With this MBA in Data Science Management you will be the best possible candidate for the management of any team, bringing with you a unique analytical and technical point of view’’

Companies which are up-to-date with the reality of the digital world we live in require multidisciplinary professionals. These teams are generally made up of highly qualified individuals who require an even more specialized level of management which has been adapted according to the level of their expertise. This MBA in Data Science Management covers this niche market, providing students with a unique and useful set of skills with which to lead teams. Using data science and deep analytics, the student will be able to make quick decisions from a global perspective with a comprehensive understanding of the reality that surrounds these complex and changing business environments.

The training material covers all aspects necessary to successfully lead, from an analytical perspective, the management, manipulation and interpretation of collected data; the optimal devices and platforms for data management; data mining, data graphing and data-driven predictive models; and finally, leadership and effective communication with large groups at the workplace. In addition to all of the above, there are other complementary, more technical skills that make this a wide-ranging and comprehensive course.

Moreover, the students will have the total freedom to take on this program at their own pace, since it is a 100% online qualification, without fixed schedules or the obligation to attend in person. The didactic material is accessible at all times and the student can adapt the learning experience to suit their personal and professional obligations.  

With the skill set provided by this MBA in Data Science Management ,you will have everything it takes to launch your career to new heights and achieve your goals’’

This MBA in Data Science Management contains the most complete and up-to-date educational program on the market. The most important features include: 

• The development of case studies presented by experts in leadership and data analytics 
• The graphic, schematic, and eminently 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 
• Special emphasis on innovative methodologies in the field of data science 
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection work 
• Content that is accessible from any fixed or portable device with an Internet connection 

Leaders with the greatest skills and knowledge are the ones who can make a difference in a contested and competitive business environment.Make a difference and succeed where others have failed with leadership and data science skills’’

The faculty includes professionals belonging to the field of business management and data science, who bring to this program their vast work experience, as well as recognized specialists from prestigious societies and universities of reference.

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 deliver an immersive learning experience, programmed to train in real situations.

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

This is your moment to reach the zenith of your professional career. Specialize with this MBA in Data Science Management and apply for the jobs you've always dreamed of"

TECH offers you the possibility to study at your own pace. You have a unique opportunity to give your resume a unique touch of class and stand out from the rest of the competition"

The MBA in Data Science Management is composed of 19 modules made up of a variety of different topics and subtopics, all the information is compiled in a precise and clear way so that the students will not have any difficulties when it comes to carrying out their studies. During the course, the students will learn innovative work methodologies, different forms of data management and storage, as well as how to solve and mediate any possible conflicts that may arise in the workplace, alongside additional key information that will also prove useful to their professional career in management. 

This MBA in Data Science Management gives you the opportunity to acquire the best knowledge in the most concise and precise way possible’’

Module 1. Data Analytics in the Business Organization 

1.1. Business Analysis 

1.1.1. Business Analysis 
1.1.2. Data Structure 
1.1.3. Phases and Elements 

1.2. Data Analytics in the Company 

1.2.1. Scorecards and KPIs by Departments 
1.2.2. Operational, Tactical and Strategic Reporting 
1.2.3. Data Analytics Applied to Each Department 

1.2.3.1. Marketing and Communication 
1.2.3.2. Commercial 
1.2.3.3. Customer Service 
1.2.3.4. Purchasing 
1.2.3.5. Administration 
1.2.3.6. Human Resources 
1.2.3.7. Production 
1.2.3.8. IT 

1.3. Marketing and Communication 

1.3.1. KPIs to be Measured, Applications and Benefits 
1.3.2. Marketing Systems and Data Warehouse 
1.3.3. Implementation of a Data Analytics Marketing Framework 
1.3.4. Marketing and Communication Plan 
1.3.5. Strategies, Forecasting and Campaign Management 

1.4. Commerce and Sales 

1.4.1. Contributions of Data Analytics in the Commercial Area 
1.4.2. Sales Department Needs 
1.4.3. Market Research 

1.5. Customer Service 

1.5.1. Loyalty 
1.5.2. Personal Coaching and Emotional Intelligence 
1.5.3. Customer Satisfaction 

1.6. Purchasing 

1.6.1. Data Analytics for Market Research 
1.6.2. Data Analytics for Competitive Studies 
1.6.3. Other Applications 

1.7. Administration 

1.7.1. Needs in the Administration Department 
1.7.2. Data Warehouse and Financial Risk Analysis 
1.7.3. Data Warehouse and President of Credit Risk Analysis 

1.8. Human Resources 

1.8.1. Human Resources and Benefits of Data Analytics
1.8.2. Data Analytics Tools in the PR Department
1.8.3. Data Analytics Application in the PR Department

1.9. Production 

1.9.1. Data Analysis in a Production Department 
1.9.2. Applications 
1.9.3. Benefits 

1.10. IT 

1.10.1. IT Department 
1.10.2. Data Analytics and Digital Transformation 
1.10.3. Innovation and Productivity 

Module 2. Data Management, Data Manipulation and Data Science Reporting

2.1. Statistics: Variables, Indexes and Ratios 

2.1.1. Statistics 
2.1.2. Statistical Dimensions 
2.1.3. Variables, Indexes and Ratios 

2.2. Data Typology 

2.2.1. Qualitative 
2.2.2. Quantitative 
2.2.3. Characterization and Categories 

2.3. Knowledge of Data Places from Measurement 

2.3.1. Centralization Measures 
2.3.2. Measures of Dispersion 
2.3.3. Correlation 

2.4. Knowledge of Data Places from Graph 

2.4.1. Display According to Data Type 
2.4.2. Interpretation of Graphic Information 
2.4.3. Customization of Graphics with R 

2.5. Probability 

2.5.1. Probability 
2.5.2. Probability Function 
2.5.3. Distribution 

2.6. Data Collection 

2.6.1. Methodology of Data Collection 
2.6.2. Data Collection Tools 
2.6.3. Data Collection Channels 

2.7. Data Cleaning 

2.7.1. Phases of Data Cleansing 
2.7.2. Data Quality 
2.7.3. Data Manipulation (with R) 

2.8. Data Analysis, Interpretation and Evaluation of Results 

2.8.1. Statistical Measures 
2.8.2. Relationship Indices 
2.8.3. Data Mining 

2.9. Data Warehouse 

2.9.1. Components 
2.9.2. Design 

2.10. Data Availability 

2.10.1. Access 
2.10.2. Uses 
2.10.3. Security/Safety 

Module 3. IoT Devices and Platforms as a Foundation for Data Science

3.1. Internet of Things 

3.1.1. Internet of the Future, Internet of Things 
3.1.2. The Industrial Internet Consortium 

3.2. Architecture of Reference 

3.2.1. The Architecture of Reference 
3.2.2. Layers 
3.2.3. Components 

3.3. Sensors and IoT Devices

3.3.1. Main Components 
3.3.2. Sensors and Actuators 

3.4. Communications and Protocols 

3.4.1. Protocols. OSI Model 
3.4.2. Communication Technologies 

3.5. Cloud Platforms for IoT and IIoT 

3.5.1. General Purpose Platforms 
3.5.2. Industrial Platforms 
3.5.3. Open Code Platforms 

3.6. Data Management on IoT Platforms 

3.6.1. Data Management Mechanisms. Open Data 
3.6.2. Data Exchange and Visualization 

3.7. IoT Security 

3.7.1. Requirements and Safety Areas 
3.7.2. IIoT Security Strategies 

3.8. IoT Applications 

3.8.1. Intelligent Cities 
3.8.2. Health and Fitness 
3.8.3. Smart Home 
3.8.4. Other Applications 

3.9. IIoT Applications 

3.9.1. Fabrication 
3.9.2. Transport 
3.9.3. Energy 
3.9.4. Agriculture and Livestock 
3.9.5. Other Sectors 

3.10. Industry 4.0 

3.10.1. IoRT (Internet of Robotics Things) 
3.10.2. 3D Additive Manufacturing 
3.10.3. Big Data Analytics

Module 4. Graphical Representation for Data Analysis 

4.1. Exploratory Analysis 

4.1.1. Representation for Information Analysis
4.1.2. The Value of Graphical Representation 
4.1.3. New Paradigms of Graphical Representation 

4.2. Optimization for Data Science 

4.2.1. Color Range and Design 
4.2.2. Gestalt in Graphic Representation 
4.2.3. Mistakes to Avoid and Tips 

4.3. Sources of Basic Data 

4.3.1. For Quality Representation 
4.3.2. For Amount Representation 
4.3.3. For Time Representation 

4.4. Sources of Complexity Data 

4.4.1. Files, Listings and Data Bases 
4.4.2. Open Data 
4.4.3. Continuous Generation Data 

4.5. Types of Graphs 

4.5.1. Basic Representations 
4.5.2. Block Representation 
4.5.3. Presentation for Dispersion Analysis 
4.5.4. Circular Representations 
4.5.5. Bubble Representations 
4.5.6. Geographical Representations 

4.6. Types of Display 

4.6.1. Comparative and Relational 
4.6.2. Distribution 
4.6.3. Hierarchical 

4.7. Report Design with Graphical Representation 

4.7.1. Application of Graphs in Marketing Reports 
4.7.2. Application of Graphs in Scorecards and KPIs 
4.7.3. Application of Graphics in Strategic Plans 
4.7.4. Other Uses: Science, Health, Business 

4.8. Graphic Narration 

4.8.1. Graphic Narration 
4.8.2. Evolution 
4.8.3. Uses 

4.9. Visualization-Oriented Tools 

4.9.1. Advanced Tools 
4.9.2. Online Software 
4.9.3. Open Source 

4.10. New Technologies in Data Visualization 

4.10.1. Systems for Virtualization of Reality 
4.10.2. Reality Enhancement and Augmentation Systems 
4.10.3. Intelligent Systems 

Module 5. Data Science Tools 

5.1. Data Science 

5.1.1. Data Vizualization 
5.1.2. Advanced Tools for the Data Scientist 

5.2. Data, Information and Knowledge 

5.2.1. Data, Information and Knowledge 
5.2.2. Types of Data 
5.2.3. Sources of Data 

5.3. From Data to Information 

5.3.1. Data Analysis 
5.3.2. Types of Analysis 
5.3.3. Extraction of Information from a Dataset 

5.4. Extraction of Information by Visualization 

5.4.1. Visualization as an Analysis Tool 
5.4.2. Visualization Methods 
5.4.3. Visualization of a Data Set 

5.5. Quality of Data 

5.5.1. Quality Data 
5.5.2. Data Cleansing 
5.5.3. Data Pre-Basic Processing 

5.6. Dataset 

5.6.1. Dataset Enrichment 
5.6.2. The Curse of Dimensionality 
5.6.3. Modification of a Data Set 

5.7. Imbalance 

5.7.1. Class Imbalance
5.7.2. Imbalance Mitigation Techniques 
5.7.3. Balancing a Dataset 

5.8. Unsupervised Models 

5.8.1. Unsupervised Models 
5.8.2. Methods 
5.8.3. Classification with Unsupervised Models 

5.9. Supervised Models 

5.9.1. Supervised Models 
5.9.2. Methods 
5.9.3. Classification with Unsupervised Models 

5.10. Tools and Best Practices 

5.10.1. Good Practices for the Data Scientist 
5.10.2. The Best Model 
5.10.3. Useful Tools 

Module 6. Data Mining Selection, Pre-Processing and Transformation

6.1. Statistical Inference 

6.1.1. Descriptive Statistics vs. Statistical Inference 
6.1.2. Parametric Procedures 
6.1.3. Non-Parametric Procedures 

6.2. Exploratory Analysis 

6.2.1. Descriptive Analysis 
6.2.2. Visualization 
6.2.3. Data Preparation 

6.3. Data Preparation 

6.3.1. Data Integration and Data Cleansing 
6.3.2. Data Normalization 
6.3.3. Transforming Attributes 

6.4. Lost Values 

6.4.1. Treatment of Missing Values 
6.4.2. Maximum Likelihood Imputation Methods 
6.4.3. Missing Value Imputation Using Machine Learning 

6.5. Noise in the Data 

6.5.1. Noise Classes and Attributes 
6.5.2. Noise Filtering 
6.5.3. The Effect of Noise 

6.6. The Curse of Dimensionality 

6.6.1. Oversampling 
6.6.2. Undersampling 
6.6.3. Multidimensional Data Reduction 

6.7. From Continuous to Discrete Attributes 

6.7.1. Continuous Data vs. Discreet Data 
6.7.2. Discretization Process 

6.8. The Data 

6.8.1. Data Selection 
6.8.2. Prospects and Selection Criteria 
6.8.3. Selection Methods 

6.9. Instance Selection 

6.9.1. Methods for Instance Selection 
6.9.2. Prototype Selection 
6.9.3. Advanced Methods for Instance Selection 

6.10. Data Pre-Processing in Big Data Environments 

6.10.1. Big Data 
6.10.2. Classical vs. Massive Pre-Processing 
6.10.3. Smart Data 

Module 7. Predictability and Analysis of Stochastic Phenomena 

7.1. Time Series 

7.1.1. Time Series 
7.1.2. Utility and Applicability 
7.1.3. Related Case Studies 

7.2. The Time Series 

7.2.1. ST Seasonality Trend 
7.2.2. Typical Variations 
7.2.3. Residue Analysis 

7.3. Typology 

7.3.1. Stationary 
7.3.2. Non-Stationary 
7.3.3. Transformations and Adjustments 

7.4. Schemes for Time Series 

7.4.1. Additive Scheme (Model) 
7.4.2. Multiplied Scheme (Model) 
7.4.3. Procedures for Determining the Type of Model 

7.5. Basic Forecasting Methods 

7.5.1. Stockings 
7.5.2. Naïve 
7.5.3. Seasonal Naivety 
7.5.4. Comparison of Methods 

7.6. Residue Analysis 

7.6.1. Autocorrelation 
7.6.2. Waste ACF 
7.6.3. Correlation Test 

7.7. Regression in the Context of Time Series 

7.7.1. ANOVA 
7.7.2. Fundamentals 
7.7.3. Practical Applications 

7.8. Predictive Time Series Models 

7.8.1. ARIMA 
7.8.2. Exponential Smoothing 

7.9. Manipulation and Analysis of Time Series with R 

7.9.1. Data Preparation 
7.9.2. Pattern Identification 
7.9.3. Model Analysis 
7.9.4. Prediction 

7.10. Combined Graphical Analysis with R 

7.10.1. Typical Situations 
7.10.2. Practical Application for Simple Problem Solving 
7.10.3. Practical Application for Advanced Problem Solving 

Module 8. Design and Development of Intelligent Systems 

8.1. Data Pre-Processing 

8.1.1. Data Pre-Processing 
8.1.2. Data Transformation 
8.1.3. Data Mining 

8.2. Automatic Learning 

8.2.1. Supervised and Unsupervised Learning 
8.2.2. Reinforcement Learning 
8.2.3. Other Learning paradigms 

8.3. Classification Algorithms 

8.3.1. A Utomatic Learning 
8.3.2. SVM & KNN 
8.3.3. Metrics and Scores for Ranking 

8.4. Regression Algorithms 

8.4.1. Linear Regression, Logistic Regression and Nonlinear Models 
8.4.2. The Time Series 
8.4.3. Metrics and Scores for Regression 

8.5. Clustering Algorithms 

8.5.1. Hierarchical Grouping Techniques 
8.5.2. Hierarchical Grouping Techniques 
8.5.3. Metrics and Scores for Clustering 

8.6. Association Rules Techniques 

8.6.1. Methods for Rules Extraction 
8.6.2. Metrics and Scores for Association Rule Algorithms 

8.7. Advanced Classification Techniques. Multiclassifiers 

8.7.1. Bagging Algorithms 
8.7.2. Random Forests Sorter 
8.7.3. Boosting for Decision Trees 

8.8. Probabilistic Graphical Models 

8.8.1. Probability Models 
8.8.2. Bayesian Networks Properties, Representation and Parameterization 
8.8.3. Other Probabilistic Graphical Models 

8.9. Neural Networks 

8.9.1. Machine Learning with Neural Networks Artificial 
8.9.2. Feedforward Networks 

8.10. Deep Learning 

8.10.1. Deep Feedforward Networks 
8.10.2. Convolutional Neural Networks and Sequence Models 
8.10.3. Tools for Implementing Deep Neural Networks 

Module 9. Tools for Implementing Deep Neural Networks 

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

9.1.1. Reliability 
9.1.2. Adaptability 
9.1.3. Maintainability 

9.2. Data Models 

9.2.1. Relational Model 
9.2.2. Documentary Model 
9.2.3. Network Data Model 

9.3. Databases Data Storage and Retrieval Engines 

9.3.1. Hash Indexes 
9.3.2. Structured Log Storage 
9.3.3. Trees B 

9.4. Data Encoding Formats 

9.4.1. Language-Specific Formats 
9.4.2. Standardized Formats 
9.4.3. Binary Encoding Formats 
9.4.4. Data Flow between Processes 

9.5. Replication 

9.5.1. Replication Objectives 
9.5.2. Replication Models 
9.5.3. Problems with Replication 

9.6. Distributed Transactions 

9.6.1. Transaction 
9.6.2. Protocols for Distributed Transactions 
9.6.3. Serializable Transactions 

9.7. Partitions 

9.7.1. Forms of Partitioning 
9.7.2. Secondary Index Interaction and Partitioning 
9.7.3. Partition Rebalancing 

9.8. Offline Data Processing 

9.8.1. Batch Processing 
9.8.2. Distributed File Systems 
9.8.3. MapReduce 

9.9. Real-Time Data Processing 

9.9.1. Types of Message Brokers 
9.9.2. Representation of Databases as Data Flows 
9.9.3. Data Stream Processing 

9.10. Practical Applications in the Company 

9.10.1. Consistency in Readings 
9.10.2. Holistic Approach to Data 
9.10.3. Scaling of a Distributed Service 

Module 10. Practical Application of Data Science in Sectors of Business Activity 

10.1. Health Sector 

10.1.1. Implications of AI and Data Analytics in the Healthcare Sector 
10.1.2. Opportunities and Challenges 

10.2. Risks and Trends in the Healthcare Sector 

10.2.1. Use in the Healthcare Sector 
10.2.2. Potential Risks Related to the Use of AI 

10.3. Financial Services 

10.3.1. Implications of AI and Data Analytics in the Financial Services Industry
10.3.2. Use in Financial Services 
10.3.3. Potential Risks Related to the Use of AI 

10.4. Retail 

10.4.1. Implications of AI and Data Analytics in the Retail Sector 
10.4.2. Use in Retail 
10.4.3. Potential Risks Related to the Use of AI 

10.5. Industry 4.0 

10.5.1. Implications of AI and Data Analytics in 4.0 Industry 
10.5.2. Use in 4.0 Industry 

10.6. Risks and Trends in 4.0 Industry 

10.6.1. Potential Risks Related to the Use of AI 

10.7. Public Administration 

10.7.1. Implications of AI and Data Analytics in Public Administration 
10.7.2. Use in Public Administration
10.7.3. Potential Risks Related to the Use of AI 

10.8. Educational 

10.8.1. Implications of AI and Data Analytics in Educational 
10.8.2. Potential Risks Related to the Use of AI 

10.9. Forestry and Agriculture 

10.9.1. Implications of AI and Data Analytics in Forestry and Agriculture 
10.9.2. Use in Forestry and Agriculture 
10.9.3. Potential Risks Related to the Use of AI 

10.10. Human Resources 

10.10.1. Implications of AI and Data Analytics in Human Resource Management
10.10.2. Practical Applications in the Business World 
10.10.3. Potential Risks Related to the Use of AI 

Module 11. Main Information Management Systems 

11.1. ERP and CRM 

11.1.1. ERP 
11.1.2. CRM 
11.1.3. Differences between ERP and CRM Selling Point 
11.1.4. Business Success 

11.2. ERP 

11.2.1. ERP 
11.2.2. Types of ERP 
11.2.3. Development of an ERP Implementation Project 
11.2.4. ERP Resource Optimizer 
11.2.5. Architecture of an ERP System 

11.3. Information Provided by the ERP 

11.3.1. Information Provided by the ERP 
11.3.2. Advantages and Disadvantages 
11.3.3. The Information 

11.4. ERP Systems 

11.4.1. Current ERP Systems and Tools 
11.4.2. Decision-Making 
11.4.3. Day-to-Day with ERP 

11.5. CRM: The Implementation Project 

11.5.1. The CRM The Implementation Project 
11.5.2. The CRM as a Commercial Tool 
11.5.3. Strategies for the Information System 

11.6. CRM: Customer Loyalty 

11.6.1. Starting Point 
11.6.2. Sell or Loyalty 
11.6.3. Factors for Success in our Loyalty System 
11.6.4. Multi-Channel Strategies 
11.6.5. Design of Loyalty Actions 
11.6.6. E-Loyalty 

11.7. CRM: Communication Campaigns 

11.7.1. Communication Actions and Plans 
11.7.2. Importance of the Informed Customer 
11.7.3. Listening to the Client 

11.8. CRM: Preventing Dissatisfaction 

11.8.1. Customer Cancellations 
11.8.2. Detecting Errors in Time 
11.8.3. Improvement Processes 
11.8.4. Recovery of the Dissatisfied Customer 

11.9. CRM: Special Communication Actions 

11.9.1. Objectives and Planning of a Company Event 
11.9.2. Design and Realization of the Event 
11.9.3. Actions from the Department 
11.9.4. Analysis of Results 

11.10. Relational Marketing 

11.10.1. Implantation. Errors 
11.10.2. Methodology, Segmentation and Processes 
11.10.3. Performance, According to the Department 
11.10.4. CRM Tools 

Module 12. Data Types and Data Life Cycle 

12.1. Statistics 

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

12.2. Types of Data Statistics 

12.2.1. According to Type 

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

12.2.2. According to their Shape 

12.2.2.1. Numeric 
12.2.2.2. Text 
12.2.2.3. Logical 

12.2.3. According to its Source 

12.2.3.1. Primary 
12.2.3.2. Secondary 

12.3. Life Cycle of Data 

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

12.4. Initial Stages of the Cycle 

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

12.5. Data Collection 

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

12.6. Data Cleaning 

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

12.7. Data Analysis, Interpretation and Evaluation of Results 

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

12.8. Data Warehouse 

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

12.9. Data Availability 

12.9.1. Access 
12.9.2. Uses 
12.9.3. Security/Safety 

12.10. Regulatory Aspects 

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

Module 13. Number - Machine Learning 

13.1. Knowledge in Databases 

13.1.1. Data Pre-Processing 
13.1.2. Analysis 
13.1.3. Interpretation and Evaluation of the Results 

13.2. Machine Learning 

13.2.1. Supervised and Unsupervised Learning 
13.2.2. Reinforcement Learning 
13.2.3. Semi-Supervised Learning. Other Learning Models 

13.3. Classification 

13.3.1. Decision Trees and Rule-Based Learning
13.3.2. Support Vector Machines (SVM) and K-Nearest Neighbour (KNN) Algorithms 
13.3.3. Metrics for Sorting Algorithms 

13.4. Regression 

13.4.1. Linear and Logistic Regression 
13.4.2. Non-Linear Regression Models
13.4.3. Time Series Analysis 
13.4.4. Metrics for Regression Algorithms 

13.5. Clustering 

13.5.1. Hierarchical Grouping 
13.5.2. Partitional Grouping
13.5.3. Metrics for Clustering Algorithms 

13.6. Association Rules 

13.6.1. Measures of Interest 
13.6.2. Rule Extraction Methods 
13.6.3. Metrics for Association Rule Algorithms 

13.7. Multiclassifiers 

13.7.1. Bootstrap Aggregation or Bagging 
13.7.2. "Random Forests" Algorithm 
13.7.3. “Boosting” Algorithm 

13.8. Probabilistic Reasoning Models 

13.8.1. Probabilistic Reasoning 
13.8.2. Bayesian Networks or Belief Networks 
13.8.3. Hidden Markov Models 

13.9. Multilayer Perceptron 

13.9.1. Neural Network
13.9.2. Machine Learning with Neural Networks 
13.9.3. Gradient Descent, Backpropagation and Activation Functions 
13.9.4. Implementation of an Artificial Neural Network 

13.10. Deep Learning 

13.10.1. Deep Neural Networks. Introduction 
13.10.2. Convolutional Networks 
13.10.3. Sequence Modelling 
13.10.4. TensorFlow and Pytorch 

Module 14. Web Analytics 

14.1. Web Analytics 

14.1.1. Introduction 
14.1.2. Evolution of Web Analytics 
14.1.3. Process of Analysis 

14.2. Google Analytics 

14.2.1. Google Analytics 
14.2.2. Use 
14.2.3. Objectives 

14.3. Hits. Interactions with the Website 

14.3.1. Basic Metrics 
14.3.2. KPI (Key Performance Indicators) 
14.3.3. Adequate Conversion Rates 

14.4. Frequent Dimensions 

14.4.1. Source 
14.4.2. Medium 
14.4.3. Keyword 
14.4.4. Campaign 
14.4.5. Personalized Labelling 

14.5. Setting up Google Analytics 

14.5.1. Installation. Creating the Account 
14.5.2. Versions of the Tool: UA/GA4 
14.5.3. Tracking Label 
14.5.4. Conversion Objectives 

14.6. Organization of Google Analytics 

14.6.1. Account 
14.6.2. Property 
14.6.3. View 

14.7. Google Analytics Reports 

14.7.1. In Real Time 
14.7.2. Audience 
14.7.3. Acquisition 
14.7.4. Behaviour 
14.7.5. Conversions 
14.7.6. e-Commerce 

14.8. Google Analytics Advanced Reports 

14.8.1. Personalized Reports 
14.8.2. Panels 
14.8.3. APIs 

14.9. Filters and Segments 

14.9.1. Filter 
14.9.2. Segment 
14.9.3. Types of Segments: Predefined / Customized 
14.9.4. Remarketing Lists 

14.10. Digital Analytics Plan 

14.10.1. Measurement 
14.10.2. Implementation in the Technological Environment 
14.10.3. Conclusions 

Module 15. Data Management Regulations 

15.1. Regulatory Framework

15.1.1. Normative Framework and Definitions 
15.1.2. Controllers, Joint Controllers and Processors 
15.1.3. Forthcoming Regulatory Framework for Artificial Intelligence 

15.2. Principles Relating to the Processing of Personal Data 

15.2.1. Lawfulness, Fairness and Transparency and Purpose Limitation 
15.2.2. Data Minimization, Accuracy and Limitation of Retention Period 
15.2.3. Integrity and Confidentiality 
15.2.4. Proactive Responsibility 

15.3. Legitimation and Authorization for Processing 

15.3.1. Basis of Legitimacy 
15.3.2. Authorizations for the Processing of Special Categories of Data 
15.3.3. Data Communications 

15.4. Individuals Rights 

15.4.1. Transparency and Information 
15.4.2. Access 
15.4.3. Rectification and Deletion (Right to be Forgotten), Limitation and Portability 
15.4.4. Opposition and Automated Individual Decisions 
15.4.5. Limits to Rights 

15.5. Risk Analysis and Management 

15.5.1. Identification of Risks and Threats to the Rights and Freedoms of Individuals 
15.5.2. Risk Assessment 
15.5.3. Risk Management Plan 

15.6. Proactive Accountability Measures 

15.6.1. Identifying Techniques to Ensure and Accredit Compliance 
15.6.2. Organizational Measures 
15.6.3. Technical Measures 
15.6.4. Management of Personal Data Security Breaches 
15.6.5. The Register of Processing Activities 

15.7. The Data Protection Impact Assessment (DPA or DPIA) 

15.7.1. Activities Requiring PCIA 
15.7.2. Evaluation Methodology 
15.7.3. Identification of Risks, Threats and Consultation with the Control Authority 

15.8. Contractual Regulation: Persons Responsible, Persons in Charge and Other Subjects 

15.8.1. Data Protection Contracts 
15.8.2. Attribution of Responsibilities 
15.8.3. Contracts between Both Responsible Parties 

15.9. International Data Transfers 

15.9.1. Definition and Safeguards to Be Adopted 
15.9.2. Standard Contractual Clauses 
15.9.3. Other Instruments to Regulate Transfers 

15.10. Violations and Penalties 

15.10.1. Violations and Penalties 
15.10.2. Graduation Criteria for Penalties 
15.10.3. The Data Protection Officer 
15.10.4. Functions of the Supervisory Authorities 

Module 16. Scalable and Reliable Mass Data Usage Systems 

16.1. Scalability, Reliability and Maintainability 

16.1.1. Scales 
16.1.2. Reliability 
16.1.3. Maintainability 

16.2. Data Models 

16.2.1. Evolution of Data Models 
16.2.2. Comparison of Relational Model with Document-Based NoSQL Model 
16.2.3. Network Model 

16.3. Data Storage and Retrieval Engines 

16.3.1. Structured Log Storage 
16.3.2. Storage in Segment Tables 
16.3.3. Trees B 

16.4. Services, Message Passing and Data Encoding Formats 

16.4.1. Data Flow in REST Services 
16.4.2. Data Flow in Message Passing 
16.4.3. Message Sending Formats 

16.5. Replication 

16.5.1. CAP Theorem 
16.5.2. Consistency Models 
16.5.3. Models of Replication Based on Leader and Follower Concepts 

16.6. Distributed Transactions 

16.6.1. Atomic Operations 
16.6.2. Distributed Transactions from Different Approaches Calvin, Spanner 
16.6.3. Serializability 

16.7. Partitions 

16.7.1. Types of Partitions 
16.7.2. Indexes in Partitions
16.7.3. Partition Rebalancing 

16.8. Batch Processing 

16.8.1. Batch Processing 
16.8.2. MapReduce 
16.8.3. Post-MapReduce Approaches 

16.9. Data Stream Processing 

16.9.1. Messaging Systems 
16.9.2. Persistence of Data Flows 
16.9.3. Uses and Operations with Data Flows 

16.10. Case Uses. Twitter, Facebook, Uber 

16.10.1. Twitter: The Use of Caches 
16.10.2. Facebook: Non-Relational Models 
16.10.3. Uber: Different Models for Different Purposes 

Module 17. System Administration for Distributed Deployments 

17.1. Classic Administration. The Monolithic Model 

17.1.1. Classical Applications. The Monolithic Model 
17.1.2. System Requirements for Monolithic Applications 
17.1.3. The Administration of Monolithic Systems 
17.1.4. Automization 

17.2. Distributed Applications. The Microservice 

17.2.1. Distributed Computing Paradigm 
17.2.2. Microservices-Based Models 
17.2.3. System Requirements for Distributed Models 
17.2.4. Monolithic vs. Distributed Applications 

17.3. Tools for Resource Exploitation 

17.3.1. “Iron” Management 
17.3.2. Virtualization 
17.3.3. Emulation 
17.3.4. Paravirtualization 

17.4. IaaS, PaaS and SaaS Models 

17.4.1. LaaS Model 
17.4.2. PaaS Model 
17.4.3. SaaS Model 
17.4.4. Design Patterns 

17.5. Containerization 

17.5.1. Virtualization with Cogroups 
17.5.2. Containers 
17.5.3. From Application to Container 
17.5.4. Container Orchestration 

17.6. Clustering 

17.6.1. High Performance and High Availability 
17.6.2. High Availability Models 
17.6.3. Cluster as SaaS Platform 
17.6.4. Cluster Securitization 

17.7. Cloud Computing 

17.7.1. Clusters vs Clouds 
17.7.2. Types of Clouds 
17.7.3. Clouds Service Models 
17.7.4. Oversubscription 

17.8. Monitoring and Testing 

17.8.1. Types of Monitoring 
17.8.2. Visualization 
17.8.3. Infrastructure Tests 
17.8.4. Chaos Engineering 

17.9. Study Case: Kubernetes 

17.9.1. Structure 
17.9.2. Administration 
17.9.3. Deployment of Services 
17.9.4. Development of Services for K8S 

17.10. Study Case: OpenStack 

17.10.1. Structure 
17.10.2. Administration 
17.10.3. Deployment 
17.10.4. Development of Services for OpenStack 

Module 18. Project Management and Agile Methodologies 

18.1. Project Management 

18.1.1. The Project 
18.1.2. Phases of a Project 
18.1.3. Project Management 

18.2. PMI Methodology for Project Management 

18.2.1. PMI (Project Management Institute) 
18.2.2. PMBOK 
18.2.3. Difference between Project, Program and Project Portfolio 
18.2.4. Evolution of Organizations Working with Projects 
18.2.5. Process Assets in Organizations 

18.3. PMI Methodology for Project Management: Processes 

18.3.1. Groups of Processes 
18.3.2. Knowledge Areas 
18.3.3. Process Matrix 

18.4. Agile Methodologies for Project Management 

18.4.1. VUCA context (Volatility, Uncertainty, Complexity and Ambiguity) 
18.4.2. Agile Values 
18.4.3. Principles of the Agile Manifesto 

18.5. Agile Scrum Framework for Project Management 

18.5.1. Scrum 
18.5.2. The Pillars of the Scrum Methodology 
18.5.3. The Values in Scrum 

18.6. Agile Scrum Framework for Project Management. Process 

18.6.1. The Scrum Process 
18.6.2. Typified Roles in a Scrum Process 
18.6.3. The Ceremonies of Scrum 

18.7. Agile Scrum Framework for Project Management. Artefacts 

18.7.1. Artefacts in the Scrum Process 
18.7.2. The Scrum Team 
18.7.3. Metrics for Evaluating the Performance of a Scrum Team 

18.8. Agile Scrum Framework for Project Management. Kanban Method 

18.8.1. Kanban 
18.8.2. Benefits of Kanban 
18.8.3. Kanban Method Components 

18.9. Agile Scrum Framework for Project Management. Kanban Method Practices 

18.9.1. The Values of Kanban 
18.9.2. Principles of the Kanban Method 
18.9.3. General Practices of the Kanban Method 
18.9.4. Metrics for Kanban Performance Evaluation 

18.10. Comparison: PMI, Scrum and Kanban 

18.10.1. PMI- SCRUM 
18.10.2. PMI- KANBAN 
18.10.3. SCRUM - KANBAN 

Module 19. Communication, Leadership and Team Management 

19.1. Organizational Development in Business 

19.1.1. Climate, Culture and Organizational Development in the Company 
19.1.2. Human Capital Management 

19.2. Direction Models Decision Making 

19.2.1. Paradigm Shift in Management Models 
19.2.2. Management Process of the Technology Company 
19.2.3. Decision Making Planning Instruments 

19.3. Leadership. Delegation and Empowerment 

19.3.1. Leadership 
19.3.2. Delegation and Empowerment 
19.3.3. Performance Evaluation 

19.4. Leadership. Knowledge and Talent Management 

19.4.1. Talent Management in the Company 
19.4.2. Engagement Management in the Company 
19.4.3. Improving Communication in the Company 

19.5. Coaching Applied to Business 

19.5.1. Executive Coaching 
19.5.2. Team Coaching 

19.6. Mentoring Applied to Business 

19.6.1. Mentor Profile 
19.6.2. The 4 Processes of a Mentoring Program 
19.6.3. Tools and Techniques in a Mentoring Process 
19.6.4. Benefits of Mentoring in the Business Environment 

19.7. Team Management I. Interpersonal Relations 

19.7.1. Interpersonal Relationships 
19.7.2. Relational Styles: Approach 
19.7.3. Effective Meetings and Agreements in Difficult Situations 

19.8. Team Management II. The Conflicts 

19.8.1. The Conflicts 
19.8.2. Preventing, Addressing and Resolving Conflict 

19.8.2.1. Strategies to Prevent Conflict 
19.8.2.2. Conflict Management. Basic Principles      

19.8.3. Conflict Resolution Strategies 
19.8.4. Stress and Work Motivation 

19.9. Team Management III. Negotiation 

19.9.1. Negotiation at the Managerial Level in Technology Companies 
19.9.2. Styles of Negotiation 
19.9.3. Negotiation Phases 

19.9.3.1. Barriers to Overcome in Negotiations 

19.10. Team Management IV. Negotiation Techniques 

19.10.1. Negotiation Techniques and Strategies 

19.10.1.1. Strategies and Main Types of Negotiation 
19.10.1.2. Negotiation Tactics and Practical Issues 

19.10.2. The Figure of the Negotiating Subject

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