Discover the latest trends in statistics in R such as Data Mining techniques or biomedical research with a nutritional perspective by enrolling in this Postgraduate diploma"

The topics covered by scientific research in the nutritional area are extensive. From the effect of certain diets on different people to the interaction between a group of foods and diseases such as cancer, the researcher must have an excellent knowledge not only in the scientific postulates and current material, but also in the essential statistical tools to carry out the project.

From its very genesis, the research team must define the objectives and scientific methodology to be used, as well as the ethics that the project must follow. This program, created by a team of expert researchers with years of experience, compiles both the necessary scientific basis and the most useful tools to undertake a research project in Nutrition with all the guarantees.

Therefore, it includes extensive topics on expectations, hypotheses, biostatistics, multivariate analysis, types of graphs and many other essential issues for researchers who wish to be at the forefront of their field. All the teaching material is reinforced by a large number of simulated and real cases, which help to adequately contextualize each topic covered. Thus, theory and practice are complemented with detailed videos, interactive summaries and more content created by the best professionals in the field.

The 100% online Postgraduate diploma makes it possible to combine it with all kinds of activities and responsibilities, both professional and personal. The student is completely free to distribute the teaching load according to their own interests, being able to download the entire syllabus from any electronic device with internet connection.

Position yourself at the forefront of dietary research with the best tools, precepts and practical guidelines that TECH puts at your disposal in this program"

This Postgraduate diploma in Health Research Tools contains the most complete and up-to-date scientific program on the market. Its most outstanding features are:

• 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

Delve into how Data Mining and massive data analysis can be a key point in Health and Nutrition Research"

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 provide the professionals with situated and contextual learning, i.e., a simulated environment that will provide an 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 throughout the program. This will be done with the help of an innovative system of interactive videos made by renowned experts.

Use a 100% online format without any kind of restriction for you, with the freedom you need to complete it in just 6 months"

You will have access to a complete reference guide that you can download to continue using even after you finish your program"

In order to facilitate the student's work, TECH has incorporated the Relearning methodology in all the contents of this program. This means that the key concepts in Health Research Tools are reiterated naturally and progressively throughout the entire Postgraduate Diplomat. This saves numerous hours of study time, which in turn can be invested in the large amount of supplementary material provided for each knowledge module.

You will have 24-hour access to a library of high-quality multimedia content, which you can download and play later on your favorite electronic device” 

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. 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
2.5.4. 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 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)

Self-awareness exercises, complementary readings, interactive summaries, detailed videos and more multimedia material await you when you enroll in this program”