An online program that will provide you with a broad vision of the multiple businesses that can arise with the application of Big Data techniques"

The leader of a project or Start-Up is much more than the person who knows how to manage the Human Resources of the company, it is currently the person who dominates this field, but also the Big Data, or the large volume of existing information on the network, and that well focused is able to bring priceless value to the company. To be able to manage Business Intelligence in the company, it is necessary to acquire advanced and updated knowledge that can be achieved with this Hybrid Master.

This program, aimed primarily at computer engineers who wish to reorient their work towards the world of business intelligence, or established professionals in the field of BI who wish to advance their knowledge, will delve into the transformation of data-driven business. The specialized teaching team with extensive experience in digital companies, consulting or Marketing will be responsible for providing the latest knowledge in this field, where data will be the star. In this way, the most recent technological tools and techniques used for their visualization and analysis will be shown, also providing a future vision with applications in virtual reality, augmented reality and artificial intelligence.

Likewise, this program will provide cases of real simulations that will allow students to find a direct relationship between the theoretical framework and the direct application in the professional field. The existing legal regulations, marketing strategies or the optimization of the human capital of the company will be other points addressed in this qualification, which will provide extensive knowledge to the IT professional who wishes to pursue a career in this area.

An excellent opportunity for students who wish not only to acquire knowledge, but also to live a real professional experience, where they can apply all the learning received. At the end of the first stage of the Hybrid Master, students will have a practical stay in a relevant company in the sector, where they will be able, together with professionals in this field, to complete their development in the field of Business Intelligence.

This qualification offers you the flexibility and convenience of 24-hour access to all its multimedia content"

This Hybrid Master's Degree in MBA in Business Intelligence Management contains the most complete and up-to-date program on the market. The most important features include:

• Development of more than 100 cases presented by professionals from different digital areas of companies and consulting firms
• The graphic, schematic and practical contents with which they are conceived, gather information on those disciplines that are essential for professional practice
• Plans for a dynamic business model that supports your company's growth in intangible resources
• Analysis of the sessions of a website in order to better understand your customers
• Plan proper data management, collection and cleansing in accordance to business objectives
• All of this will be complemented by theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection
• Furthermore, you will be able to do an internship in one of the best specialized centers in the world

Take an intensive 3-week internship in a reference company and live an experience that will make you grow in the digital business area"

In this Master's proposal, of a professional nature and hybrid learning modality, the program is aimed at updating professionals who perform their functions in companies or who wish to lead a project, and who require a high level of qualification. The contents are based on the latest scientific evidence, and oriented in an educational way to integrate theoretical knowledge into practice, and the theoretical-practical elements will facilitate knowledge update and decision-making in patient management.

Thanks to its multimedia content developed with the latest educational technology, they will allow the professional a situated and contextual learning, that is to say, a simulated environment that will provide an immersive learning programmed to prepare in real situations. This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise throughout the program. For this purpose, the students will be assisted by an innovative interactive video system created by renowned and experienced experts.

This Hybrid Master will show you the most used metrics tools in Digital Marketing"

Have you thought about leading a Start-up? Acquire all the necessary knowledge with this Hybrid Master and graduate in 12 months with the most prominent experts"

The syllabus of this Hybrid Master has been developed by a specialized teaching team that will delve into the main aspects that make up the Business Intelligence in the company. Therefore, students will be able to access the complete syllabus of this teaching, which consists of 10 modules from the first day. In this way, the professional will be able to connectto the content, from any electronic device, at any time of day which can also be distributed according to their needs. In addition, the long hours of study will be reduced in this program thanks to the Relearning system that TECH applies in all its degrees.

Access to a syllabus with the latest content on Artificial Intelligence and its application in companies"

Module 1. Enterprise Business Intelligence

1.1. Enterprise Business Intelligence

1.1.1. The World of Data
1.1.2. Relevant Concepts
1.1.3. Main Characteristics
1.1.4. Solutions in Today's Market
1.1.5. Overall Architecture of a BI Solution
1.1.6. Cybersecurity in BI and Data Science

1.2. New Business Concept

1.2.1. Why BI
1.2.2. Obtaining Information
1.2.3. BI in the Different Departments of the Company
1.2.4. Reasons to Invest in BI

1.3. Data Warehouse

1.3.1. Definition and Objectives Data Warehouse and Data Mart
1.3.2. Architecture
1.3.3. Dimensional Modeling and its Types of Diagrams
1.3.4. Extraction, Transformation and Loading Process (ETL)
1.3.5. Metadata

1.4. Big Data and Data Capture

1.4.1. Capture
1.4.2. Transformation
1.4.3. Storage

1.5. Reporting Business Intelligence (BI)

1.5.1. B.D. Structures
1.5.2. BB.DD. OLTP and OLAP
1.5.3. Examples

1.6. Dashboards or Balanced Scorecards

1.6.1. Control Panels
1.6.2. Decision Support Systems
1.6.3. Executive Information Systems

1.7. Deep Learning

1.7.1. Deep Learning
1.7.2. Deep Learning Fundamentals
1.7.3. Deep Learning Applications

1.8. Machine Learning

1.8.1. Machine Learning
1.8.2. Machine Learning Fundamentals
1.8.3.  Machine Learning Applications
1.8.4. Deep Learning vs. Machine Learning

1.9. BI Tools and Solutions

1.9.1. Choosing the Best Tool
1.9.2. Microsoft Power BI, MicroStrategy y Tableau
1.9.3. SAP BI, SAS BI and Qlikview
1.9.4. Prometheus

1.10. BI Project Planning and Management

1.10.1. First Steps to Define a BI Project
1.10.2. BI Solution for Your Company
1.10.3. Requirements and Objectives

Module 2. Business Perspective

2.1. The Company

2.1.1. Capital, Investment and Risk
2.1.2. Organizational Morphology: Size, Shape, Activity and Sectors
2.1.3. Organization and Resources
2.1.4. Management and Their Needs

2.2. Company: Market and Customer

2.2.1. Market and Customer
2.2.2. Market Analysis and Segmentation
2.2.3. Direct and Indirect Competition
2.2.4. Competitive Advantage

2.3. Business strategy

2.3.1. Business Strategy
2.3.2. SWOT Analysis
2.3.3. Objectives and deadlines
2.3.4. Measuring Results: Knowing the Reality
2.3.5. Key Indicators

2.4. Information as an Asset

2.4.1. Information and Management
2.4.2. Life Cycle Information
2.4.3. Operational System and Strategic System

2.5. Integral Control Panel

2.5.1. Operational, Tactical and Strategic Scorecards
2.5.2. CMI Definition
2.5.3. Financial Perspective
2.5.4. Customer Perspective
2.5.5. Internal Processes Perspective
2.5.6. Learning and Growth Perspective

2.6. Productivity Analysis

2.6.1. Income, Expenditures, Investment and Consumption
2.6.2. Cost Analysis and Allocation
2.6.3. ROI and other Ratios of interest

2.7. Distribution and Sales

2.7.1. Relevance of the Department
2.7.2. Channels and Equipment
2.7.3. Types of Sales and Consumption

2.8. Other Common Areas

2.8.1. Production and Service Delivery
2.8.2. Distribution and Logistics
2.8.3. Commercial Communication
2.8.4. Inbound Marketing

2.9. Data Management

2.9.1. Roles and Responsibilities
2.9.2. Stakeholder Identification
2.9.3. Information Management Systems
2.9.4. Type of Operating Systems
2.9.5. Strategic or Decision Support Systems
2.9.6. Platforms for information: Cloud Computing vs On Premise

2.10. Exploring the Information

2.10.1. Intro SQL: Relational Databases Basic Concepts
2.10.2. Networks and Communications: Public/Private Networks, Network/Subnet/Router Address and DNS. VPN Tunnel and SSH
2.10.3. Operational System: Standardized Data Templates
2.10.4. Strategic System: OLAP, Multidimensional Model and Graphical Dashboards
2.10.5. Strategic analysis of BB's. DD and report composition

Module 3. Data-driven business transformation

3.1. Big Data

3.1.1. Big Data in Enterprises
3.1.2. Concept of Value
3.1.3. Value Project Management

3.2. Marketing Digital

3.2.1. Digital Marketing
3.2.2. Benefits of Digital Marketing

3.3. Action Plan

3.3.1. Campaigns and Types
3.3.2. Redemption and Drive
3.3.3. Types of Strategies
3.3.4. Digital Marketing Plans

3.4. Execution of the Marketing Plan

3.4.1. Customer Journey (Baseline-Campaign-Redemption-Improvement) and Digital Marketing
3.4.2. Web Integration of Digital Marketing Tools
3.4.3. Digital Marketing Tools

3.5. Customer Journey

3.5.1. Customer Life Cycle
3.5.2. Association of Campaigns to the Life Cycle
3.5.3. Campaign Metrics

3.6. Data Management for Campaigns

3.6.1. Data Lab and Data Mart
3.6.2. Campaign Creation Tools
3.6.3. Drive Methods

3.7. GDPR in Digital Marketing

3.7.1. Data Anonymization and Manipulation of Personal Data
3.7.2. Robinson Concept
3.7.3. Exclusion lists

3.8. Control Panels

3.8.1. KPIs
3.8.2. Audience
3.8.3. Data Science
3.8.4. Storytelling

3.9. Customer Analysis and Characterization

3.9.1. 360º Customer Vision
3.9.2. Relation of Analysis to Tactical Actions
3.9.3. Analysis Tools

3.10. Business Examples Applying Big Data Techniques

3.10.1. Upselling/Cross-Selling
3.10.2. Propensity Models
3.10.3. Risk Models 
3.10.4. Predictions
3.10.5. Image Processing

Module 4. Data Visualization

4.1. Data Visualization

4.1.1. Data visualization
4.1.2. Importance of Data Analysis and Visualization
4.1.3. Evolution

4.2. The Design

4.2.1. Use of Color
4.2.2. Composition and Typography
4.2.3. Recommendations

4.3. Types of Data

4.3.1. Qualitative
4.3.2. Quantitative
4.3.3. Temporary Data

4.4. Data Sets

4.4.1. Files
4.4.2. Databases
4.4.3. Open Data
4.4.4. Streaming Data

4.5. Common Types of Representation

4.5.1. Columns
4.5.2. Bars
4.5.3. Lines
4.5.4. Areas
4.5.5. Dispersion

4.6. Advanced Types of Representation

4.6.1. Circulars
4.6.2. Rings
4.6.3. Bubbles 
4.6.4. Maps

4.7. Application by Area

4.7.1. Political Science and Sociology
4.7.2. Science
4.7.3. Marketing
4.7.4. Health and Well-being
4.7.5. Meteorology
4.7.6. Business and Finance

4.8. Storytelling

4.8.1. Importance of Storytelling
4.8.2. Storytelling History
4.8.3. Application of Storytelling

4.9. Visualization Software

4.9.1. Commercials
4.9.2. Free
4.9.3. Online
4.9.4. Free Software

4.10. The Future of Data Visualization

4.10.1. Virtual reality
4.10.2. Augmented Reality
4.10.3. Artificial Intelligence

Module 5. Programming for data analysis

5.1. Programming for data analysis

5.1.1. Language for Data Analysis
5.1.2. Evolution and Characteristics of the Main Tools
5.1.3. Installation and Configuration

5.2. Types of Data

5.2.1. Basic Types
5.2.2. Complex Types
5.2.3. Other Structures

5.3. Structures and Operations

5.3.1. Data Operations
5.3.2. Control Structures
5.3.3. File Operations

5.4. Data Extraction and Analysis

5.4.1. Statistical Summaries
5.4.2. Univariate Analysis
5.4.3. Multivariate Analysis

5.5. Visualization

5.5.1. Univariate Graphs
5.5.2. Multivariable Graphs
5.5.3. Other Charts of Interest

5.6. Pre-processing

5.6.1. The Importance of Data Quality
5.6.2. Outlier Detection and Analysis
5.6.3. Other Dataset Quality Factors

5.7. Advanced Pre-processing

5.7.1. Subsampling
5.7.2. Resampling
5.7.3. Dimensionality Reduction

5.8. Data Modeling

5.8.1. Modeling Phases
5.8.2. Division of the Data Set
5.8.3. Metrics for Prediction

5.9. Advanced Data Modeling

5.9.1. Unsupervised Models
5.9.2. Supervised Models
5.9.3. Libraries for Modeling

5.10. Tools and Good Practices

5.10.1. Best Practices for Modeling
5.10.2. The Tools of a Data Analyst
5.10.3. Conclusion and Bookstores of Interest

Module 6. Digital Marketing Analytics

6.1. Web Analytics

6.1.1. Web Analytics Use 
6.1.2. History
6.1.3. Applicable Methodology

6.2. Google Analytics

6.2.1. About Google Analytics
6.2.2. Metrics vs. Dimension
6.2.3. Measurement Objectives

6.3. Reports

6.3.1. Basic Metric
6.3.2. Advanced Metrics or KPIs (Key Performance Indicators)
6.3.3. Conversions

6.4. Dimensions

6.4.1. Campagin/Keyword)
6.4.2. Source/Media
6.4.3. Content

6.5. Universal Analytics vs. Google Analytics4

6.5.1. UA Differences vs. GA4
6.5.2. Advantages and Limitations
6.5.3. Use of UA and GA4 Tools

6.6. Setting up Google Analytics

6.6.1. Installation and Integration
6.6.2. Structure of Universal Analytics: Accounts, Properties and Views
6.6.3. Conversion Goals and Funnels

6.7. Personalized Marketing in the Luxury Market

6.7.1. Real-Time Analytics
6.7.2. Audience Analytics
6.7.3. Purchase Analytics
6.7.4. Behavior Analytics
6.7.5. Conversion Analytics

6.8. Advanced Reports

6.8.1. Panels 
6.8.2. Personalized Reports
6.8.3. API

6.9. Segments

6.9.1. Difference between Segment and Filter
6.9.2. Types of Segments: Predefined / Customised
6.9.3. Remarketing

6.10. Digital Analytics

6.10.1. Measurement
6.10.2. Implementation
6.10.3. Conclusions

Module 7. Data management

7.1. Statistics

7.1.1. Statistics: Descriptive Statistics, Statistical Inferences
7.1.2. Population, Sample, Individual
7.1.3. Variables: Definition, Measurement Scales

7.2. Types of Data Statistics

7.2.1. According to Type

7.2.1.1. Quantitative: Continuous Data and Discrete Data
7.2.1.2. Qualitative: Binomial Data, Nominal Data and Ordinal Data

7.2.2. According to Its Form: Numerical, Text, Logical
7.2.3. According to Their Source: Primary, Secondary

7.3. Data Management Planning

7.3.1. Definition of Objectives
7.3.2. Determination of Available Resources
7.3.3. Establishment of Time Lapses
7.3.4. Data Structure

7.4. Data Collection

7.4.1. Methodology of Data Collection
7.4.2. Data Collection Tools
7.4.3. Data Collection Channels

7.5. Data Cleaning

7.5.1. Phases of Data Cleansing
7.5.2. Data Quality
7.5.3. Data Manipulation (with R)

7.6. Data Analysis, Interpretation and Evaluation of Results

7.6.1. Statistical Measures
7.6.2. Relationship Indices
7.6.3. Data Mining

7.7. Data Visualization

7.7.1. Suitable Display According to Data Type
7.7.2. End-User Considerations
7.7.3. Executive Models of Results Presentation

7.8. Data Warehouse

7.8.1. Elements that Comprise it
7.8.2. Design
7.8.3. Aspects to Consider

7.9. Data Availability

7.9.1. Access
7.9.2. Uses
7.9.3. Security/Safety

7.10. Practical Applications

7.10.1. Data Exploration
7.10.2. Manipulation and Adjustment of Patterns and Structures
7.10.3. Test Application and Modeling

Module 8. Data Protection

8.1. Data Protection Regulation

8.1.1. Regulatory Framework
8.1.2. Definitions
8.1.3. Subjects Obliged to Comply with the Regulations

8.1.3.1. Differences between controllers, co-responsible parties and data processors

8.1.4. Data Protection Officer

8.2. Harmonized Regulation of Artificial Intelligence: Proposal for a European Regulation

8.2.1. Prohibited Practices
8.2.2. High-Risk Artificial Intelligence Systems
8.2.3. Innovation Support Measures

8.3. Principles Relating to the Processing of Personal Data

8.3.1. Fairness, Loyalty and Transparency
8.3.2. Purpose Limitation
8.3.3. Data Minimization, Accuracy and Limitation of Retention Period
8.3.4. Integrity and Confidentiality
8.3.5. Proactive Responsibility

8.4. Basis of Lawfulness or Legitimacy and Authorizations for the Processing, Including, if Applicable, the Communication of the Data

8.4.1. Consent
8.4.2. Contractual Relationship or Pre-contractual Measures
8.4.3. Fulfillment of a Legal Obligation
8.4.4. Protection of Vital Interests of the Data Subject or Another Person
8.4.5. Public Interest or Exercise of Public Powers
8.4.6. Legitimate Interest: Weighing of interests

8.5. Individuals Rights

8.5.1. Transparency and Information
8.5.2. Access
8.5.3. Rectification and Deletion (Right to be Forgotten), Limitation and Portability
8.5.4. Opposition and Automated Individual Decisions
8.5.5. Limits to Rights

8.6. Data Protection by Design: Analysis and Management of Personal Data Processing Risks

8.6.1. Identification of Risks and Threats to the Rights and Freedoms of Individuals
8.6.2. Risk Assessment
8.6.3. Risk Management Plan

8.7. Techniques for Ensuring Compliance with Data Protection Regulations

8.7.1. Identification of Proactive Accountability Measures
8.7.2. Organizational measures
8.7.3. Technical Measures
8.7.4. The Register of Processing Activities
8.7.5. Security Breach Management
8.7.6. Codes of Conduct and Certifications

8.8. Data Protection Impact Assessment (DPIA)

8.8.1. EIPD Needs Assessment
8.8.2. Evaluation Methodology
8.8.3. Identification of Risks and Threats
8.8.4. Prior Consultation with the Supervisory Authority

8.9. Contractual Regulation between Those Responsible, Those in charge and, Where Applicable, Other International Data

8.9.1. Data Access or Data Processing Contract
8.9.2. Contracts between Co-Responsible Parties
8.9.3. Responsibilities of the Parties
8.9.4. Definition and Safeguards to be Adopted in International Transfers

8.10. Control Authorities. Violations and Penalties

8.10.1. Violations
8.10.2. Fines
8.10.3. Penalty Procedure
8.10.4. Control Authorities and Cooperation Mechanisms

Module 9. Business Intelligence and Artificial Intelligence: Strategies and Applications

9.1. Financial Services

9.1.1. The Implications of Artificial Intelligence (AI) in Financial Services. Opportunities and Challenges
9.1.2. Case Uses 
9.1.3. Potential Risks Related to the Use of AI
9.1.4. Potential Future Developments/Uses of AI

9.2. Implications of Artificial Intelligence in the Healthcare Service

9.2.1. Implications of AI in the Healthcare Sector. Opportunities and Challenges
9.2.2. Case Uses

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

9.3.1. Potential Risks Related to the Use of AI
9.3.2. Potential Future Developments/Uses of AI

9.4. Retail

9.4.1. Implications of AI in Retail. Opportunities and Challenges
9.4.2. Case Uses
9.4.3. Potential Risks Related to the Use of AI
9.4.4. Potential Future Developments/Uses of AI

9.5. Industry 4.0

9.5.1. Implications of AI in the 4.0 Industry. Opportunities and Challenges
9.5.2. Case Uses

9.6. Potential Risks Related to the use of AI in the 4.0 Industry

9.6.1. Case Uses
9.6.2. Potential Risks Related to the Use of AI
9.6.3. Potential Future Developments/Uses of AI

9.7. Public Administration

9.7.1. Implications of AI in Public Administration: Opportunities and Challenges
9.7.2. Case Uses
9.7.3. Potential Risks Related to the Use of AI
9.7.4. Potential Future Developments/Uses of AI

9.8. Educational

9.8.1. Implications of AI in Educational: Opportunities and Challenges
9.8.2. Case Uses
9.8.3. Potential Risks Related to the Use of AI
9.8.4. Potential Future Developments/Uses of AI

9.9. Forestry and Agriculture

9.9.1. Implications of AI in Forestry and Agriculture. Opportunities and Challenges
9.9.2. Case Uses
9.9.3. Potential Risks Related to the Use of AI
9.9.4. Potential Future Developments/Uses of AI

9.10. Human resources.

9.10.1. Implications of AI for Human Resources Opportunities and Challenges
9.10.2. Case Uses
9.10.3. Potential Risks Related to the Use of AI
9.10.4. Potential Future Developments/Uses of AI

Module 10. Optimization of the company's human capital

10.1. Human Capital in the Company

10.1.1. Value of Human Capital in the Technological World
10.1.2. Managerial Skills
10.1.3. Paradigm Shift in Management Models

10.2. Manager's Skills

10.2.1. Management Process
10.2.2. Management Functions
10.2.3. Group Leadership Management in Companies Group Relations

10.3. Communication in the Company

10.3.1. The Company's Communication Process
10.3.2. Interpersonal Relations in the Company
10.3.3. Communication Techniques for Change

10.3.3.1. Storytelling
10.3.3.2. Assertive Communication Techniques. Feedback, Consensus

10.4. Business Coaching

10.4.1. Business Coaching
10.4.2. The Practice of Coaching
10.4.3. Types of Coaching and Coaching in Organizations

10.4.3.1. Coaching as a Leadership Style

10.5. Business Mentoring

10.5.1. Mentoring in the Company
10.5.2. The 4 Processes of a Mentoring Program
10.5.3. Benefits of this Business Tool

10.6. Mediation and Conflict Resolution in the Company

10.6.1. The Conflicts
10.6.2. Preventing, Addressing and Resolving Conflict
10.6.3. Stress and Work Motivation

10.7. Negotiation Techniques 

10.7.1. Negotiation at the Managerial Level in Technology Companies
10.7.2. Strategies and Main Types of Negotiation

10.7.2.1. The Figure of the Negotiating Subject

10.8. Enterprise Change Management

10.8.1. Factors of Organizational Change
10.8.2. Strategic Planning
10.8.3. Organizational Change Management

10.8.3.1. For Intangible Change: Teams, Communication, Culture, Leadership
10.8.3.2. For basic or Tangible Change: Goal Setting, Performance Measurement, Learning, Recognition and Rewards

10.9. Techniques for Improving Equipment Performance

10.9.1. Teamwork Techniques
10.9.2. Delegating in work Equipment

10.10. Focus Group. Classification

10.10.1. The Role of the Dynamizer
10.10.2. Group Dynamics Techniques

10.10.2.1. Brainstorming
10.10.2.2. Phillips 6/ 6
10.10.2.3. Hot Air Balloon D

This academic itinerary is exclusive to TECH and you will be able to develop it at your own pace thanks to its 100% online Relearning methodology"