Analyze the most appropriate techniques for each data set and examine the results obtained” 

The Postgraduate diploma has been designed to provide computer engineers with all the knowledge they need to analyze company data. This is essential for the profile of any professional working in this field, since the volume of information increases every year, making analysis and interpretation more difficult.  

Therefore, it is necessary to be trained in specialized knowledge to adequately manage data, focusing at all times on typology and life cycle and practical approaches using the available resources. In data science, knowledge of statistics is indispensable, hence the importance placed on the module that covers it.

At the end of the program, computer engineers will develop a critical attitude toward the strategies applied, being able to discern in each case the most appropriate solution and explain in a reasoned way the results obtained in the different metrics.

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. 

Generate hypotheses to solve practical cases and validate them using metrics in a critical and reasoned manner” 

This Postgraduate diploma in Exploratory Data Analysis contains the most comprehensive and up-to-date academic program in the university landscape. 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
• Content that is accessible from any fixed or portable device with an Internet connection

Analyze the different software tools for graphic and exploratory data analysis with a 100% online program” 

The program’s teaching staff includes professionals from the 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. For this purpose, the student will be assisted by an innovative, interactive video system created by renowned and experienced experts.

Produce relevant and effective information to aid in decision-making while developing critical thinking skills"

Develop skills to solve practical cases using data science techniques"

Understanding the enormous mass of company information generated on a daily basis requires professionals trained in the different software tools for graphing and exploratory data analysis. Therefore, this Postgraduate diploma will guide student’s learning in this and other related points, which will allow them to awaken their critical thinking to make decisions according to the situations in their work environments.  

Transform data into information, adding value and enabling the generation of new knowledge” 

Module 1. Data and Information Management and Manipulation in 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. Graphical Representation of Data Analysis        

2.1. Exploratory Analysis 

2.1.1. Representation for Information Analysis
2.1.2. The Value of Graphical Representation
2.1.3. New Paradigms of Graphical Representation

2.2. Optimization for Data Science 

2.2.1. Color Range and Design
2.2.2. Gestalt in Graphic Representation
2.2.3. Errors to Avoid and Advice  

2.3. Basic Data Sources

2.3.1. For Quality Representation
2.3.2. For Quantity Representation
2.3.3. For Time Representation

2.4. Complex Data Sources

2.4.1. Files, Lists and Databases 
2.4.2. Open Data
2.4.3. Continuous Data Generation

2.5. Types of Graphs 

2.5.1. Basic Representations
2.5.2. Block Representation 
2.5.3. Representation for Dispersion Analysis
2.5.4. Circular Representations
2.5.5. Bubble Representations
2.5.6. Geographical Representations 

2.6. Types of Visualization

2.6.1. Comparative and Relational
2.6.2. Distribution
2.6.3. Hierarchical

2.7. Report Design with Graphic Representation 

2.7.1. Application of Graphs in Marketing Reports
2.7.2. Using Graphs in Scorecards and KPIs
2.7.3. Application of Graphs in Strategic Plans
2.7.4. Other Uses: Science, Health, Business 

2.8. Graphic Narration

2.8.1. Graphic Narration
2.8.2. Evolution 
2.8.3. Uses

2.9. Tools Oriented Towards Visualization 

2.9.1. Advanced Tools
2.9.2. Online Software
2.9.3. Open Source

2.10. New Technologies in Data Visualization 

2.10.1. Systems for Virtualization of Reality
2.10.2. Reality Enhancement and Improvement Systems
2.10.3. Intelligent Systems

Module 3. Data Science Tools

3.1. Data Science

3.1.1. Data Science
3.1.2. Advanced Tools for Data Scientists  

3.2. Data, Information and Knowledge

3.2.1. Data, Information and Knowledge 
3.2.2. Types of Data
3.2.3. Data Sources

3.3. From Data to Information 

3.3.1. Data Analysis
3.3.2. Types of Analysis
3.3.3. Extraction of Information from a Dataset 

3.4. Extraction of Information Through Visualization

3.4.1. Visualization as an Analysis Tool
3.4.2. Visualization Methods 
3.4.3. Visualization of a Data Set

3.5. Data Quality

3.5.1. Quality Data
3.5.2. Data Cleaning 
3.5.3. Basic Data Pre-Processing

3.6. Dataset

3.6.1. Dataset Enrichment
3.6.2. The Curse of Dimensionality
3.6.3. Modification of Our Data Set

3.7. Unbalance 

3.7.1. Classes of Unbalance
3.7.2. Unbalance Mitigation Techniques
3.7.3. Balancing a Dataset

3.8.     Unsupervised Models 

3.8.1. Unsupervised Model
3.8.2. Methods
3.8.3. Classification with Unsupervised Models

3.9. Supervised Models

3.9.1. Supervised Model
3.9.2. Methods
3.9.3. Classification with Supervised Models

3.10. Tools and Good Practices

3.10.1. Good Practices for Data Scientists
3.10.2. The Best Model 
3.10.3. Useful Tools

Generate hypotheses to solve practical cases and validate them using metrics in a critical and reasoned manner”