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"

Performing a preliminary analysis to implement value extracting techniques on the data requires professionals trained in all the key aspects of this field. Thus, this program has been designed to help examine the main selection, preprocessing and transformation techniques used prior to the data mining process. Likewise, it will allow future graduates to develop the skills necessary to solve practical cases using such techniques.

Improve your problem-solving skills by analytically evaluating situations”

General Objectives

• Analyze the benefits of applying data analytic techniques in every company department
• Develop the basis for understanding the needs and applications of each department
• Generate specialized knowledge to select the right tool
• Propose techniques and objectives in order to be as productive as possible according to the department

Specific Objectives

Module 1. Data Science Tools

• Develop the skills to convert data into information from which knowledge can be extracted
• Determine the main features of a dataset, its structure, components and the implications of its distribution in the modeling
• Support decision-making by performing comprehensive data analysis in advance
• Develop skills to solve practical cases using data science techniques
• Establish the most appropriate general tools and methods for modeling each Dataset based on the preprocessing performed
• Evaluate the results in an analytical way, understanding the impact of the chosen strategy on the various metrics
• Demonstrate critical analysis of the results obtained after applying preprocessing or modeling methods

Module 2. Data Mining: Selection, Pre-Processing and Transformation

• Generate specialized knowledge about the statistical prerequisites for any data analysis and evaluation
• Develop the necessary skills for data identification, preparation and transformation
• Evaluate the various methodologies presented and identify advantages and drawbacks
• Examine the problems in high dimensional data environments
• Implement algorithms used for data preprocessing
• Demonstrate the ability to interpret data visualization for descriptive analysis
• Develop advanced knowledge of the different existing data preparation techniques for data cleaning, normalization and transformation

Module 3. Design and Development of Intelligent Systems

• Analyze the step from information to knowledge
• Develop the different types of machine learning
• Examine metrics and scores to quantify model quality
• Implement the various machine learning algorithms
• Identify probabilistic reasoning models
• Lay the foundations for deep learning
• Demonstrate the skills acquired to understand the various machine learning algorithms 

Demonstrate your critical analysis of the results obtained after using preprocessing or modeling methods”