This program will allow you to master all the fundamental aspects involved in managing any type of business project, and will prepare you to lead your company to immediate success"

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”

ThisAdvanced master’s degree will cover a wide range of content, designed to provide professionals with a comprehensive understanding of the intersection between business management and data science. This will include fundamentals of data analytics, machine learning, data mining and advanced statistics. Graduates will also be immersed in topics related to data-driven decision making, data visualization strategies and predictive modeling methods. In addition, crucial aspects of management such as leadership, effective communication, ethics and alignment of data strategies with business objectives will be addressed.

You will equip yourself with a comprehensive set of competencies, merging data science expertise with business management skills essential to lead in the information age”

Syllabus

This TECH Global University Advanced master’s degree MBA in Data Science Management (DSO, Chief Data Science Officer) is an intense program that prepares students to face challenges and business decisions globally. 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 characteristics of data science to apply to each department of the company and is designed for managers to understand business management 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 management of Data Science and business management. A program that understands their needs and those of their company through innovative content based on the latest trends, and supported by the best educational methodology and an exceptional faculty, which will provide them with the skills to solve critical situations in a creative and efficient way.

Module 1. Leadership, Ethics and Social Responsibility in Companies  
Module 2. Strategic Management and 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. Data Analysis in a Business Organization
Module 12. Data and Information Management and Manipulation in Data
Module 13. IoT Devices and Platforms as the Basis for Data Science
Module 14. Graphical Representation of Data Analysis
Module 15. Data Science Tools
Module 16. Data Mining: Selection, Pre-Processing and Transformation
Module 17. Predictability and Analysis of Stochastic Phenomena
Module 18. Design and Development of Intelligent Systems
Module 19. Architecture and Systems for Intensive Use of Data  
Module 20. Practical Application of Data Science in Businessmen Sectors

Where, When and How is it Taught?

TECH offers the possibility of developing this Advanced master’s degree MBA in Data Science Management (DSO, Chief Data Science Officer) completely online. Throughout the 24 months of the educational program, the students will be able to access all the contents of this program at any time, allowing them to self-manage their 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 the 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 Corporations and 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: Typology

2.2. Corporate Strategy

2.2.1. Competitive Corporate Strategy
2.2.2. Growth Strategies: Typology
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. Conflict 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. New Challenges in the Human Resources Department
3.22.4. Management of Innovation
3.22.5. 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 Methodologie

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. Management 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 

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 Cash Requirements
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 Activity
4.8.5. The Company as a Facilitator of the Work of the of the State

4.9. Corporate Control Systems

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. Analyzing and Solving 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. Supply Chain Typologies (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. Costs and Efficiency of the Operations Chain
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 Issues
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 Logistics Chains

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 for 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 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 in Companies

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 Technological 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 and 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 Systems or ERP

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. The Basic Elements of Marketing
7.2.3. Marketing Activities in Companies

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 Customer Loyalty

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. Features 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. Motivation, Social Action, Participation and Training Programs 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. Market 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, Briefing Concept 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  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 Factors 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 para 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 and Direction: 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 Role of the CEO
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 for Personal Brand Development
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. Data Analysis in a Business Organization 

11.1. Business Analysis  

11.1.1. Business Analysis 
11.1.2. Data Structure 
11.1.3. Phases and Elements

11.2. Data Analysis in the Business

11.2.1. Scorecards and KPIs by Departments
11.2.2. Operational, Tactical and Strategic Reports
11.2.3. Data Analytics Applied to Each Department

11.2.3.1. Marketing and Communication
11.2.3.2. Commercial
11.2.3.3. Customer Service
11.2.3.4. Purchasing
11.2.3.5. Administration
11.2.3.6. HR
11.2.3.7. Production
11.2.3.8. IT

11.3. Marketing and Communication 

11.3.1. KPIs to be Measured, Applications and Benefits 
11.3.2. Marketing Systems and Data Warehouse 
11.3.3. Implementation of a Data Analytics Marketing Framework 
11.3.4. Marketing and Communication Plan 
11.3.5. Strategies, Prediction and Campaign Management 

11.4. Commerce and Sales  

11.4.1. Contributions of Data Analytics in the Commercial Area  
11.4.2. Needs of the Sales Department 
11.4.3. Market Research  

11.5. Customer Service

11.5.1. Loyalty
11.5.2. Personal Coaching and Emotional Intelligence
11.5.3. Customer Satisfaction

11.6. Purchasing

11.6.1. Data Analysis for Market Research
11.6.2. Data Analysis for Competency Research
11.6.3. Other Applications

11.7. Administration

11.7.1. Needs of the Administration Department
11.7.2. Data Warehouse and Financial Risk Analysis
11.7.3. Data Warehouse and Credit Risk Analysis

11.8. Human Resources

11.8.1. HR and the Benefits of Data Analysis
11.8.2. Data Analytics Tools for the HR Department
11.8.3. Data Analytics Applications for the HR Department

11.9. Production

11.9.1. Data Analysis in a Production Department
11.9.2. Applications
11.9.3. Benefits

11.10. IT

11.10.1. IT Department
11.10.2. Data Analysis and Digital Transformation
11.10.3. Innovation and Productivity

Module 12. Data and Information Management and Manipulation in Data Science 

12.1. Statistics Variables, Indexes and Ratios

12.1.1. Statistics
12.1.2. Statistical Dimensions
12.1.3. Variables, Indexes and Ratios

12.2. Type of Data

12.2.1. Qualitative
12.2.2. Quantitative
12.2.3. Characterization and Categories

12.3. Data Knowledge from the Measurements

12.3.1. Centralization Measurements
12.3.2. Measures of Dispersion
12.3.3. Correlation

12.4. Data Knowledge from the Graphs

12.4.1. Visualization According to Type of Data
12.4.2. Interpretation of Graphic Information
12.4.3. Customization of Graphics with R

12.5. Probability

12.5.1. Probability
12.5.2. Function of Probability
12.5.3. Distributions

12.6. Data Collection

12.6.1. Methodology of Data Collection
12.6.2. Data Collection Tools
12.6.3. Data Collection Channels

12.7. Data Cleaning

12.7.1. Phases of Data Cleansing
12.7.2. Data Quality
12.7.3. Data Manipulation (with R)

12.8. Data Analysis, Interpretation and Evaluation of Results

12.8.1. Statistical Measures
12.8.2. Relationship Indexes
12.8.3. Data Mining

12.9. The Marketing Mix

12.9.1. Components
12.9.2. Design

12.10. Data Availability

12.10.1. Access
12.10.2. Uses
12.10.3. Security

Module 13. IoT Devices and Platforms as the Basis for Data Science 

13.1. Internet of Things

13.1.1. Internet of the Future, Internet of Things
13.1.2. The Industrial Internet Consortium

13.2. Architecture of Reference

13.2.1. The Architecture of Reference
13.2.2. Layers
13.2.3. Components

13.3. Sensors and IoT Devices

13.3.1. Principal Components
13.3.2. Sensors and Actuators

13.4. Communications and Protocols

13.4.1. Protocols OSI Model
13.4.2. Communication Technologies

13.5. Cloud Platforms for IoT and IIoT

13.5.1. General Purpose Platforms
13.5.2. Industrial Platforms
13.5.3. Open Code Platforms

13.6. Data Management on IoT Platforms

13.6.1. Data Management Mechanisms Open Data
13.6.2. Data Exchange and Visualization

13.7. IoT Security

13.7.1. Requirements and Security Areas
13.7.2. Security Strategies in IIoT

13.8. Applications of IoT

13.8.1. Intelligent Cities
13.8.2. Health and Fitness
13.8.3. Smart Home
13.8.4. Other Applications

13.9. Applications of IIoT

13.9.1. Fabrication
13.9.2. Transport
13.9.3. Energy
13.9.4. Agriculture and Livestock
13.9.5. Other Sectors

13.10. Industry 4.0

13.10.1. IoRT (Internet of Robotics Things)
13.10.2. 3D Additive Manufacturing
13.10.3. Big Data Analytic

Module 14. Graphical Representation of Data Analysis 

14.1. Exploratory Analysis

14.1.1. Representation for Information Analysis
14.1.2. The Value of Graphical Representation
14.1.3. New Paradigms of Graphical Representation

14.2. Optimization for Data Science

14.2.1. Color Range and Design
14.2.2. Gestalt in Graphic Representation
14.2.3. Errors to Avoid and Advice

14.3. Basic Data Sources

14.3.1. For Quality Representation
14.3.2. For Quantity Representation
14.3.3. For Time Representation

14.4. Complex Data Sources

14.4.1. Files, Lists and Databases
14.4.2. Open Data
14.4.3. Continuous Data Generation

14.5. Types of Graphs

14.5.1. Basic Representations
14.5.2. Block Representation
14.5.3. Representation for Dispersion Analysis
14.5.4. Circular Representations
14.5.5. Bubble Representations
14.5.6. Geographical Representations

14.6. Types of Visualization

14.6.1. Comparative and Relational
14.6.2. Distribution
14.6.3. Hierarchical

14.7. Report Design with Graphic Representation

14.7.1. Application of Graphs in Marketing Reports
14.7.2. Application of Graphs in Scorecards and KPI’s
14.7.3. Application of Graphs in Strategic Plans
14.7.4. Other Uses: Science, Health, Business

14.8. Graphic Narration

14.8.1. Graphic Narration
14.8.2. Evolution
14.8.3. Uses

14.9. Tools Oriented Towards Visualization

14.9.1. Advanced Tools
14.9.2. Online Software
14.9.3. Open Source

14.10. New Technologies in Data Visualization

14.10.1. Systems for Virtualization of Reality
14.10.2. Reality Enhancement and Improvement Systems
14.10.3. Intelligent Systems

Module 15. Data Science Tools 

15.1. Data Science

15.1.1. Data Science
15.1.2. Advanced Tools for the Data Scientist

15.2. Data, Information and Knowledge

15.2.1. Data, Information and Knowledge
15.2.2. Types of Data
15.2.3. Data Sources

15.3. From Data to Information

15.3.1. Data Analysis
15.3.2. Types of Analysis
15.3.3. Extraction of Information from a Dataset

15.4. Extraction of Information Through Visualization

15.4.1. Visualization as an Analysis Tool
15.4.2. Visualization Methods
15.4.3. Visualization of a Data Set

15.5. Data Quality

15.5.1. Quality Data
15.5.2. Data Cleaning
15.5.3. Basic Data Pre-Processing

15.6. Dataset

15.6.1. Dataset Enrichment
15.6.2. The Curse of Dimensionality
15.6.3. Modification of Our Data Set

15.7. Unbalance

15.7.1. Classes of Unbalance
15.7.2. Unbalance Mitigation Techniques
15.7.3. Balancing a Dataset

15.8. Unsupervised Models

15.8.1. Unsupervised Model
15.8.2. Methods
15.8.3. Classification with Unsupervised Models

15.9. Supervised Models

15.9.1. Supervised Model
15.9.2. Methods
15.9.3. Classification with Supervised Models

15.10. Tools and Good Practices

15.10.1. Good Practices for Data Scientists
15.10.2. The Best Model
15.10.3. Useful Tools

Module 16. Data Mining: Selection, Pre-Processing and Transformation 

16.1. Statistical Inference

16.1.1. Descriptive Statistics vs. Statistical Inference
16.1.2. Parametric Procedures
16.1.3. Non-Parametric Procedures

16.2. Exploratory Analysis

16.2.1. Descriptive Analysis
16.2.2. Visualization
16.2.3. Data Preparation

16.3. Data Preparation

16.3.1. Integration and Data Cleaning
16.3.2. Normalization of Data
16.3.3. Transforming Attributes

16.4. Missing Values

16.4.1. Treatment of Missing Values
16.4.2. Maximum Likelihood Imputation Methods
16.4.3. Missing Value Imputation Using Machine Learning

16.5. Noise in the Data

16.5.1. Noise Classes and Attributes
16.5.2. Noise Filtering
16.5.3. The Effect of Noise

16.6. The Curse of Dimensionality

16.6.1. Oversampling
16.6.2. Undersampling
16.6.3. Multidimensional Data Reduction

16.7. From Continuous to Discrete Attributes

16.7.1. Continuous Data Vs. Discreet Data
16.7.2. Discretization Process

16.8. The Data

16.8.1. Data Selection
16.8.2. Prospects and Selection Criteria
16.8.3. Selection Methods

16.9. Instance Selection

16.9.1. Methods for Instance Selection
16.9.2. Prototype Selection
16.9.3. Advanced Methods for Instance Selection

16.10. Data Pre-Processing in Big Data Environments

16.10.1. Big Data
16.10.2. Classical Versus Massive Pre-Processing
16.10.3. Smart Data

Module 17. Predictability and Analysis of Stochastic Phenomena

17.1. Time Series

17.1.1. Time Series
17.1.2. Utility and Applicability
17.1.3. Related Case Studies

17.2. Time Series

17.2.1. Trend Seasonality of ST
17.2.2. Typical Variations
17.2.3. Waste Analysis

17.3. Typology

17.3.1. Stationary
17.3.2. Non-Stationary
17.3.3. Transformations and Settings

17.4. Time Series Schemes

17.4.1. Additive Scheme (Model)
17.4.2. Multiplicative Scheme (Model)
17.4.3. Procedures to Determine the Type of Model

17.5. Basic Forecasting Methods

17.5.1. Media
17.5.2. Naïve
17.5.3. Seasonal Naivety
17.5.4. Method Comparison

17.6. Waste Analysis

17.6.1. Autocorrelation
17.6.2. ACF of Waste
17.6.3. Correlation Test

17.7. Regression in the Context of Time Series

17.7.1. ANOVA
17.7.2. Fundamentals
17.7.3. Practical Applications

17.8. Predictive Methods of Time Series

17.8.1. ARIMA
17.8.2. Exponential Smoothing

17.9. Manipulation and Analysis of Time Series with R

17.9.1. Data Preparation
17.9.2. Identification of Patterns
17.9.3. Model Analysis
17.9.4. Prediction

17.10. Combined Graphical Analysis with R

17.10.1. Normal Situations
17.10.2. Practical Application for the Resolution of Simple Problems
17.10.3. Practical Application for the Resolution of Advanced Problems

Module 18. Design and Development of Intelligent Systems 

18.1. Data Pre-Processing

18.1.1. Data Pre-Processing
18.1.2. Data Transformation
18.1.3. Data Mining

18.2. Machine Learning

18.2.1. Supervised and Unsupervised Learning
18.2.2. Reinforcement Learning
18.2.3. Other Learning Paradigms

18.3. Classification Algorithms

18.3.1. Inductive Machine Learning
18.3.2. SVM and KNN
18.3.3. Metrics and Scores for Ranking

18.4. Regression Algorithms

18.4.1. Lineal Regression, Logistical Regression and Non-Lineal Models
18.4.2. Time Series
18.4.3. Metrics and Scores for Regression

18.5. Clustering Algorithms

18.5.1. Hierarchical Clustering Techniques
18.5.2. Partitional Clustering Techniques
18.5.3. Metrics and Scores for Clustering

18.6. Association Rules Techniques

18.6.1. Methods for Rule Extraction
18.6.2. Metrics and Scores for Association Rule Algorithms

18.7. Advanced Classification Techniques. Multiclassifiers

18.7.1. Bagging Algorithms
18.7.2. Random ”Forests Sorter”
18.7.3. ”Boosting” for Decision Trees

18.8. Probabilistic Graphical Models

18.8.1. Probabilistic Models
18.8.2. Bayesian Networks Properties, Representation and Parameterization
18.8.3. Other Probabilistic Graphical Models

18.9. Neural Networks

18.9.1. Machine Learning with Artificial Neural Networks
18.9.2. Feedforward Networks

18.10. Deep Learning

18.10.1. Deep Feedforward Networks
18.10.2. Convolutional Neural Networks and Sequence Models
18.10.3. Tools for Implementing Deep Neural Networks

Module 19. Architecture and Systems for Intensive Use of Data

19.1. Non-Functional Requirements. Pillars of Big Data Applications

19.1.1. Reliability
19.1.2. Adaptation
19.1.3. Maintainability

19.2. Data Models

19.2.1. Relational Model
19.2.2. Document Model
19.2.3. Graph Type Data Model

19.3. Databases. Data Storage and Retrieval Management

19.3.1. Hash Indexes
19.3.2. Structured Log Storage
19.3.3. Trees B

19.4. Data Coding Formats

19.4.1. Language-Specific Formats
19.4.2. Standardized Formats
19.4.3. Binary Coding Formats
19.4.4. Data Stream Between Processes

19.5. Replication

19.5.1. Objectives of Replication
19.5.2. Replication Models
19.5.3. Problems with Replication

19.6. Distributed Transactions

19.6.1. Transaction
19.6.2. Protocols for Distributed Transactions
19.6.3. Serializable Transactions

19.7. Partitions

19.7.1. Forms of Partitioning
19.7.2. Secondary Index Interaction and Partitioning
19.7.3. Partition Rebalancing

19.8.  Data Processing

19.8.1. Batch Processing
19.8.2. Distributed File Systems
19.8.3. MapReduce

19.9. Data Processing in Real Time

19.9.1. Types of Message Brokers
19.9.2. Representation of Databases as Data Streams
19.9.3. Data Stream Processing

19.10. Practical Applications in Business

19.10.1. Consistency in Readings
19.10.2. Holistic Focus of Data
19.10.3. Scaling of a Distributed Service

Module 20. Practical Application of Data Science in Business Sectors

20.1. Health Sector

20.1.1. Implications of AI and Data Analysis in the Health Sector
20.1.2. Opportunities and Challenges

20.2. Risks and Trends in the Health Sector

20.2.1. Use in the Health Sector
20.2.2. Potential Risks Related to the Use of AI

20.3. Financial Services

20.3.1. Implications of AI and Data Analysis in Financial Services Sector
20.3.2. Use in the Financial Services
20.3.3. Potential Risks Related to the Use of AI

20.4. Retail

20.4.1. Implications of AI and Data Analytics in the Retail Sector
20.4.2. Use in Retail
20.4.3. Potential Risks Related to the Use of AI

20.5. Industry 4.0

20.5.1. Implications of AI and Data Analysis in Industry 4.0
20.5.2. Use in the 4.0 Industry

20.6. Risks and Trends in Industry 4.0

20.6.1. Potential Risks Related to the Use of AI

20.7. Public Administration

20.7.1. Implications of AI and Data Analytics for Public Administration
20.7.2. Use in Public Administration
20.7.3. Potential Risks Related to the Use of AI

20.8. Educational

20.8.1. Implications of AI and Data Analysis in Education
20.8.2. Potential Risks Related to the Use of AI

20.9. Forestry and Agriculture

20.9.1. Implications of AI and Data Analysis in Forestry and Agriculture
20.9.2. Use in Forestry and Agriculture
20.9.3. Potential Risks Related to the Use of AI

20.10. Human Resources

20.10.1. Implications of AI and Data Analysis in Human Resources
20.10.2. Practical Applications in the Business World
20.10.3. Potential Risks Related to the use of AI