With this Postgraduate diploma you will get all the keys in the types of graphics and survival analysis to add value to your own and collaborative projects"

The strong demand that exists in the current research sector, directly confronts individuals who develop physiotherapeutic practice with lack of knowledge. This reality means that on many occasions patients reject non-pharmacological therapies and lean towards the most intrusive intervention, deteriorating their wellbeing. This is the reason why companies make a narrow selection to choose the professionals that make up their teams.

Taking this reality into account, TECH has designed a comprehensive program in Physiotherapeutic Research that aims to update the knowledge of specialists in this field. To this end, the Technological University has provided itself with a team of experts in the area of Health Sciences that it has developed and that will be in charge of teaching the subject. It is a study that deepens the management of clinical information, key to the management of the social and health field, as well as research and publication of articles, theses and applied reports. Thus, it is a unique opportunity for experts who are committed to new technologies applied to their professional performance.  

The present program has a 100% online modality, which allows for the monitoring of the subject without leaving aside the other activities of the daily life of the specialists, such as their employment or motherhood. In this sense, TECH has also incorporated innovative pedagogical tools that facilitate and speed up the assimilation of the contents. An example of this is the Relearning methodology, which exempts students from long hours of memorization common in other orthodox programs.  

Intervene in scientific studies by applying statistics and R which are decisive in the advancement of physiotherapeutic research with this Postgraduate diploma"

This Postgraduate diploma in Tools for Health Research contains the most complete and up-to-date scientific program on the market. The most important features include:

• Case studies presented by experts in Health Sciences Research
• The graphic, schematic, and practical contents with which they are created, provide medical 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 t 

Don't be left behind in the updating process, master the new statistical methods thanks to the theoretical and practical knowledge of TECH"

The program's teaching staff includes professionals from the sector who contribute their work experience to this program, in addition to renowned specialists from leading societies and prestigious universities.

Its 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 an immersive education programmed to learn in real situations.

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

Get all the knowledge about Poisson and Negative Binomial regression inflated by zeros and offer a more accurate service in your area of work"

Delve into the treatment of outlier data and use advanced technological tools to gain in-depth knowledge of R strategies"

The syllabus of this Postgraduate diploma has been prepared by a teaching team specialized in Health Sciences, whose main objective is to offer students the most comprehensive content on statistics in research and the generation of projects. The program has a theoretical-practical approach, so that students can delve into the particularities of observation with R and the graphical representations of the results, among other issues. In addition, the Relearning system, based on the reiteration of content, exempts specialists from long hours of memorization and allows progressive and easy study.   

Learn how to use innovative tools such as GLM and GAMM and work with samples with guaranteed success"

Module 1. Generation of Research Projects

1.1. General Structure of a Project 
1.2. Presentation of Background and Preliminary Data 
1.3. Definition of the Hypothesis 
1.4. Definition of General and Specific Objectives 
1.5. Definition of the Type of Sample, Number and Variables to be Measured 
1.6. Establishment of the Scientific Methodology 
1.7. Exclusion/Inclusion Criteria in Projects with Human Samples 
1.8. Establishment of the Specific Team: Balance and Expertise 
1.9. Ethical aspects and Expectations: an Important Element that we Forget 
1.10. Budget Generation: a fine Tuning between the Needs and the Reality of the Call

Module 2. Statistics and R in Health Research

2.1. Biostatistics 

2.1.1. Introduction to The Scientific Method 
2.1.2. Population and Sample. Sampling Measures of Centralization 
2.1.3. Discrete Distributions and Continuous Distributions 
2.1.4. General Outline of Statistical Inference. Inference about a Normal Population Mean. Inference about a General Population Mean 
2.1.5. Introduction to Nonparametric Inference 

2.2. Introduction to R 

2.2.1. Basic Features of the Program 
2.2.2. Main Object Types 
2.2.3. Simple Examples of Simulation and Statistical Inference 
2.2.4. Graphs 
2.2.5. Introduction to R Programming 

2.3. Regression Methods with R  

2.3.1. Regression Models 
2.3.2. Variable selection 
2.3.3. Model diagnosis 
2.3.4. Outlier treatment 
2.3.5. Regression analysis 

2.4. Multivariate Analysis with R 

2.4.1. Description of Multivariate Data 
2.4.2. Multivariate Distributions 
2.4.3. Dimension Reduction 
2.4.4. Unsupervised Classification: Cluster Analysis 
2.4.5. Supervised Classification: Discriminant Analysis 

2.5. Regression Methods for Research with R 

2.5.1. Generalized Linear Models (GLM): Poisson Regression and Negative Binomial Regression 
2.5.2. Generalized Linear Models (GLM): Logistic and Binomial Regressions 
2.5.3. Poisson and Negative Binomial Regression Inflated by Zeros 
2.5.4. Local Fits and Generalized Additive Models (GAMs) 
2.5.5. Generalized Mixed Models (GLMM) and Generalized Additive Mixed Models (GAMM) 

2.6. Statistics Applied to Biomedical Research with R I 

2.6.1. Basic Notions of R. Variables and Objects in R. Data handling Files Graphs 
2.6.2. Descriptive Statistics and Probability Functions 
2.6.3. Programming and Functions in R 
2.6.4. Contingency Table Analysis 
2.6.5. Basic Inference with Continuous Variables 

2.7. Statistics Applied to Biomedical Research with R II 

2.7.1. Analysis of Variance 
2.7.2. Correlation Analysis 
2.7.3. Simple Linear Regression 
2.7.4. Multiple Linear Regression 
2.7.5. Logistic Regression 

2.8. Statistics Applied to Biomedical Research with R III 

2.8.1. Confounding Variables and Interactions 
2.8.2. Construction of a Logistic Regression Model 
2.8.3. Survival Analysis 
2.8.4. Cox Regression 
2.8.5. Predictive Models. ROC Curve Analysis 

2.9. Statistical Data Mining Techniques with R I 

2.9.1. Introduction. Data Mining. Supervised and Unsupervised Learning Predictive Models Classification and Regression
2.9.2. Descriptive Analysis Data Pre-Processing
2.9.3. Principal Component Analysis (PCA) 
2.9.4. Cluster Analysis. Hierarchical Methods. K-Means 

2.10. Statistical Data Mining Techniques with R II 

2.10.1. Model Evaluation Measures. Predictive Ability Measures. ROC Curves 
2.10.2. Models Assessment Techniques. Cross-Validation. Bootstrap Samples 
2.10.3. Tree-Based Methods (CART) 
2.10.4. Support Vector Machines (SVM) 
2.10.5. Random Forest (RF) and Neural Networks (NN)

Module 3. Graphical Representations of Data in Health Research and Other Advanced Analysis

3.1. Types of Graphs 
3.2. Survival Analysis 
3.3. ROC Curves 
3.4.  Multivariate Analysis (Types of Multiple Regression) 
3.5. Binary Regression Models 
3.6. Massive Data Analysis  
3.7. Dimensionality Reduction Methods 
3.8. Comparison of Methods: PCA, PPCA and KPCA 
3.9. T-SNE (t-Distributed Stochastic Stochastic Neighbor Embedding) 
3.10. UMAP (Uniform Manifold Approximation and Projection) 

A designed program for you to perfect your skills as a researcher and obtain better results in your scientific trials"