A Postgraduate certificate of great value for you to validate your hypotheses and conclusions with greater strength through Biostatistics with R" 

Biostatistics has become essential in healthcare research to design experimental and observational studies, analyze the data collected and make decisions based on these results. In fact, it allows researchers to consider factors such as variability and uncertainty in the data, which is relevant to validate the conclusions obtained. Moreover, research results are often presented in the form of figures or statistics, and biostatistics helps researchers to interpret them correctly. Therefore, its contribution is undoubtedly essential to improve the understanding of diseases and medical treatments in fields such as dentistry.

That is why, if dental professionals do not master Biostatistics with R they would be at a clear disadvantage, and their research would be less solid than that of other colleagues because they do not apply advanced tools for the validity of the results. All the more reason why this Postgraduate certificate will be of great value to update your knowledge in this subject of growing interest. Therefore, you will learn about statistical concepts applied to research, the fundamentals of the R language and its methods of regression and multivariate analysis. In addition, you will delve into Supervised and Unsupervised Learning using Data Mining in its work methodology.

Undoubtedly, a high-level preparation that will take your research to the next level. For this, you will only need a device with Internet connection, which will open the doors to an extensive digital catalog of resources at your disposal in the Virtual Campus. Within this platform, students will be in charge of their own educational time, making use of advanced contents designed by a reference teaching team. 

This is the program you are looking for to analyze in detail the methods of Regression and multivariate analysis with R"

This Postgraduate certificate in Biostatistics with R contains the most complete and up-to-date scientific program on the market. Its most notable features are:

• The development of case studies presented by experts in Biostatistics. with R 
• The graphic, schematic, and 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

Through advanced educational materials, you will benefit from an educational cycle condensed into only 150 hours that you will manage at your convenience as you go through the fundamentals of the R language"

The program’s teaching staff includes professionals from the field 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 students will be assisted by an innovative interactive video system created by renowned and experienced experts.

You will master each and every one of the main concepts of Biostatistics with R to take your dental research to the next level"

Do you want to handle the most effective statistical techniques with Data Mining? You will do it in only 6 weeks! "
   

The syllabus has been designed following an online format that will allow dental professionals to combine their activity with this high level of training without any problem. In fact, it is not necessary to attend a single day at an on-site center and you will not even have to adapt to specific schedules. Instead, you will be able to manage your study time and even internalize the ideas of the syllabus more quickly thanks to the dynamism of the Virtual Campus resources. Multimedia formats as varied as interactive diagrams, videos, master classes and case studies are available. 

Interactive diagrams, videos, master classes or case analyses will boost your performance"

Module 1. Statistics and R in Health Research

1.1 Biostatistics

1.1.1. Introduction to The Scientific Method
1.1.2. Population and Sample. Sampling Measures of Centralization
1.1.3. Discrete Distributions and Continuous Distributions
1.1.4. General Outline of Statistical Inference. Inference about a Normal Population Mean. Inference about a General Population Mean
1.1.5. Introduction to Nonparametric Inference

1.2 Introduction to R

1.2.1. Basic Features of the Program
1.2.2. Main Object Types
1.2.3. Simple Examples of Simulation and Statistical Inference
1.2.4. Graphs
1.2.5. Introduction to R Programming

1.3 Regression Methods with R

1.3.1. Regression Models
1.3.2. Variable Selection
1.3.3. Model Diagnosis
1.3.4. Treatment of Outliers
1.3.5. Regression Analysis

1.4 Multivariate Analysis with R

1.4.1. Description of Multivariate Data
1.4.2. Multivariate Distributions
1.4.3. Dimension Reduction
1.4.4. Unsupervised Classification: Cluster Analysis
1.4.5. Supervised Classification: Discriminant Analysis

1.5 Regression Methods for Research with R

1.5.1. Generalized Linear Models (GLM): Poisson Regression and Negative Binomial Regression
1.5.2. Generalized Linear Models (GLM): Logistic and Binomial Regressions
1.5.3. Poisson and Negative Binomial Regression Inflated by Zeros
1.5.4 Local adjustments and generalized additive models (GAM)
1.5.5. Generalized Mixed Models (GLMM) and Generalized Additive Mixed Models (GAMM)

1.6 Statistics Applied to Biomedical Research with R I

1.6.1. Basic Notions of R. Variables and Objects in R. Data handling. Files Graphs
1.6.2. Descriptive Statistics and Probability Functions
1.6.3. Programming and Functions in R
1.6.4. Contingency Table Analysis
1.6.5. Basic Inference with Continuous Variables

1.7 Statistics Applied to Biomedical Research with R II

1.7.1. Analysis of Variance
1.7.2. Correlation Analysis
1.7.3. Simple Linear Regression
1.7.4. Multiple Linear Regression
1.7.5. Logistic Regression

1.8 Statistics Applied to Biomedical Research with R III

1.8.1. Confounding Variables and Interactions
1.8.2. Construction of a Logistic Regression Model
1.8.3. Survival Analysis
1.8.4. Cox Regression
1.8.5. Predictive Models. ROC Curve Analysis

1.9 Statistical Data Mining Techniques with R I

1.9.1. Introduction. Data Mining. Supervised and Unsupervised Learning. Predictive Models Classification and Regression
1.9.2. Descriptive Analysis Data Pre-Processing
1.9.3. Principal Component Analysis (PCA)
1.9.4. Cluster Analysis. Hierarchical Methods. K-Means

1.10 Statistical Data Mining Techniques with R II

1.10.1. Model Evaluation Measures. Predictive Ability Measures. ROC Curves
1.10.2. Models Assessment Techniques. Cross-Validation. Bootstrap Samples
1.10.3. Tree-Based Methods (CART)
1.10.4. Support Vector Machines (SVM)
1.10.5. Random Forest (RF) and Neural Networks (NN)}

Make the most of this opportunity to surround yourself with expert professionals and learn from their work methodology"