Establish the conditions that must be met and replicated to optimize data utilization and quality” 

This Postgraduate diploma will lay the foundations of knowledge computer engineers must have when managing data structures, focusing on typology and life cycle. For this reason, valuable statistical information will be provided, fundamental to better understand the process of extraction, analysis and synthesis.
 
It is also necessary to establish the importance of new technologies such as IoT, Internet of Things.  Such technologies have been revolutionary in their ability to convert simple and inert objects into objects that interact and connect to the Internet. That is how they have become a technological solution for companies seeking to create an ecosystem that enables industrial solutions.  

Finally, the technologies and tools on the market will be addressed, examining the principles of the most important components in a system designed to meet the challenge of big data. At the end of the program, engineers will be equipped with specialized knowledge of the different possibilities when designing distributed systems, the advantages and penalties involved in choosing tools or technologies over others, which requires understanding their components. 

All of the above is complemented by a 100% online program, which can be studied at our students' convenience, wherever and whenever it suits them. All you need is a device with Internet access to take your career one step further. A modality in accord with the current times and all the guarantees to position engineers in a highly demanded field.

Establish the conditions that must be met and replicated to optimize data utilization and quality” 

This Postgraduate diploma in Information Use in Data Science contains the most complete and up-to-date academic program on the market. The most important features of the program include:   

• Practical cases studies are presented by experts in Engineering in data analysis 
• 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  
• Its special emphasis on innovative methodologies   
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments  
• Access to content from any fixed or portable device with an Internet connection 

Develop partitioning methods and distributed transactions with a program that will enhance your professional aptitude” 

The program’s teaching staff includes professionals from sector who contribute their work experience to this training program, as well as renowned 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 immersive training programmed to train 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 during the academic year. This will be done with the help of an innovative, interactive video system developed by renowned experts with extensive personal training experience.

Examine the different IoT Cloud platforms: General purpose, industrial, open source"

Analyze the key functionalities for distributed systems and their importance in different types of systems"

This Postgraduate diploma is designed to ensure computer engineers interested in this field of action reach excellence. Thus, at the end of each module, students will have optimally developed their skills in terms of data analysis and processing, as well as in identifying the protocols and communication technologies used in IoT. 

Evaluate which widely used applications implement distributed systems fundamentals to design their systems”

Module 1. Data Management, Data Manipulation and Information Management for Data Science 

1.1. Statistics: Variables, Indexes and Ratios  

1.1.1. Statistics  
1.1.2. Statistical Dimensions  
1.1.3. Variables, Indexes and Ratios  

1.2. Type of Data  

1.2.1. Qualitative  
1.2.2. Quantitative  
1.2.3. Characterization and Categories  

1.3. Data Knowledge from the Measurements  

1.3.1. Centralization Measurements  
1.3.2. Measures of Dispersion  
1.3.3. Correlation  

1.4. Data Knowledge from the Graphs  

1.4.1. Visualization According to Type of Data  
1.4.2. Interpretation of Graphic Information  
1.4.3. Customization of graphics with R  

1.5. Probability  

1.5.1. Probability  
1.5.2. Function of Probability  
1.5.3. Distributions  

1.6. Data Collection  

1.6.1. Methodology of Data Collection  
1.6.2. Data Collection Tools  
1.6.3. Data Collection Channels  

1.7. Data Cleaning  

1.7.1. Phases of Data Cleansing  
1.7.2. Data Quality  
1.7.3. Data Manipulation (with R)  

1.8. Data Analysis, Interpretation and Evaluation of Results  

1.8.1. Statistical Measures  
1.8.2. Relationship Indices  
1.8.3. Data Mining  

1.9. Data Warehouse  

1.9.1. Components   
1.9.2. Design  

1.10. Data Availability  

1.10.1. Access  
1.10.2. Uses  
1.10.3. Security 

Module 2. Devices and IoT Platforms as a Base for Data Science

2.1. The Internet of Things 

2.1.1. The Internet of the Future, Internet of Things 
2.1.2. The Industrial Internet Consortium 

2.2. Architecture of Reference  

2.2.1. The Architecture of Reference 
2.2.2. Layers 
2.2.3. Components 

2.3. Sensors and IoT Devices  

2.3.1. Principal Components 
2.3.2. Sensors and Actuators 

2.4. Communications and Protocols  

2.4.1. Protocols. OSI Model 
2.4.2. Communication Technologies 

2.5. Cloud Platforms for IoT and IioT  

2.5.1. General Purpose Platforms 
2.5.2. Industrial Platforms 
2.5.3. Open Code Platforms 

2.6. Data Management on IoT Platforms  

2.6.1. Data Management Mechanisms. Open Data 
2.6.2. Data Exchange and Visualization 

2.7. IoT Security  

2.7.1. Requirements and Security Areas 
2.7.2. Security Strategies in IioT 

2.8. Applications of IoT  

2.8.1. Intelligent Cities 
2.8.2. Health and Fitness 
2.8.3. Smart Home 
2.8.4. Other Applications 

2.9. Applications of IioT  

2.9.1. Fabrication 
2.9.2. Transport 
2.9.3. Energy 
2.9.4. Agriculture and Livestock 
2.9.5. Other Sectors 

2.10. Industry 4.0  

2.10.1. IoRT (Internet of Robotics Things) 
2.10.2. 3D Additive Manufacturing 
2.10.3. Big Data Analytics

Module 3. Architecture and Systems for Intensive Use of Data 

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

3.1.1. Reliability 
3.1.2. Adaptation 
3.1.3. Maintainability 

3.2. Data Models 

3.2.1. Relational Model 
3.2.2. Document Model 
3.2.3. Graph Type Data Model 

3.3. Databases. Storage Management and Data Recovery 

3.3.1. Hash Indexes     
3.3.2. Structured Log Storage 
3.3.3. Trees B 

3.4. Data Coding Formats 

3.4.1. Language-Specific Formats 
3.4.2. Standardized Formats 
3.4.3. Binary Coding Formats 
3.4.4. Data Stream Between Processes 

3.5. Replication 

3.5.1. Objectives of Replication 
3.5.2. Replication Models 
3.5.3. Problems with Replication 

3.6. Distributed Transactions 

3.6.1. Transaction  
3.6.2. Protocols for Distributed Transactions
3.6.3. Serializable Transactions 

3.7. Partitions 

3.7.1. Forms of Partitioning 
3.7.2. Secondary Index Interaction and Partitioning 
3.7.3. Partition Rebalancing 

3.8. Offline Data Processing 

3.8.1. Batch Processing 
3.8.2. Distributed File Systems 
3.8.3. MapReduce 

3.9. Data Processing in Real Time 

3.9.1. Types of Message Broker 
3.9.2. Representation of Databases as Data Streams 
3.9.3. Data Stream Processing 

3.10. Practical Applications in Business 

3.10.1. Consistency in Readings 
3.10.2. Holistic Focus of Data 
3.10.3. Scaling of a Distributed Service

Determine mass data usage system requirements with a program that will lead you to professional excellence”