. Use of AI in the Creation of Graphical Representations of Complex Data Microsoft Power BI 
26.6.3. Visualization Techniques for Easy Interpretation of Research Results 
26.6.4. Augmented and Virtual Reality Tools for Visualization of Biomedical Data 

26.7. Natural Language Processing in Scientific and Clinical Documentation 

26.7.1. Application of NLP for the Analysis of Scientific Literature and Clinical Records with Linguamatics 
26.7.2. AI Tools for the Extraction of Relevant Information from Medical Texts 
26.7.3. AI Systems for Summarizing and Categorizing Scientific Publications 
26.7.4. Use of NLP to Identify Trends and Patterns in Clinical Documentation 

26.8. Heterogeneous Data Processing in Clinical Research with Google Cloud Healthcare API and IBM Watson Health 

26.8.1. AI Techniques for Integrating and Analyzing Data from Diverse Clinical Sources 
26.8.2. Tools for the Management of Unstructured Clinical Data 
26.8.3. AI Systems for Correlating Clinical and Demographic Data 
26.8.4. Analysis of Multidimensional Data for Clinical Insights 

26.9. Applications of Neural Networks in Biomedical Research 

26.9.1. Use of Neural Networks for Disease Modeling and Treatment Prediction 
26.9.2. Implementation of Neural Networks in Genetic Disease Classification 
26.9.3. Development of Diagnostic Systems Based on Neural Networks 
26.9.4. Application of Neural Networks in the Personalization of Medical Treatments 

26.10. Predictive Modeling and its Impact on Clinical Research 

26.10.1. Development of Predictive Models for the Anticipation of Clinical Outcomes 
26.10.2. Use of AI in the Prediction of Side Effects and Adverse Reactions 
26.10.3. Implementation of Predictive Models in the Optimization of Clinical Trials 
26.10.4. Risk Analysis in Medical Treatments Using Predictive Modeling 

Module 27. Biomedical Research with AI

27.1. Design and Implementation of Observational Studies with AI 

27.1.1. Implementation of AI for the Selection and Segmentation of Populations in Studies 
27.1.2. Use of Algorithms for Real-Time Monitoring of Observational Study Data 
27.1.3. AI Tools for Identifying Patterns and Correlations in Observational Studies with Flatiron Health 
27.1.4. Automation of the Data Collection and Analysis Process in Observational Studies 

27.2. Validation and Calibration of Models in Clinical Research 

27.2.1. AI Techniques to Ensure the Accuracy and Reliability of Clinical Models 
27.2.2. Use of AI in the Calibration of Predictive Models in Clinical Research 
27.2.3. Cross-Validation Methods Applied to Clinical Models Using IA, with KINME Analytics Platform 
27.2.4. AI Tools for the Evaluation of Generalization of Clinical Models 

27.3. Methods for Integration of Heterogeneous Data in Clinical Research 

27.3.1. AI Techniques for Combining Clinical, Genomic and Environmental Data with DeepGenomics 
27.3.2. Use of Algorithms to Manage and Analyze Unstructured Clinical Data 
27.3.3. AI Tools for Clinical Data Standardization and Normalization with Informatica's Healthcare Data Management 
27.3.4. AI Systems for Correlation of Different Types of Data in Research 

27.4. Multidisciplinary Biomedical Data Integration using Flatiron Health’s OncologyCloud and AutoML 

27.4.1. AI Systems to Combine Data from Different Biomedical Disciplines 
27.4.2. Algorithms for Integrated Analysis of Laboratory and Clinical Data 
27.4.3. AI Tools for Visualization of Complex Biomedical Data 
27.4.4. Use of AI in the Creation of Holistic Health Models from Multidisciplinary Data 

27.5. Deep Learning Algorithms in Biomedical Data Analysis 

27.5.1. Implementation of Neural Networks in the Analysis of Genetic and Proteomic Data 
27.5.2. Use of Deep Learning for Pattern Identification in Biomedical Data 
27.5.3. Development of Predictive Models in Precision Medicine with Deep Learning 
27.5.4. Application of AI in Advanced Biomedical Image Analysis Using Aidoc 

27.6. Optimization of Research Processes with Automation 

27.6.1. Automation of Laboratory Routines by Means of AI Systems with Beckman Coulter 
27.6.2. Use of AI for Efficient Management of Resources and Time in Research 
27.6.3. AI Tools for Optimization of Workflows in Clinical Research 
27.6.4. Automated Systems for Tracking and Reporting Progress in Research 

27.7. Simulation and Computational Modeling in Medicine with AI 

27.7.1. Development of Computational Models to Simulate Clinical Scenarios 
27.7.2. Use of AI for the Simulation of Molecular and Cellular Interactions with Schrödinger  
27.7.3. AI Tools in the Creation of Predictive Disease Models with GNS Healthcare 
27.7.4. Application of AI in the Simulation of Drug and Treatment Effects 

27.8. Use of Virtual and Augmented Reality in Clinical Studies with Surgical Theater 

27.8.1. Implementation of Virtual Reality for Training and Simulation in Medicine 
27.8.2. Use of Augmented Reality in Surgical and Diagnostic Procedures 
27.8.3. Virtual Reality Tools for Behavioral and Psychological Studies 
27.8.4. Application of Immersive Technologies in Rehabilitation and Therapy 

27.9. Data Mining Tools Applied to Biomedical Research 

27.9.1. Use of Data Mining Techniques to Extract Knowledge from Biomedical Databases 
27.9.2. Implementation of AI Algorithms to Discover Patterns in Clinical Data 
27.9.3. AI Tools for Trend Identification in Large Data Sets with Tableau 
27.9.4. Application of Data Mining in the Generation of Research Hypotheses  

27.10. Development and Validation of Biomarkers with Artificial Intelligence 

27.10.1. Use of AI for the Identification and Characterization of Innovative Biomarkers 
27.10.2. Implementation of AI Models for the Validation of Biomarkers in Clinical Studies 
27.10.3. AI tools in Correlating Biomarkers with Clinical Outcomes with Oncimmune 
27.10.4. Application of AI in Biomarker Analysis for Personalized Medicine 

Module 28. Practical Application of Artificial Intelligence in Clinical Research

28.1. Genomic Sequencing Technologies and Data Analysis with AI with DeepGenomics 

28.1.1. Use of AI for Rapid and Accurate Analysis of Genetic Sequences 
28.1.2. Implementation of Machine Learning Algorithms in the Interpretation of Genomic Data 
28.1.3. AI Tools for Identification of Genetic Variants and Mutations 
28.1.4. Development of AI Systems for Anomaly Detection in Medical Images 

28.2. AI in Biomedical Images Analysis with Aidoc 

28.2.1. Development of AI Systems for the Detection of Anomalies in Medical Images 
28.2.2. Use of Deep Learning in the Interpretation of X-rays, MRI and CT Scans 
28.2.3. AI Tools to Improve Accuracy in Diagnostic Imaging 
28.2.4. Implementation of AI in Biomedical Image Classification and Segmentation 

28.3. Robotics and Automation in Clinical Laboratories 

28.3.1. Use of Robots for the Automation of Tests and Processes in Laboratories 
28.3.2. Implementation of Automatic Systems for the Management of Biological Samples 
28.3.3. Development of Robotic Technologies to Improve Efficiency and Accuracy in Clinical Analysis 
28.3.4. AI Application in Optimization of Workflows in Laboratories with Optum 

28.4. AI in the Personalization of Therapies and Precision Medicine 

28.4.1. Development of AI Models for the Personalization of Medical Treatments 
28.4.2. Use of Predictive Algorithms in the Selection of Therapies based on Genetic Profiling 
28.4.3. AI Tools in the Adaptation of Drug Doses and Combinations with PharmGKB 
28.4.4. Application of AI in the Identification of Effective Treatments for Specific Groups  

28.5. Innovations in AI-Assisted Diagnostics using ChatGPT and Amazon Comprehend Medical 

28.5.1. Implementation of AI Systems for Rapid and Accurate Diagnostics 
28.5.2. Use of AI in Early Identification of Diseases through Data Analysis 
28.5.3. Development of AI Tools for Clinical Test Interpretation 
28.5.4. Application of AI in Combining Clinical and Biomedical Data for Comprehensive Diagnostics 

28.6. AI Applications in Microbiome and Microbiology Studies with Metabiomics 

28.6.1. Use of AI in the Analysis and Mapping of the Human Microbiome 
28.6.2. Implementation of Algorithms to Study the Relationship between Microbiome and Diseases 
28.6.3. AI Tools in the Identification of Patterns in Microbiological Studies 
28.6.4. Application of AI in Microbiome-Based Therapeutics Research 

28.7. Wearables and Remote Monitoring in Clinical Trials 

28.7.1. Development of Wearable Devices with AI for Continuous Health Monitoring with FitBit 
28.7.2. Use of AI in the Interpretation of Data Collected by Wearables 
28.7.3. Implementation of Remote Monitoring Systems in Clinical Trials 
28.7.4. Application of AI in the Prediction of Clinical Events through Wearable Data 

28.8. AI in Clinical Trial Management with Oracle Health Sciences 

28.8.1. Use of AI Systems for Optimization of Clinical Trial Management 
28.8.2. Implementation of AI in the Selection and Monitoring of Participants 
28.8.3. AI Tools for Analysis of Clinical Trial Data and Results 
28.8.4. Application of AI to Improve Trial Efficiency and Reduce Trial Costs 

28.9. Development of AI-Assisted Vaccines and Treatments with Benevolent AI 

28.9.1. Use of AI to Accelerate Vaccine Development 
28.9.2. Implementation of Predictive Models in the Identification of Potential Treatments 
28.9.3. AI Tools to Simulate Responses to Vaccines and Drugs 
28.9.4. Application of AI in the Personalization of Vaccines and Therapies 

28.10. AI Applications in Immunology and Immune Response Studies 

28.10.1. Development of AI Models to Understand Immunological Mechanisms with Immuneering 
28.10.2. Use of AI in the Identification of Patterns in Immune Responses 
28.10.3. Implementation of AI in Autoimmune Disorders Research 
28.10.4. Application of AI in the Design of Personalized Immunotherapies 

Module 29. Big Data Analytics and Machine Learning in Clinical Research

29.1. Big Data in Clinical Research: Concepts and Tools 

29.1.1. The Explosion of Data in the Field of Clinical Research 
29.1.2. Concept of Big Data and Main Tools 
29.1.3. Applications of Big Data in Clinical Research 

29.2. Data Mining in Clinical and biomedical Records with KNIME and Python 

29.2.1. Main Methodologies for Data Mining 
29.2.2. Data Integration of Clinical and Biomedical Registry Data 
29.2.3. Detection of Patterns and Anomalies in Clinical and Biomedical Records 

29.3. Machine Learning Algorithms in Biomedical Research with KNIME and Python 

29.3.1. Classification Techniques in Biomedical Research 
29.3.2. Regression Techniques in Biomedical Research 
29.3.3. Unsupervised Techniques in Biomedical Research 

29.4. Predictive Analytics Techniques in Clinical Research with KNIME and Python 

29.4.1. Classification Techniques in Clinical Research 
29.4.2. Regression Techniques in Clinical Research 
29.4.3. Deep Learning in Clinical Research 

29.5. AI Models in Epidemiology and Public Health with KNIME and Python 

29.5.1. Classification Techniques for Epidemiology and Public Health 
29.5.2. Regression Techniques for Epidemiology and Public Health 
29.5.3. Unsupervised Techniques for Epidemiology and Public Health 

29.6. Analysis of Biological Networks and Disease Patterns with KNIME and Python 

29.6.1. Exploration of Interactions in Biological Networks for the Identification of Disease Patterns 
29.6.2. Integration of Omics Data in Network Analysis to Characterize Biological Complexities 
29.6.3. Application of Machine Learning Algorithms for the Discovery of Disease Patterns 

29.7. Development of Tools for Clinical Prognosis with Workflow and Python Platforms 

29.7.1. Creation of Innovative Clinical Prognostic Tools based on Multidimensional Data 
29.7.2. Integration of Clinical and Molecular Variables in the Development of Prognostic Tools 
29.7.3. Evaluating the Effectiveness of Prognostic Tools in Diverse Clinical Contexts 

29.8. Advanced Visualization and Communication of Complex Data with Tools such as PowerBI and Python 

29.8.1. Use of Advanced Visualization Techniques to Represent Complex Biomedical Data 
29.8.2. Development of Effective Communication Strategies to Present Results of Complex Analyses 
29.8.3. Implementation of Interactivity Tools in Visualizations to Enhance Understanding 

29.9. Data Security and Challenges in Big Data Management 

29.9.1. Addressing Data Security Challenges in the Context of Biomedical Big Data 
29.9.1. Strategies for Privacy Protection in the Management of Large Biomedical Datasets 
29.9.3. Implementation of Security Measures to Mitigate Risks in the Handling of Sensitive Data 

29.10. Practical Applications and Case Studies on Biomedical Big Data  

29.10.1. Exploration of Successful Cases in the Implementation of Biomedical Big Data in Clinical Research 
29.10.2. Development of Practical Strategies for the Application of Big Data in Clinical Decision-Making 
29.10.3. Evaluation of Impact and Lessons Learned through Case Studies in the Biomedical Field 

Module 30. Ethical, Legal and Future Aspects of Artificial Intelligence in Clinical Research

30.1. Ethics in the Application of AI in Clinical Research 

30.1.1. Ethical Analysis of AI-Assisted Decision Making in Clinical Research Settings 
30.1.2. Ethics in the Use of AI Algorithms for Participant Selection in Clinical Trials 
30.1.3. Ethical Considerations in the Interpretation of Results Generated by AI Systems in Clinical Research 

30.2. Legal and Regulatory Considerations in Biomedical AI 

30.2.1. Analysis of Legal Regulations in the Development and Application of AI Technologies in the Biomedical Field 
30.2.2. Assessment of Compliance with Specific Regulations to Ensure the Safety and Efficacy of AI-Based Solutions 
30.2.3. Addressing Emerging Regulatory Challenges Associated with the Use of AI in Biomedical Research 

30.3. Informed Consent and Ethical Aspects in the Use of Clinical Data 

30.3.1. Development of Strategies to Ensure Effective Informed Consent in AI Projects 
30.3.2. Ethics in the Collection and Use of Sensitive Clinical Data in the Context of AI-Driven Research 
30.3.3. Addressing Ethical Issues Related to Ownership and Access to Clinical Data in Research Projects 

30.4. AI and Liability in Clinical Research 

30.4.1. Evaluation of Ethical and Legal Accountability in the Implementation of AI Systems in Clinical Research Protocols 
30.4.2. Development of Strategies to Address Potential Adverse Consequences of the Application of AI in Biomedical Research 
30.4.3. Ethical Considerations in the Active Participation of AI in Clinical Research Decision Making 

30.5. Impact of AI on Equity and Access to Health Care 

30.5.1. Evaluation of the Impact of AI Solutions on Equity in Clinical Trial Participation 
30.5.2. Development of Strategies to Improve Access to AI Technologies in Diverse Clinical Settings 
30.5.3. Ethics in the Distribution of Benefits and Risks Associated with the Application of AI in Health Care 

30.6. Privacy and Data Protection in Research Projects 

30.6.1. Ensuring the Privacy of Participants in Research Projects Involving the Use of AI 
30.6.2. Development of Policies and Practices for Data Protection in Biomedical Research 
30.6.3. Addressing Specific Privacy and Security Challenges in the Handling of Sensitive Data in the Clinical Environment 

30.7. AI and Sustainability in Biomedical Research 

30.7.1. Assessment of the Environmental Impact and Resources Associated with the Implementation of AI in Biomedical Research 
30.7.2. Development of Sustainable Practices in the Integration of AI Technologies into Clinical Research Projects 
30.7.3. Ethics in Resource Management and Sustainability in the Adoption of AI in Biomedical Research 

30.8. Auditing and Explainability of AI Models in the Clinical Setting 

30.8.1. Development of Audit Protocols for Assessing the Reliability and Accuracy of AI Models in Clinical Research 
30.8.2. Ethics in Explainability of Algorithms to Ensure Understanding of Decisions Made by AI Systems in Clinical Contexts 
30.8.3. Addressing Ethical Challenges in the Interpretation of AI Model Results in Biomedical Research 

30.9. Innovation and Entrepreneurship in the Field of Clinical AI 

30.9.1. Responsible Innovation Ethics in Developing AI Solutions for Clinical Applications 
30.9.2. Development of Ethical Business Strategies in the Field of Clinical AI 
30.9.3. Ethical Considerations in the Commercialization and Adoption of AI Solutions in the Clinical Sector 

30.10. Ethical Considerations in International Collaboration in Clinical Research 

30.10.1. Development of Ethical and Legal Arrangements for International Collaboration in AI-Driven Research Projects 
30.10.2. Ethics in Multi-Institutional and Multi-Country Involvement in Clinical Research using AI Technologies 
30.10.3. Addressing Emerging Ethical Challenges Associated with Global Collaboration in Biomedical Research 

A reference teaching team will introduce you to the latest advances in Deep Computer Vision with Convolutional Neural Networks”