This specialization will provide you with a sense of security in medical practice, which will help you grow personally and professionally”

The scale and complexity of genomic data dwarf the measurements traditionally used in laboratory testing. In recent years there has been an enormous development of informatics to analyze and interpret DNA sequencing, and it has created a gap between biological knowledge and its application to routine clinical practice. It is therefore necessary to educate, disseminate and incorporate these informatics techniques among the medical community in order to be able to interpret the massive analysis of data from publications, biological or medical databases and medical records, among others, and thus enrich the biological information available at the clinical level.

This machine learning will enable the development of precision oncology, in order to interpret genomic characteristics and find targeted therapies, or to identify risks to certain diseases and establish more individualized preventive measures. A fundamental objective of the program is to bring students closer to and disseminate computer knowledge, which is already applied in other fields of knowledge but has 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 bioinformatic 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.

Update your knowledge with the postgraduate diploma in the Use of Linux and R Language Programming for Oncology"

This postgraduate diploma in Use of Linux and R Language Programming for Oncology is the most complete and up-to-date scientific program on the market. Its most notable features are:

• Development of practical cases presented by experts in the use of Linux and programming with R language for oncology. Its graphic, schematic and eminently practical contents provide scientific and practical information on those disciplines that are essential for professional practice
• News developments in the use of linux and R language programming for oncology
• It contains practical exercises where the self-assessment process can be carried out to improve learning
• With special emphasis on innovative methodologies in the use of linux and R  language programming for 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 diploma may be the best investment you can make in the selection of an updated program for two reasons: besides updating your knowledge in the use of Linux and programming with R language for oncology, you will obtain a postgraduate diploma from TECH Technological University"

It includes in its teaching staff professionals belonging to the field of the use of linux and R language programming for oncology, who pour into this specialization 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 postgraduate diploma 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 student must try to solve the different professional practice situations that arise during the course. For this, the student will have the help of an innovative interactive video system made by recognized experts in the field of the use of linux and R language programming for oncology and with great teaching experience.

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

Take the opportunity to learn about the latest advances in the Use of Linux and R Language Programming for Oncology and improve patient care"

The program in Use of Linux and R Language Programming for Oncology is oriented to facilitate 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 postgraduate diploma is designed for you to update your knowledge in the Use of Linux and R Language Programming for Oncology, with the use of the latest educational technology, to contribute with quality and safety to decision making"

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 Objectives

Module 1. Use of Unix and Linux in Bioinformatics

• Learn about the Linux operating system, which is currently essential in the scientific world both for the interpretation of biological data from sequencing and it also should be for medical text mining when handling large-scale data
• Provide the basics of accessing a Linux server and how to find and install packages to install software locally
• Describe basic Linux commands for: creating, renaming, moving, and deleting directories; listing, reading, creating, editing, copying, and deleting files
• Understand how permissions work and how to decrypt the most cryptic Linux permissions easily

Module 2. Data Analysis Processing in Big Data Projects: R Language Programming

• 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

Module 3. Statistical Analysis in R

• 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

Module 4. Graphical Environment in R

• 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

Take the opportunity and take the step to get up to date on the latest developments in the Use of Linux and R Language Programming for Oncology"