Develop your skills as a computer engineer in Data Science and Data Mining” 

This Postgraduate certificate will analyze the theoretical bases that help computer engineers develop advanced knowledge about the different existing data preparation techniques for data cleaning, normalization and transformation. It will also present the necessary tools to evaluate different methodologies in search of errors that may cause problems in the work environment.

The entire program is composed of a series of case studies that will favor the learning of students who seek to further advance their professional careers and challenge themselves to achieve excellence.

All this will be feasible thanks to a 100% online program, which adapts to the daily needs of its students. You will only need a device with an Internet connection to start developing a complete professional profile with international projection.  

Evaluate the various methodologies presented to identify advantages and drawbacks”  

This Postgraduate certificate in Data Mining Processing and Transformation 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 self-assessment can be used 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  

Develop the necessary skills for data identification, preparation and transformation” 

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.

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. This will be done with the help of an innovative, interactive video system developed by renowned experts with extensive experience in Data Mining Processing and Transformation.

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

Turn your career around and start developing in-company improvement strategies"

The modules on this program provide a theoretical and practical perspective to examine state-of-the-art data cleaning techniques, transformation, dimensionality reduction, as well as feature and instance selection. Thus will the objectives of the program to train professional, integral and prestigious engineers be fulfilled. 

Develop advanced knowledge of the different existing data preparation techniques for data cleaning, normalization and transformation”  

Module 1. Data Mining: Selection, Pre-Processing and Transformation

1.1. Statistical Inference 

1.1.1. Descriptive Statistics vs. Statistical Inference 
1.1.2. Parametric Procedures 
1.1.3. Non-Parametric Procedures 

1.2. Exploratory Analysis 

1.2.1. Descriptive Analysis 
1.2.2. Visualization 
1.2.3. Data Preparation 

1.3. Data Preparation 

1.3.1. Integration and Data Cleaning 
1.3.2. Normalization of Data 
1.3.3. Transforming Attributes 

1.4. Missing Values 

1.4.1. Treatment of Missing Values 
1.4.2. Maximum Likelihood Imputation Methods 
1.4.3. Missing Value Imputation Using Machine Learning 

1.5. Noise in the Data 

1.5.1. Noise Classes and Attributes 
1.5.2. Noise Filtering 
1.5.3. The Effect of Noise 

1.6. The Curse of Dimensionality 

1.6.1. Oversampling 
1.6.2. Undersampling 
1.6.3. Multidimensional Data Reduction 

1.7. From Continuous to Discrete Attributes 

1.7.1. Continuous vs. Discrete Data 
1.7.2. Discretization Process 

1.8. The Data 

1.8.1. Data Selection
1.8.2. Prospects and Selection Criteria 
1.8.3. Selection Methods

1.9. Instance Selection 

1.9.1. Methods for Instance Selection 
1.9.2. Prototype Selection 
1.9.3. Advanced Methods for Instance Selection 

1.10. Data Preprocessing in Big Data Environments 

1.10.1. Big Data 
1.10.2. “Conventional” Vs. Mass Pre-Processing 
1.10.3. Smart Data 

Completing this program will allow students to better understand data selection methods”