You will learn in a 100% online environment with unlimited access to the virtual campus and library. Enroll now and get ready to achieve professional success!” 

Computational fluid mechanics is a key discipline in engineering, since it allows the simulation and analysis of complex problems in different fields, such as aeronautics, automotive or energy industry. Nowadays, the demand for professionals highly qualified in CFD techniques for pre-design and analysis is increasing. Engineers must be constantly updating their knowledge and skills in this area in order to meet the challenges faced by today's industry.

The Postgraduate certificate in Advanced Multivariate is the answer to this growing need. The program offers specialized qualification in Advanced Multivariate techniques, both in their theoretical aspect and in their practical application in computational fluid mechanics. Therefore, students will be able to delve into the knowledge and mastery of techniques such as correspondence analysis, discriminant analysis and cluster analysis, among others, which will allow them to improve their ability to analyze and understand multivariate data and make more informed decisions.

The program is developed in a 100% online format, which allows for greater flexibility in learning and adaptability to the needs of the students. In addition, it uses the Relearning methodology, which optimizes the learning experience and ensures effectiveness in the acquisition of knowledge. For all these reasons, this academic program is presented as a unique opportunity to acquire highly valued skills in the industry and improve the ability to solve complex problems in computational fluid mechanics.

You will master, thanks to this qualification, techniques such as correspondence analysis, discriminant analysis and cluster analysis to apply them in different fields of engineering” 

This Postgraduate certificate in Advanced Multivariate contains the most complete and up-to-date program on the market. The most important features include:

• The development of case studies presented by experts in Applied Statistics
• The graphic, schematic and eminently practical contents with which it is conceived provide sporting and practical information on those 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

You will master techniques such as correspondence analysis, discriminant analysis and cluster analysis to make informed decisions in different fields of engineering” 

The program’s teaching staff includes professionals from sector who contribute their work experience to this educational 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 education programmed to learn 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.

You will be able to access the virtual campus 24 hours a day and enjoy a learning experience adapted to your schedule and needs"

You will acquire skills that are highly valued in the industry and improve your ability to solve complex problems in computational fluid mechanics"

The syllabus has been designed with the current needs of the engineer in mind and provides comprehensive and contemporary teaching to help students improve their ability to analyze and understand multivariate data, which will enable them to make better professional decisions. And to facilitate the integration of new knowledge, the program is developed in a 100% online format, allowing students to adapt their learning to their schedules and needs, and uses Relearning methodology to optimize the learning experience and ensure effective knowledge acquisition.

Optimize your learning experience with Relearning methodology and ensure effective knowledge acquisition”

Module 1. Multivariate Statistical Techniques I

1.1. Factor Analysis

1.1.1. Introduction
1.1.2. Fundamentals of Factor Analysis
1.1.3. Factor Analysis
1.1.4. Factor Rotation Methods and Factor Analysis Interpretation

1.2. Factor Analysis Modeling

1.2.1. Examples
1.2.2. Statistical Software Modeling

1.3. Main Component Analysis

1.3.1. Introduction
1.3.2. Main Component Analysis
1.3.3. Systematic Principal Component Analysis

1.4. Principal Component Analysis Modeling

1.4.1. Examples
1.4.2. Statistical Software Modeling

1.5. Correspondence Analysis

1.5.1. Introduction
1.5.2. Independence Test
1.5.3. Row and Column Profiles
1.5.4. Inertia Analysis of a Point Cloud
1.5.5. Multiple Correspondence Analysis

1.6. Correspondence Analysis Modeling

1.6.1. Examples
1.6.2. Statistical Software Modeling

1.7. Discriminant Analysis

1.7.1. Introduction
1.7.2. Decision Rules for Two Groups
1.7.3. Classification over Several Populations
1.7.4. Fisher's Canonical Discriminant Analysis
1.7.5. Selecting Variables: Forward and Backward Procedure
1.7.6. Systematic Discriminant Analysis

1.8. Discriminant Analysis Modeling

1.8.1. Examples
1.8.2. Statistical Software Modeling

1.9. Cluster Analysis

1.9.1. Introduction
1.9.2. Distance and Similarity Measures
1.9.3. Hierarchical Classification Algorithms
1.9.4. Non-Hierarchical Classification Algorithms
1.9.5. Procedures to Determine the Appropriate Number of Clusters
1.9.6. Characterization of Clusters
1.9.7. Systematic Cluster Analysis
1.9.8. Cluster Analysis Modeling

1.10. Examples

1.10.1. Statistical Software Modeling

Module 2. Multivariate Statistical Techniques II

2.1. Introduction
2.2. Nominal Scale

2.2.1. Measures of Association for 2x2 Tables

2.2.1.1. Phi Coefficient
2.2.1.2. Relative Risk
2.2.1.3. Cross-Product Ratio (Odds Ratio)

2.2.2. Measures of Association for IxJ Tables

2.2.2.1. Contingency Ratio
2.2.2.2. Cramer's V
2.2.2.3. Lambdas
2.2.2.4. Tau of Goodman and Kruskal
2.2.2.5. Uncertainty Coefficient

2.2.3. Kappa Coefficient

2.3. Ordinal Scale

2.3.1. Gamma Coefficients
2.3.2. Kendall's Tau-B and Tau-C
2.3.3. Sommers' D

2.4. Interval or Ratio Scale

2.4.1. Eta Coefficient
2.4.2. Pearson's and Spearman's Correlation Coefficients

2.5. Stratified Analysis in 2x2 Tables

2.5.1. Stratified Analysis
2.5.2. Stratified Analysis in 2x2 Tables

2.6. Problem Formulation in Log-linear Models

2.6.1. The Saturated Model for Two Variables
2.6.2. The General Saturated Model
2.6.3. Other Types of Models

2.7. The Saturated Model

2.7.1. Calculation of Effects
2.7.2. Goodness of Fit
2.7.3. Test of K effects
2.7.4. Partial Association Test

2.8. The Hierarchical Model

2.8.1. Backward Methods

2.9. Probit Response Models

2.9.1. Problem Formulation
2.9.2. Parameter Estimation
2.9.3. Chi-Square Goodness-of-Fit Test
2.9.4. Parallelism Test for Groups
2.9.5. Estimation of the Dose Required to Obtain a Given Response Ratio

2.10. Binary Logistic Regression

2.10.1. Problem Formulation
2.10.2. Qualitative Variables in Logistic Regression
2.10.3. Selection of Variables
2.10.4. Parameter Estimation
2.10.5. Goodness of Fit
2.10.6. Classification of Individuals
2.10.7. Prediction

Develop skills in data analysis and statistics that will allow you to excel in your professional career”