Develop the skills to turn data into information and extract knowledge to then apply it critically in your department”

The objective of this Postgraduate diploma is to provide computer engineers with the necessary knowledge to learn the tools, fundamentals of data science and its uses in solving practical cases. They will be able to analyze data, visualize sets and draw conclusions about the processes required before modeling and how they influence the results.

As the program progresses, special emphasis will be placed on extracting the maximum value from the data to generate specialized knowledge about statistics and inference procedures. Future graduates will be able to understand and examine the most advanced data cleaning techniques, data transformation and dimensionality reduction, as well as feature and instance selection.

This will be complemented by a module devoted to promoting knowledge about the different machine learning techniques and algorithms used, depending on the type of mining to be implemented. The interesting thing about this program and syllabus is its ability to present the theory of neural networks and their evolution throughout history, in a didactic and practical way.

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. 

Specify effective and efficient procedures for data processing according to the type of problem presented” 

This Postgraduate diploma in Techniques, Algorithms and Tools 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

Determine the main features of a dataset, its structure, components and the implications of its distribution in the modeling process” 

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.

The design of this program focuses on Problem-Based Learning, which means the student must try to solve the different real-life situations of that arise throughout the academic program. For this purpose, the student will be assisted by an innovative interactive video system created by renowned and experienced experts. 

Demonstrate your ability to interpret data visualization for descriptive analysis with a program that presents case studies for didactic learning” 

Examine metrics and scores to quantify model quality and become a critical thinking computer engineer"

The syllabus has been designed to cover a series of indispensable knowledge for the professional development of computer engineers. Professionals will thus be able to develop critical thinking skills when applying strategies by weighing up the advantages and disadvantages. 

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

Module 1. Data Science Tools

1.1. Data Science

1.1.1. Data Science
1.1.2. Advanced Tools for Data Scientists  

1.2. Data, Information and Knowledge

1.2.1. Data, Information and Knowledge 
1.2.2. Types of Data
1.2.3. Data Sources

1.3. From Data to Information 

1.3.1. Data Analysis
1.3.2. Types of Analysis
1.3.3. Extraction of Information from a Dataset 

1.4. Extraction of Information Through Visualization

1.4.1. Visualization as an Analysis Tool
1.4.2. Visualization Methods 
1.4.3. Visualization of a Data Set

1.5. Data Quality

1.5.1. Quality Data
1.5.2. Data Cleaning 
1.5.3. Basic Data Pre-Processing

1.6. Dataset

1.6.1. Dataset Enrichment
1.6.2. The Curse of Dimensionality
1.6.3. Modification of Our Data Set

1.7. Unbalance 

1.7.1. Classes of Unbalance
1.7.2. Unbalance Mitigation Techniques
1.7.3. Balancing a Dataset

1.8. Unsupervised Models 

1.8.1. Unsupervised Model
1.8.2. Methods
1.8.3. Classification with Unsupervised Models

1.9. Supervised Models

1.9.1. Supervised Model
1.9.2. Methods
1.9.3. Classification with Supervised Models

1.10. Tools and Good Practices

1.10.1. Good Practices for Data Scientists
1.10.2. The Best Model 
1.10.3. Useful Tools

Module 2. Data Mining: Selection, Preprocessing and Transformation     

2.1. Statistical Inference

2.1.1. Descriptive Statistics vs. Statistical Inference
2.1.2. Parametric Procedures
2.1.3. Non-Parametric Procedures

2.2. Exploratory Analysis

2.2.1. Descriptive Analysis 
2.2.2. Visualization
2.2.3. Data Preparation

2.3. Data Preparation

2.3.1. Integration and Data Cleaning 
2.3.2. Normalization of Data
2.3.3. Transforming Attributes 

2.4. Missing Values

2.4.1. Treatment of Missing Values
2.4.2. Maximum Likelihood Imputation Methods
2.4.3. Missing Value Imputation Using Machine Learning

2.5. Noise in the Data 

2.5.1. Noise Classes and Attributes
2.5.2. Noise Filtering 
2.5.3. The Effect of Noise

2.6. The Curse of Dimensionality

2.6.1. Oversampling
2.6.2. Undersampling
2.6.3. Multidimensional Data Reduction

2.7. From Continuous to Discrete Attributes

2.7.1. Continuous vs. Discrete Data
2.7.2. Discretization Process

2.8. The Data 

2.8.1. Data Selection 
2.8.2. Prospects and Selection Criteria
2.8.3. Selection Methods 

2.9. Instance Selection

2.9.1. Methods for Instance Selection
2.9.2. Prototype Selection
2.9.3. Advanced Methods for Instance Selection

2.10. Data Pre-Processing in Big Data Environments

2.10.1. Big Data
2.10.2. “Conventional” Vs. Mass Pre-Processing
2.10.3. Smart Data  

Module 3. Design and Development of Intelligent Systems        

3.1. Data Pre-Processing  

3.1.1. Data Pre-Processing
3.1.2. Data Transformation 
3.1.3. Data Mining

3.2. Machine Learning

3.2.1. Supervised and Unsupervised Learning
3.2.2. Reinforcement Learning
3.2.3. Other Learning Paradigms

3.3. Classification Algorithms

3.3.1. Inductive Machine Learning
3.3.2. SVM and KNN
3.3.3. Metrics and Scores for Ranking

3.4. Regression Algorithms 

3.4.1. Lineal Regression, Logistical Regression and Non-Lineal Models 
3.4.2. Time Series 
3.4.3. Metrics and Scores for Regression 

3.5. Clustering Algorithms 

3.5.1. Hierarchical Clustering Techniques 
3.5.2. Partitional Clustering Techniques 
3.5.3. Metrics and Scores for Clustering 

3.6. Association Rules Techniques 

3.6.1. Methods for Rule Extraction 
3.6.2. Metrics and Scores for Association Rule Algorithms 

3.7. Advanced Classification Techniques. Multiclassifiers 

3.7.1. Bagging Algorithms 
3.7.2. Random “Forests Sorter” 
3.7.3. “Boosting” for Decision Trees 

3.8. Probabilistic Graphical Models 

3.8.1. Probabilistic Models 
3.8.2. Bayesian Networks. Properties, Representation and Parameterization  
3.8.3. Other Probabilistic Graphical Models 

3.9. Neural Networks 

3.9.1. Machine Learning with Artificial Neural Networks 
3.9.2.  Feed Forward Networks 

3.10. Deep Learning 

3.10.1. Deep Feed Forward Networks 
3.10.2. Convolutional Neural Networks and Sequence Models 
3.10.3. Tools for Implementing Deep Neural Networks

Evince the skills acquired in understanding the different machine learning algorithms with the most up-to-date software on the market”