An exhaustive and 100% online program, exclusive to TECH, with an international perspective supported by our membership with the Business Graduates Association”

The rise of Data Science in business processes is redefining the way organizations conceive corporate strategy. For example, its ability to transform information into actionable knowledge allows companies to anticipate trends, optimize their operations, and make informed decisions. In this context, professionals need to develop advanced competencies for the optimal handling of Data Science tools. This will enable them to ensure that data treatments are efficient. 

To facilitate this task, TECH launches an exclusive Professional master’s degree MBA in Corporate Technical Data Science Management. Designed by leading experts in the sector, the academic journey will delve into aspects ranging from the technical approach to information management systems to the detailed analysis of the data lifecycle. Additionally, the syllabus will provide cutting-edge tools for data collection, cleaning, and even modeling. In this regard, the learning materials will also cover the regulatory foundations related to data protection. Thanks to this, students will develop advanced competencies to design scalable infrastructures, manage massive data flows in real-time, and deploy services in high-availability architectures. 

Regarding methodology, TECH employs its disruptive Relearning system, based on the natural and progressive reiteration of key concepts in the syllabus. To access the Virtual Campus, IT professionals will only need a device with internet access. Additionally, renowned International Guest Directors will deliver rigorous Masterclasses. 

Furthermore, thanks to TECH's membership in Business Graduates Association (BGA), students will have access to exclusive and up-to-date resources that will strengthen their continuous learning and professional development, as well as discounts on professional events that will facilitate networking with industry experts. Additionally, they will be able to expand their professional network by connecting with specialists from different regions, fostering the exchange of knowledge and new job opportunities.

Renowned International Guest Directors will offer intensive Masterclasses on the latest trends in Corporate Technical Data Science Management” 

This Professional master’s degree MBA in Corporate Technical Data Science Management contains the most complete and up-to-date program on the market. The most important features include:

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

With TTECH's Relearning system you will not have to invest a large amount of study hours and you will focus on the most relevant concepts. Enroll now!” 

The faculty includes professionals from the field of Corporate Technical Data Science Management, who bring their work experience to this program, along with recognized specialists from leading societies and prestigious universities. 

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

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

You will have comprehensive knowledge of data management systems and their strategic application in the corporate environment” 

You will master the most sophisticated techniques for collecting, cleaning, and storing large volumes of data” 

This academic pathway will delve into the essential aspects of assuming responsibilities in Corporate Technical Data Science Management. In this way, the syllabus will address key regulations for the responsible management of data, enabling professionals to operate within demanding regulatory frameworks and ensure the integrity of information. Additionally, the educational materials will explore the specifics of scalable systems for the massive use of sensitive data, which are essential in high-demand corporate environments. Moreover, modern tools will be provided to design robust architectures that meet real business needs. 

You will integrate data infrastructures based on scalable, distributed systems designed for multiple services” 

Module 1. Main Information Management Systems

1.1. ERP and CRM

1.1.1. ERP
1.1.2. CRM
1.1.3. Differences between ERP and CRM. Point of Sale
1.1.4. Business Success

1.2. ERP

1.2.1. ERP
1.2.2. Types of ERP
1.2.3. Development of an ERP Implementation Project
1.2.4. ERP Resource Optimizer
1.2.5. Architecture of an ERP System

1.3. Information Provided by the ERP

1.3.1. Information Provided by the ERP
1.3.2. Advantages and Disadvantages
1.3.3. The Information

1.4. ERP Systems

1.4.1. Current ERP Systems and Tools
1.4.2. Decision-Making
1.4.3. Day-to-day with an ERP

1.5. CRM: The Implementation Project

1.5.1. CRM. The Implementation Project
1.5.2. The CRM as a Commercial Tool
1.5.3. Strategies for the Information System

1.6. CRM: Customer Loyalty

1.6.1. Starting Point
1.6.2. Sell or Retain
1.6.3. Success Factors in Our Loyalty System
1.6.4. Multichannel Strategies
1.6.5. Design of Loyalty Actions
1.6.6. E-Loyalty

1.7. CRM: Communication Campaigns

1.7.1. Communication Actions and Plans
1.7.2. Importance of an Informed Customer
1.7.3. Listening to the Customer

1.8. CRM: Preventing Dissatisfaction

1.8.1. Customer Losses
1.8.2. Timely Error Detection
1.8.3. Improvement Processes
1.8.4. Recovery of the Dissatisfied Customer

1.9. CRM: Special Communication Actions

1.9.1. Objectives and Planning of a Company Event
1.9.2. Event Design and Execution
1.9.3. Actions from the Department
1.9.4. Result Analysis

1.10. Relational Marketing

1.10.1. Implementation. Mistakes
1.10.2. Methodology, Segmentation, and Processes
1.10.3. Action, According to the Department
1.10.4. CRM Tools

Module 2. Data Types and Life Cycle

2.1. Statistics

2.1.1. Statistics: Descriptive Statistics, Inferential Statistics
2.1.2. Population, Sample, Individual
2.1.3. Variables: Definition, Measurement Scales

2.2. Types of Statistical Data

2.2.1. By Type
2.2.1.1. Quantitative: Continuous Data and Discrete Data
2.2.1.2. Qualitative: Binomial Data, Nominal Data and Ordinal Data
2.2.2. By Form
2.2.2.1. Numerical
2.2.2.2. Text
2.2.2.3. Logical
2.2.3. By Source
2.2.3.1. Primary
2.2.3.2. Secondary

2.3. Data Lifecycle

2.3.1. Lifecycle Stages
2.3.2. Lifecycle Milestones
2.3.3. FAIR Principles

2.4. Initial Stages of the Cycle

2.4.1. Goal Definition
2.4.2. Determination of Required Resources
2.4.3. Gantt Chart
2.4.4. Data Structure

2.5. Data Collection

2.5.1. Data Collection Methodology
2.5.2. Data Collection Tools
2.5.3. Data Collection Channels

2.6. Data Cleaning

2.6.1. Data Cleaning Phases
2.6.2. Data Quality
2.6.3. Data Manipulation (using R)

2.7. Data Analysis, Interpretation and Evaluation of Results

2.7.1. Statistical Measures
2.7.2. Relationship Indices
2.7.3. Data Mining

2.8. Data Warehouse

2.8.1. Components of a Data Warehouse
2.8.2. Design
2.8.3. Aspects to Consider

2.9. Data Availability

2.9.1. Access
2.9.2. Utility
2.9.3. Security

Module 3. Machine Learning

3.1. Knowledge in Databases

3.1.1. Data Preprocessing
3.1.2. Analysis
3.1.3. Interpretation and Evaluation of the Results

3.2. Machine Learning

3.2.1. Supervised and Unsupervised Learning
3.2.2. Reinforcement Learning
3.2.3. Semi-Supervised Learning. Other Learning Models

3.3. Classification

3.3.1. Decision Trees and Rule-Based Learning.
3.3.2. Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) Algorithm
3.3.3. Metrics for Classification Algorithms

3.4. Regression

3.4.1. Linear and Logistic Regression
3.4.2. Non-Linear Regression Models
3.4.3. Time Series Analysis
3.4.4. Metrics for Regression Algorithms

3.5. Clustering

3.5.1. Hierarchical Clustering
3.5.2. Partitioned Clustering
3.5.3. Metrics for Clustering Algorithms

3.6. Association Rules

3.6.1. Interest Measures
3.6.2. Rule Extraction Methods
3.6.3. Metrics for Association Rule Algorithms

3.7. Multiclassifiers

3.7.1. Bootstrap Aggregation or Bagging
3.7.2. Random Forest Algorithm
3.7.3. Boosting Algorithm

3.8. Probabilistic Reasoning Models

3.8.1. Probabilistic Reasoning
3.8.2. Bayesian Networks or Belief Networks
3.8.3. Hidden Markov Models

3.9. Multilayer Perceptron

3.9.1. Neural Network
3.9.2. Machine Learning with Neural Networks
3.9.3. Gradient Descent, Backpropagation, and Activation Functions
3.9.4. Implementation of an Artificial Neural Network

3.10. Deep Learning

3.10.1. Deep Neural Networks. Introduction
3.10.2. Convolutional Networks
3.10.3. Sequence Modeling
3.10.4. Tensorflow and PyTorch

Module 4. Web Analytics

4.1. Web Analytics

4.1.1. Introduction
4.1.2. Evolution of Web Analytics
4.1.3. Analysis Process

4.2. Google Analytics

4.2.1. Google Analytics
4.2.2. Usage
4.2.3. Objectives

4.3. Hits. Interactions with the Website

4.3.1. Basic Metrics
4.3.2. KPI (Key Performance Indicators)
4.3.3. Adequate Conversion Rates

4.4. Frequent Dimensions

4.4.1. Source
4.4.2. Medium
4.4.3. Keyword
4.4.4. Campaign
4.4.5. Custom Labeling

4.5. Google Analytics Setup

4.5.1. Installation. Account Creation
4.5.2. Tool Versions: UA/GA4
4.5.3. Tracking Tag
4.5.4. Conversion Goals

4.6. Google Analytics Organization

4.6.1. Account
4.6.2. Property
4.6.3. View

4.7. Google Analytics Reports

4.7.1. Real-time
4.7.2. Audience
4.7.3. Acquisition
4.7.4. Behavior
4.7.5. Conversions
4.7.6. E-Commerce

4.8. Advanced Google Analytics Reports

4.8.1. Custom Reports
4.8.2. Dashboards
4.8.3. APIs

4.9. Filters and Segments

4.9.1. Filter
4.9.2. Segment
4.9.3. Segment Types: Predefined/Custom
4.9.4. Remarketing Lists

4.10. Digital Analytics Plan

4.10.1. Measurement
4.10.2. Implementation in the Technological Environment
4.10.3. Conclusions

Module 5. Scalable and Reliable Mass Data Usage Systems

5.1. Scalability, Reliability and Maintainability

5.1.1. Scalability
5.1.2. Reliability
5.1.3. Maintainability

5.2. Data Models

5.2.1. Evolution of Data Models
5.2.2. Comparison of Relational Model with Document-Based NoSQL Model
5.2.3. Network Model

5.3. Data Storage and Retrieval Engines

5.3.1. Structured Log Storage
5.3.2. Storage in Segment Tables
5.3.3. B Trees

5.4. Services, Message Passing and Data Encoding Formats

5.4.1. Data Flow in REST Services
5.4.2. Data Flow in Message Passing
5.4.3. Message Sending Formats

5.5. Replication

5.5.1. CAP Theorem
5.5.2. Consistency Models
5.5.3. Models of Replication Based on Leader and Follower Concepts

5.6. Distributed Transactions

5.6.1. Atomic Operations
5.6.2. Distributed Transactions from Different Approaches Calvin, Spanner
5.6.3. Serializability

5.7. Partitions

5.7.1. Types of Partitions
5.7.2. Indexes in Partitions
5.7.3. Partition Rebalancing

5.8. Batch Processing

5.8.1. Batch Processing
5.8.2. MapReduce
5.8.3. Post-MapReduce Approaches

5.9. Data Stream Processing

5.9.1. Messaging Systems
5.9.2. Persistence of Data Flows
5.9.3. Uses and Operations with Data Flows

5.10. Case Uses. Twitter, Facebook, Uber

5.10.1. Twitter: The Use of Caches
5.10.2. Facebook: Non-Relational Models
5.10.3. Uber: Different Models for Different Purposes

Module 6. System Administration for Distributed Deployments

6.1. Classic Administration. The Monolithic Model

6.1.1. Classical Applications. The Monolithic Model
6.1.2. System Requirements for Monolithic Applications
6.1.3. The Administration of Monolithic Systems
6.1.4. Automation

6.2. Distributed Applications. The Microservice

6.2.1. Distributed Computing Paradigm
6.2.2. Microservices-Based Models
6.2.3. System Requirements for Distributed Models
6.2.4. Monolithic Applications vs. Distributed Applications

6.3. Tools for Resource Exploitation

6.3.1. “Iron” Management
6.3.2. Virtualization
6.3.3. Emulation
6.3.4. Paravirtualization

6.4. IaaS, PaaS and SaaS Models

6.4.1. LaaS Model
6.4.2. PaaS Model
6.4.3. SaaS Model
6.4.4. Design Patterns

6.5. Containerization

6.5.1. Virtualization with Cgroups
6.5.2. Containers
6.5.3. From Application to Container
6.5.4. Container Orchestration

6.6. Clustering

6.6.1. High Performance and High Availability
6.6.2. High Availability Models
6.6.3. Cluster as SaaS Platform
6.6.4. Cluster Securitization

6.7. Cloud Computing

6.7.1. Clusters vs. Clouds
6.7.2. Types of Clouds
6.7.3. Cloud Service Models
6.7.4. Oversubscription

6.8. Monitoring and Testing

6.8.1. Types of Monitoring
6.8.2. Visualization
6.8.3. Infrastructure Tests
6.8.4. Chaos Engineering

6.9. Study Case: Kubernetes

6.9.1. Structure
6.9.2. Administration
6.9.3. Deployment of Services
6.9.4. Development of Services for K8S

6.10. Study Case: OpenStack

6.10.1. Structure
6.10.2. Administration
6.10.3. Deployment
6.10.4. Development of Services for OpenStack

Module 7. Internet of Things

7.1. Internet of things (IoT)

7.1.1. The Internet of the Future
7.1.2. Internet of Things and Industrial Internet of Things
7.1.3. The Industrial Internet Consortium

7.2. Architecture of Reference

7.2.1. The Architecture of Reference
7.2.2. Layers and Components

7.3. IoT Devices

7.3.1. Classification
7.3.2. Components
7.3.3. Sensors and Actuators

7.4. Communication Protocols

7.4.1. Classification
7.4.2. OSI Model
7.4.3. Technologies

7.5. IoT and IIoT platforms

7.5.1. The IoT Platform
7.5.2. General Purpose Cloud Platforms
7.5.3. Industrial Platforms
7.5.4. Open Code Platforms

7.6. Data Management on IoT Platforms

7.6.1. Management Mechanisms
7.6.2. Open Data
7.6.3. Exchange of Data
7.6.4. Data Visualization

7.7. IoT Security

7.7.1. Security Requirements
7.7.2. Security Areas
7.7.3. Security Strategies
7.7.4. IIoT Security

7.8. IoT Systems Application Areas

7.8.1. Intelligent Cities
7.8.2. Health and Fitness
7.8.3. Smart Home
7.8.4. Other Applications

7.9. Application of IIoT to Different Industrial Sectors

7.9.1. Fabrication
7.9.2. Transport
7.9.3. Energy
7.9.4. Agriculture and Livestock
7.9.5. Other Sectors

7.10. Integration of IIoT in the Industry 4.0 Model 

7.10.1. IoRT (Internet of Robotics Things)
7.10.2. 3D Additive Manufacturing
7.10.3. Big Data Analytics

Module 8. Project Management and Agile Methodologies

8.1. Project Direction and Management

8.1.1. The Project
8.1.2. Phases of a Project
8.1.3. Project Direction and Management

8.2. PMI Methodology for Project Management

8.2.1. PMI (Project Management Institute)
8.2.2. PMBOK
8.2.3. Difference between Project, Program and Project Portfolio
8.2.4. Evolution of Organizations Working with Projects
8.2.5. Process Assets in Organizations

8.3. PMI Methodology for Project Management: Process

8.3.1. Groups of Processes
8.3.2. Knowledge Areas
8.3.3. Process Matrix

8.4. Agile Methodologies for Project Management

8.4.1. VUCA Context (Volatility, Uncertainty, Complexity and Ambiguity)
8.4.2. Agile Values
8.4.3. Principles of the Agile Manifesto

8.5. Agile Scrum Framework for Project Management

8.5.1. Scrum
8.5.2. The Pillars of the Scrum Methodology
8.5.3. The Values in Scrum

8.6. Agile Scrum Framework for Project Management. Process

8.6.1. The Scrum Process
8.6.2. Typified Roles in a Scrum Process
8.6.3. The Ceremonies of Scrum

8.7. Agile Scrum Framework for Project Management. Artifacts

8.7.1. Artefacts in the Scrum Process
8.7.2. The Scrum Team
8.7.3. Metrics for Evaluating the Performance of a Scrum Team

8.8. Agile KANBAN Framework for Project Management. Kanban Method

8.8.1. Kanban
8.8.2. Benefits of Kanban
8.8.3. Kanban Method. Components

8.8. Agile Kanban Framework for Project Management. Kanban Method Practices

8.9.1. The Values of Kanban
8.9.2. Principles of the Kanban Method
8.9.3. General Practices of the Kanban Method
8.9.4. Metrics for Kanban Performance Evaluation

8.10. Comparison: PMI, Scrum y Kanban

8.10.1. PMI – SCRUM
8.10.2. PMI – KANBAN
8.10.3. SCRUM - KANBAN

Module 9. Communication, Leadership and Team Management

9.1. Organizational Development in Business

9.1.1. Climate, Culture and Organizational Development in the Company
9.1.2. Human Capital Management

9.2. Direction Models Decision-Making

9.2.1. Paradigm Shift in Management Models
9.2.2. Management Process of the Technology Company
9.2.3. Decision-Making. Planning Instruments

9.3. Leadership. Delegation and Empowerment

9.3.1. Leadership
9.3.2. Delegation and Empowerment
9.3.3. Performance Evaluation

9.4. Leadership. Knowledge and Talent Management

9.4.1. Talent Management in the Company
9.4.2. Engagement Management in the Company
9.4.3. Improving Communication in the Company

9.5. Coaching Applied to Business

9.5.1. Executive Coaching
9.5.2. Team Coaching

9.6. Mentoring Applied to Business

9.6.1. Mentor Profile
9.6.2. The 4 Processes of a Mentoring Program
9.6.3. Tools and Techniques in a Mentoring Process
9.6.4. Benefits of Mentoring in the Business Environment

9.7. Team Management I. Interpersonal Relations

9.7.1. Interpersonal Relationships
9.7.1.1. Relational Styles: Approach
9.7.1.2. Effective Meetings and Agreements in Difficult Situations

9.8. Team Management II. The Conflicts

9.8.1. The Conflicts
9.8.2. Preventing, Addressing and Resolving Conflict
9.8.2.1. Strategies to Prevent Conflict
9.8.2.2. Conflict Management. Basic Principles
9.8.2.3. Conflict Resolution Strategies
9.8.3. Stress and Work Motivation

9.9. Team Management III. Negotiation

9.9.1. Negotiation at the Managerial Level in Technology Companies
9.9.2. Styles of Negotiation
9.9.3. Negotiation Phases
9.9.3.1. Barriers to Overcome in Negotiations

9.10. Team Management IV. Negotiation Techniques

9.19.1. Negotiation Techniques and Strategies
9.10.1.1. Strategies and Main Types of Negotiation
9.10.1.2. Negotiation Tactics and Practical Issues
9.10.2. The Figure of the Negotiating Subject

Module 10. Leadership, Ethics and Social Responsibility in Companies

10.1. Globalization and Governance 

10.1.1. Governance and Corporate Governance
10.1.2. The Fundamentals of Corporate Governance in Companies
10.1.3. The Role of the Board of Directors in the Corporate Governance Framework

10.2. Cross-Cultural Management 

10.2.1. Cross-Cultural Management Concept 
10.2.2. Contributions to Knowledge of National Cultures
10.2.3. Diversity Management

10.3. Business Ethics

10.3.1. Ethics and Morals
10.3.2. Business Ethics 
10.3.3. Leadership and Ethics in Companies

10.4. Sustainability

10.4.1. Sustainability and Sustainable Development
10.4.2. The 2030 Agenda
10.4.3. Sustainable Companies

10.5. Corporate Social Responsibility

10.5.1. International Dimensions of Corporate Social Responsibility
10.5.2. Implementing Corporate Social Responsibility
10.5.3. The Impact and Measurement of Corporate Social Responsibility

10.6. Responsible Management Systems and Tools

10.6.1. CSR: Corporate Social Responsibility
10.6.2. Essential Aspects for Implementing a Responsible Management Strategy
10.6.3. Steps for the Implementation of a Corporate Social Responsibility Management System
10.6.4. CSR Tools and Standards

10.7. Multinationals and Human Rights

10.7.1. Globalization, Multinational Corporations and Human Rights
10.7.2. Multinational Corporations and International Law
10.7.3. Legal Instruments for Multinationals in the Area of Human Rights

10.8. Legal Environment and Corporate Governance

10.8.1. International Rules on Importation and Exportation
10.8.2. Intellectual and Industrial Property
10.8.3. International Labor Law 

Module 11. People and Talent Management

11.1. Strategic People Management

11.1.1. Strategic Human Resources Management
11.1.2. Strategic People Management

11.2. Human Resources Management by Competencies

11.2.1. Analysis of the Potential
11.2.2. Remuneration Policy
11.2.3. Career/Succession Planning

11.3. Performance Evaluation and Performance Management

11.3.1. Performance Management
11.3.2. Performance Management: Objectives and Process

11.4. Innovation in Talent and People Management

11.4.1. Strategic Talent Management Models
11.4.2.  Talent Identification, Training and Development
11.4.3. Loyalty and Retention
11.4.4. Proactivity and Innovation

11.5. Motivation

11.5.1. The Nature of Motivation
11.5.2. Expectations Theory
11.5.3. Needs Theory
11.5.4. Motivation and Financial Compensation

11.6. Developing High-Performance Teams

11.6.1. High-Performance Teams: Self-Managed Teams
11.6.2. Methodologies for the Management of High-Performance Self-Managed Teams

11.7. Change Management

11.7.1. Change Management
11.7.2. Type of Change Management Processes
11.7.3. Stages or Phases in the Change Management Process

11.8. Negotiation and Conflict Management

11.8.1. Negotiation
11.8.2. Conflict Management
11.8.3. Crisis Management

11.9. Executive Communication

11.9.1. Internal and External Communication in the Corporate Environment
11.9.2. Communication Departments
11.9.3. The Person in Charge of Communication of the Company. The Profile of the Dircom

11.10. Productivity, Attraction, Retention and Activation of Talent

11.10.1. Productivity
11.10.2. Talent Attraction and Retention Levers

Module 12. Economic - Financial Management

12.1. Economic Environment

12.1.1. Macroeconomic Environment and the National Financial System
12.1.2. Financial Institutions
12.1.3. Financial Markets
12.1.4. Financial Assets
12.1.5. Other Financial Sector Entities

12.2. Executive Accounting

12.2.1. Basic Concepts
12.2.2. The Company's Assets 
12.2.3. The Company's Liabilities 
12.2.4. The Company's Net Worth 
12.2.5. Results Research 

12.3. Information Systems and Business Intelligence

12.3.1. Fundamentals and Classification
12.3.2. Cost Allocation Phases and Methods
12.3.3. Choice of Cost Center and Impact

12.4. Budget and Management Control

12.4.1. The Budget Model
12.4.2. Capital Budget
12.4.3. The Operating Budget
12.4.5. Treasury Budget
12.4.6. Budget Monitoring

12.5. Financial Management

12.5.1. The Company's Financial Decisions
12.5.2. Financial Department
12.5.3. Cash Surpluses
12.5.4. Risks Associated with Financial Management
12.5.5. Financial Administration Risk Management

12.6. Financial Planning

12.6.1. Definition of Financial Planning
12.6.2. Actions to Be Taken in Financial Planning
12.6.3. Creation and Establishment of the Business Strategy
12.6.4. The Cash Flow Table
12.6.5. The Working Capital Table

12.7. Corporate Financial Strategy

12.7.1. Corporate Strategy and Sources of Financing
12.7.2. Financial Products for Corporate Financing

12.8. Strategic Financing

12.8.1. Self-Financing
12.8.2. Increase in Equity
12.8.3. Hybrid Resources
12.8.4. Financing Through Intermediaries

12.9. Financial Analysis and Planning

12.9.1.  Analysis of the Balance Sheet
12.9.2. Income Statement Analysis
12.9.3. Profitability Analysis

12.10. Analyzing and Solving Cases/Problems

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

Module 13. Commercial and Strategic Marketing Management

13.1. Commercial Management

13.1.1. Conceptual Framework of Commercial Management
13.1.2. Business Strategy and Planning
13.1.3. The Role of Sales Managers

13.2. Marketing

13.2.1. The Concept of Marketing
13.2.2. Basic Elements of Marketing
13.2.3. Marketing Activities of the Company

13.3. Strategic Marketing Management

13.3.1. The Concept of Marketing Strategic
13.3.2. Concept of Strategic Marketing Planning
13.3.3. Stages in the Process of Strategic Marketing Planning

13.4. Digital Marketing and E-Commerce

13.4.1. Digital Marketing and E-Commerce Objectives
13.4.2. Digital Marketing and Media Used
13.4.3. E-Commerce. General Context
13.4.4. Categories of E-Commerce
13.4.5. Advantages and Disadvantages of E-Commerce versus Traditional Commerce

13.5. Digital Marketing to Reinforce a Brand

13.5.1. Online Strategies to Improve Your Brand's Reputation
13.5.2. Branded Content and Storytelling

13.6. Digital Marketing to Attract and Retain Customers

13.6.1. Loyalty and Engagement Strategies through the Internet
13.6.2. Visitor Relationship Management 
13.6.3. Hypersegmentation

13.7. Managing Digital Campaigns

13.7.1.  What Is a Digital Advertising Campaign?
13.7.2. Steps to Launch an Online Marketing Campaign
13.7.3. Mistakes in Digital Advertising Campaigns

13.8. Sales Strategy

13.8.1.  Sales Strategy
13.8.2. Sales Methods

13.9. Corporate Communication

13.9.1. Concept
13.9.2. The Importance of Communication in the Organization
13.9.3. Type of Communication in the Organization
13.9.4. Functions of Communication in the Organization
13.9.5. Elements of Communication
13.9.6. Communication Problems
13.9.7. Communication Scenarios

13.10. Digital Communication and Reputation

13.10.1. Online Reputation
13.10.2. How to Measure Digital Reputation?
13.10.3. Online Reputation Tools
13.10.4. Online Reputation Report
13.10.5. Online Branding

Module 14. Executive Management

14.1. General Management

14.1.1.  The Concept of General Management
14.1.2. The Role of the General Manager
14.1.3. The Chief Executive Officer and Their Functions
14.1.4. Transforming the Work of Management

14.2. Manager Functions: Organizational Culture and Approaches

14.2.1. Manager Functions: Organizational Culture and Approaches

14.3. Operations Management

14.3.1.  The Importance of Management
14.3.2. Value Chain
14.3.3. Quality Management

14.4. Public Speaking and Spokesperson Education

14.4.1.  Interpersonal Communication
14.4.2. Communication Skills and Influence
14.4.3. Communication Barriers

14.5. Personal and Organizational Communications Tools

14.5.1. Interpersonal Communication
14.5.2. Interpersonal Communication Tools
14.5.3.  Communication in the Organization
14.5.4. Tools in the Organization

14.6. Communication in Crisis Situations

14.6.1. Crisis
14.6.2. Phases of the Crisis
14.6.3. Messages: Contents and Moments

14.7. Preparation of a Crisis Plan

14.7.1. Analysis of Possible Problems
14.7.2. Planning
14.7.3. Adequacy of Personnel

14.8. Emotional Intelligence

14.8.1. Emotional Intelligence and Communication
14.8.2.  Assertiveness, Empathy and Active Listening
14.8.3. Self-Esteem and Emotional Communication

14.9. Personal Branding

14.9.1. Strategies for Personal Brand Development
14.9.2. Personal Branding Laws
14.9.3. Tools for Creating Personal Brands

14.10. Leadership and Team Management

14.10.1. Leadership and Leadership Styles
14.10.2. Leader Capabilities and Challenges
14.10.3. Managing Change Processes
14.10.4. Managing Multicultural Teams

You will be able to access the Virtual Campus at any time and download the contents to consult them whenever you wish”