With this Postgraduate diploma, you will gain all the knowledge required about new research tools so you can keep up with the demands of the sector”

Incremental innovation justifies the scrutiny that some drugs undergo when they are marketed. In order to obtain the "flagship product", some societies have undervalued the pharmaceutical work in the market. On occasions, drugs that affected the health of individuals had to be removed from the market. An example of this is the temporary suspension of cerivastatin, at the request of the pharmaceutical laboratory Bayer, S.A., for causing kidney damage to those who consumed it.

In this sense, it is essential that pharmaceutical companies have personnel who have a thorough understanding of the action protocols, act under professional ethics and, above all, are constantly updated. To meet the qualification requirements in the labor market, TECH has developed a rigorous program that explores the management of clinical information, which is key to the management of the social and health field, as well as research and the publication of articles, theses and applied reports. In this way, specialists will increase their competitiveness in the labor market by gaining new knowledge about research.

It is a 100% online program that allows them to study the subject without neglecting other activities of the specialists' daily lives, such as their jobs. In addition, TECH applies the Relearning methodology to exempt students from long hours of memorization and allow them assimilating the contents in a gradual and constant manner. By taking this program, professionals will have the support of a teaching team of specialists who have been awarded several prizes in the health sector.

The overall structure of a project is key to its performance. Learn about the correct generation of scientific studies to understand the level of impact of their backgrounds" 

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:

• Development of practical case studies presented by experts in health sciences
• 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

Now you can be part of the professionals at the forefront of studies in pharmaceutics, thanks to the theoretical-practical knowledge you will obtain with TECH"  

The program’s teaching staff includes professionals from the sector who contribute their work experience to this educational program, as well as renowned specialists from leading societies and prestigious universities.  

Its multimedia content, developed with the latest educational technology, will allow professionals to learn in a contextual and situated learning environment, i.e., a simulated environment that will provide immersive education programmed to prepare in real situations.

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

Master the ROC Curves and the types of multiple regression analysis to apply to your studies and offer a more accurate service"

Statistics and R are key in research in health sciences. Delve into this field managing population data and working with samples with guaranteed success"

The syllabus of this Postgraduate diploma in Tools for Health Research has been developed by experts in health sciences in order to guarantee the correct instruction of the students. Thus, TECHoffers a program that explores the generation of research projects, statistics and R in health research and graphical representations of data, among many other issues. All this, in a simple way thanks to the innovative Relearning methodology, which will exempt students from long hours of study, turning it into a constant learning based on theoretical and practical exercises. 

Enroll now and stay in line with the statistical update. With TECHyou will use innovative tools such as GLM and GAMM” 

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. Expectations: An Important Element that we Forget
1.10. Budget Generation: A fine Tuning Between the Needs and the Reality of the Call
1.11. Ethical Aspects

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. Treatment of Outliers
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
2.5.1. Local Fits and Generalized Additive Models (GAMs)
2.5.1. 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 Assessment 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 Neighbor Embedding)
3.10. UMAP (Uniform Manifold Approximation and Projection)

A program designed to master your skills and obtain innovative techniques and strategies in the field of research"