Improve your knowledge in R Programming Language in Genomic Oncology through this program, where you will find the best teaching material with real clinical cases. Learn here about the latest advances in the specialty to be able to perform a quality medical practice"

A fundamental objective of the program is to bring students closer to and disseminate informatics knowledge is already applied in other areas of knowledge, but has a minimal implementation in the medical world, despite the fact that for genomic medicine to become a reality, it is necessary to accurately interpret the huge volume of clinical information currently available and associate it with the biological data generated after a bioinformatics analysis. While this is a difficult challenge, it will allow the effects of genetic variation and potential therapies to be explored quickly, inexpensively and with greater precision than is currently possible.

Humans are not naturally equipped to perceive and interpret genomic sequences, to understand all the mechanisms, pathways and interactions that take place within a living cell, nor to make medical decisions with tens or hundreds of variables. To move forward, a system with superhuman analytical capabilities is required to simplify the work environment and show the relationships and proximities between variables. In genomics and biology, it is now recognized that it is better to spend resources on new computational techniques than on pure data collection, something that is possibly the same in medicine and, of course, oncology. 

We have millions of data or publications, but when they are analyzed by physicians or biologists, the conclusions are totally subjective and relative to the available publications or data, which are prioritized arbitrarily. This generates partial knowledge, which is increasingly distanced from the genetic and biological knowledge available and supported by computation, so a giant step in the implementation of precision medicine is to reduce this distance through the massive analysis of available medical and pharmacological information.

Update your knowledge through the program in R Programming Language in Genomic Oncology”

This postgraduate certificate in R Programming Language in Genomic Oncology contains the most complete and up-to-date scientific program on the market. The most important features include: 

• Development of case studies presented by experts in R Programming Language in Genomic Oncology
• Its graphic, schematic and eminently practical contents provide scientific and practical information on those disciplines that are essential for professional practice
• Developments on R Programming Language in Genomic Oncology
• It contains practical exercises where the self-assessment process can be carried out to improve learning
• Special emphasis on innovative methodologies in R Programming Language of Bioinformatics in Genomic Oncology
• All of this will be complemented by 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

This may be the best investment you can make in the selection of a refresher program for two reasons: in addition to updating your knowledge in R Programming Language in Genomic Oncology, you will obtain a qualification from TECH Technological University"

It includes in its teaching staff professionals belonging to the field of R Programming Language in Genomic Oncology, who pour into this program the experience of their work, as well as recognized specialists belonging to reference societies and prestigious universities.

Thanks to its multimedia content elaborated with the latest educational technology, this program will allow the professional a situated and contextual learning, that is to say, a simulated environment that will provide an immersive learning programmed to work in real situations.

This program is designed around Problem-Based Learning, whereby the students must try to solve the different professional practice situations that arise throughout the program. For this purpose, the student will be assisted by an innovative interactive video system developed by recognized experts in the field of R Programming Language in Genomic Oncology with extensive teaching experience.

Increase your decision-making confidence by updating your knowledge through this program"

Take the opportunity to learn about the latest advances in the R Programming Language in Genomic Oncology and improve the care of your patients"

The program in R Programming Language in Genomic Oncology is aimed at facilitating the performance of the physician dedicated to the treatment of oncological pathology, in which it is necessary to accurately interpret the huge volume of clinical information currently available and associate it with the biological data generated after a bioinformatics analysis.

This program will provide you with the skills to carry out your medical praxis with confidence and will help you to grow both personally and professionally”

General Objective

• Be able to accurately interpret the volume of clinical information currently available and associated with the biological data generated after a bioinformatic analysis

Specific learning objectives of each module

• Discuss how the adoption of next-generation sequencing (NGS) in a diagnostic context raises numerous questions regarding the identification and reporting of variants in secondary genes for patient pathology 
• Get started in the R programming language, which has the advantages of being an open-source programming language and has multiple statistical analysis packages available 
• Learn basic R programming concepts such as data types, vector arithmetic and indexing  
• Performing operations in R, including sorting, creating or importing data  
• Learn how problem solving begins with a modular decomposition and then further decompositions of each module in a process called successive refinement  
• Learn the basics of statistical inference to understand and calculate p-values and confidence intervals while analyzing data with R  
• Provide examples of R programming in a way that will help make the connection between concepts and their implementation  
• Describe the most appropriate statistical techniques as an alternative when data do not conform to the assumptions required by the standard approach 
• Learn the basics of conducting reproducible research by using R scripts to analyze data
• Using visualization techniques to explore new datasets and determine the most appropriate approach  
• Learn how to visualize data to extract information, better understand data and make more effective decisions  
• Teach how to take data that at first glance has little meaning and visually present that data in a form that makes sense for analysis  
• Learn how to use the three main graph sources in R: base, lattice and ggplot2  
• Know what each graphics package is based on in order to define which one to use and the advantages offered by one or the other 

Make the most of this opportunity to learn about the latest advances in this subject to apply it to your daily practice"