Succeed with the best and acquire the knowledge and skills you need to embark on a career in the advanced IT sector"

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. 

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 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.  

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”

This Executive Master’s Degree in Corporate Technical Data Science Management is a tailor-made program that is taught in a 100% online format so students can choose the time and place that best suits their availability, schedules and interests.  A program that takes place over 12 months and is intended to be a unique and stimulating experience that lays the foundation for your success as a professional. 

What you study is very important. The abilities and skills you acquire are fundamental. You won't find a more complete syllabus than this one, believe us"  

Syllabus

This Executive Master’s Degree in Corporate Technical Data Science Management from TECH Global University is an intensive programme that prepares students to face challenges and business decisions in the field of Corporate Technical Data Science Management.

The content of this Executive Master’s Degree in Corporate Technical Data Science Management is designed to promote the development of skills that enable more rigorous decision-making in uncertain environments. 

Over the course of 1,500 hours, the student analyzes a plethora of practical cases through individual and teamwork. It is, therefore, an authentic immersion in real business situations.

This Executive Master’s Degree deals in depth with the world of computer science in the business world, and is designed to prepare professionals who understand Corporate Technical Data Science 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 field of business management and administration. 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 skills to solve critical situations in a creative and efficient way.

This Executive Master’s Degree takes place over 12 months and is divided into 10 modules: 

Módulo 1. Main Information Management Systems
Módulo 2. Data Types and Data Life Cycle
Módulo 3. Number Machine Learning 
Módulo 4. Web Analytics
Módulo 5. Scalable and Reliable Mass Data Usage Systems
Módulo 6. System Administration for Distributed Deployments
Módulo 7. Internet of Things
Módulo 8. Project Management and Agile Methodologies
Módulo 9. Communication, Leadership and Team Management

Where, when, and how it is taught

TECH offers the possibility of taking this program completely online. Over the course of the 12 months, the student will be able to access all the contents of this program at any time, allowing them to self-manage their study time. 

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 ERP 

1.5. CRM: The Implementation Project  

1.5.1. The 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. Sales or Loyalty 
1.6.3. Factors for Success in our Loyalty System 
1.6.4. Multi-Channel 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 the Informed Customer 
1.7.3. Listening to the Client 

1.8. CRM: Dissatisfaction Prevention  

1.8.1. Customer Cancellations 
1.8.2. Detecting Errors in Time 
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. Design and Realization of the Event 
1.9.3. Actions from the Department 
1.9.4. Analysis of Results 

1.10. Relational Marketing 

1.10.1. Implantation: Errors 
1.10.2. Methodology, Segmentation and Processes  
1.10.3. Performance, According to the Department 
1.10.4. CRM Tools  

Module 2. Data Types and Data Life Cycle 

2.1. Statistics 

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

2.2. Types of Data Statistics 

2.2.1. According to 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. According to their Shape  

2.2.2.1. Numeric 
2.2.2.2. Text  
2.2.2.3. Logical 

2.2.3. According to its Source 

2.2.3.1. Primary 
2.2.3.2. Secondary 

2.3. Life Cycle of Data 

2.3.1. Stages of the Cycle 
2.3.2. Milestones of the Cycle 
2.3.3. FAIR Principles 

2.4. Initial Stages of the Cycle 

2.4.1. Definition of Goals 
2.4.2. Determination of Resource Requirements 
2.4.3. Gantt Chart 
2.4.4. Data Structure 

2.5. Data Collection 

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

2.6. Data Cleaning 

2.6.1. Phases of Data Cleansing 
2.6.2. Data Quality 
2.6.3. Data Manipulation (with 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. Elements 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. Uses 
2.9.3. Security 

Module 3. Number Machine Learning  

3.1. Knowledge in Databases

3.1.1. Data Pre-Processing 
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 Neighbour (KNN) Algorithms.
3.3.3. Metrics for Sorting 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 Grouping
3.5.2. Partitional Grouping
3.5.3. Metrics for Clustering Algorithms

3.6. Association Rules 

3.6.1. Measures of Interest 
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 Forests” 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 Networks
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 Modelling
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. Process of Analysis 

4.2. Google Analytics 

4.2.1. Google Analytics 
4.2.2. Use  
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. Personalized Labelling 

4.5. Google Analytics Configuration 

4.5.1. Installation: Creating an Account 
4.5.2. Versions of the Tool: UA/GA4 
4.5.3. Tracking Label 
4.5.4. Conversion Objectives 

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. In Real Time 
4.7.2. Audience 
4.7.3. Acquisition 
4.7.4. Behaviour 
4.7.5. Conversions 
4.7.6. E-Commerce 

4.8. Google Analytics Advanced Reports 

4.8.1. Personalized Reports 
4.8.2. Panels 
4.8.3. APIs 

4.9. Filters and Segments 

4.9.1. Filter 
4.9.2. Segment 
4.9.3. Types of Segments: Predefined/Customized 
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. Scales 
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. Trees B 

6.4. Services, Message Passing and Data Encoding Formats 

6.4.1. Data Flow in REST Services 
6.4.2. Data Flow in Message Passing 
6.4.3. 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

6.8. Batch Processing 

6.8.1. Batch Processing 
6.8.2. MapReduce 
6.8.3. 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. Use Cases: 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 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. Case Study: 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. Case Study: 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 Management

8.1.1. Projects 
8.1.2. Phases to a Project 
8.1.3. Project 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 Methodologies 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. Artefacts

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

8.9. 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 and 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. Organisational Development in Business 

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

9.2. Management Models: Decision-Making 

9.2.1. Paradigm Shift in Management Models 
9.2.2. Management Process of a 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. Corporate Talent Management 
9.4.2. Corporate Engagement Management 
9.4.3. Improving Corporate Communication 

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 
9.7.2. Relational Styles: Approach 
9.7.3. Effective Meetings and Agreements in Difficult Situations 

9.8. Team Management II: Conflicts 

9.8.1. Conflicts 
9.8.2. Preventing, Addressing and Resolving Conflict 

9.8.2.1. Conflict Prevention Strategies 
9.8.2.2. Conflict Management: Basic Principles 

9.8.3. Conflict Resolution Strategies 
9.8.4. 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 

9.10.1. 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 Negotiator 

A unique, key and decisive educational experience to boost your professional development and make the definitive leap”