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 Global 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 structure of the contents has been designed by a team of professionals from the best educational centers, universities and companies in the national territory, aware of the relevance of current specialization in order to intervene in the specialization and accompaniment of students, and committed to quality teaching through new educational technologies.

The Postgraduate diploma in the Use of Linux and R Language Programming for Oncology contains the most complete and up-to-date scientific program on the market"

Module 1. Use of Unix and Linux in Bioinformatics

1.1. Introduction to the Linux Operating System

1.1.1. What is an Operating System?
1.1.2. The Benefits of Using Linux

1.2. Linux Environment and Installation

1.2.1. Linux Distributions?
1.2.2. Linux Installation Using a USB Memory
1.2.3. Linux Installation Using a CD-ROM
1.2.4. Linux Installation Using an Virtual Machine

1.3. The Command Line

1.3.1. Introduction
1.3.2. What is a Command Line?
1.3.3. Working on the Terminal
1.3.4. Shell and Bash

1.4. Basic Browsing

1.4.1. Introduction
1.4.2. How to Learn the Current Location?
1.4.3. Absolute and Relative Routes
1.4.4. How to Navigate in the System?

1.5. File Manipulation

1.5.1. Introduction
1.5.2. How to Build a Directory?
1.5.3. How to Move to a Directory?
1.5.4. How to Create an Empty File?
1.5.5. Copying a File and Directory
1.5.6. Deleting a File and Directory

1.6. VI Text Editor

1.6.1. Introduction
1.6.2. How to Save and Exit?
1.6.3. How to Browse a File in the VI Text Editor?
1.6.4. Deleting Contents
1.6.5. The Undo Command

1.7. Wildcards

1.7.1. Introduction
1.7.2. What are Wildcards?
1.7.3. Examples of Wildcards

1.8. Licences

1.8.1. Introduction
1.8.2. How to See the Licences of a File?
1.8.3. How to Change the Licences?
1.8.4. Licence Configuration
1.8.5. Licences for Directories
1.8.6. The “Root” User

1.9. Filters

1.9.1. Introduction
1.9.2. Head
1.9.3. Tail
1.9.4. Sort
1.9.5. nl
1.9.6. wc
1.9.7. cut
1.9.8. sed
1.9.9. uniq
1.9.10. tac
1.9.11. Other Filters

1.10. Grep and Common Expressions

1.10.1. Introduction
1.10.2. eGrep
1.10.3. Common Expressions
1.10.4. Some Examples

1.11. Pipelines and Redirection

1.11.1. Introduction
1.11.2. Redirect to a File
1.11.3. Save to File
1.11.4. Redirect From a File
1.11.5. STDERR Redirection
1.11.6. Pipelines

1.12. Managing Processes

1.12.1. Introduction
1.12.2. Active Processes
1.12.3. Closing a Corrupt Program
1.12.4. Foreground and Background Work

1.13. Bash

1.13.1. Introduction
1.13.2. Important Points
1.13.3. Why the./ ?
1.13.4. Variables
1.13.5. The Declarations

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

2.1. Introduction to R. Language Programming 

2.1.1. What is R?
2.1.2. R Installation and the Graphic Interface of R
2.1.3. Packages

2.1.3.1. Standard Packages
2.1.3.2. Contributed Packages and CRAN

2.2. Basic Features of R

2.2.1. The Environment of R
2.2.2. Software and Related Documentation
2.2.3. R and Statistics
2.2.4. R and the Window System
2.2.5. Using R Interactively
2.2.6. An Introductory Session
2.2.7. Obtaining Help With Functions and Features
2.2.8. R Commands, Cap Sensitivity, etc.
2.2.9. Recovery and Correction of Previous Commands
2.2.10. Execute Commands or Diverting the Output to a File
2.2.11. Data Storage and Object Deletion

2.3. Types of Objects in R

2.3.1. Simple Manipulations; Numbers and Vectors

2.3.1.1. Vectors and Their Assignment
2.3.1.2. Vector Arithmeti
2.3.1.3. Generating Regular Sequences
2.3.1.4. Logical Vectors
2.3.1.5. Lost Values
2.3.1.6. Character Vectors
2.3.1.7. Index Vectors

2.3.1.7.1. Selecting and Modifying Subsets of a Dataset

2.3.1.8. Other Types of Objects

2.3.2. Objects, Their Modes and Attributes

2.3.2.1. Intrinsic Attributes: Mode and Length
2.3.2.2. Changing the Length of an Object
2.3.2.3. Obtaining and Configuring Attributes
2.3.2.4. The Class of an Object

2.3.3. Sorted and Unsorted Factors

2.3.3.1. A Specific Example
2.3.3.2. The Tapply () Function and Unequal Matrices
2.3.3.3. Sorted Factors

2.3.4. Matrices

2.3.4.1. Matrices
2.3.4.2. Matrix Indexation. The Subsections of a Matrix
2.3.4.3. Index Matrices
2.3.4.4. The Array () Function
2.3.4.5. Mixed Arithmetic of Vectors and Matrices. The Recycling Rule
2.3.4.6. The Outer Product of Two Matrices
2.3.4.7. The General Transposition of a Matrix
2.3.4.8. Matrix Multiplication
2.3.4.9. Eigenvalues and Eigenvectors
2.3.4.10. Decomposition of Singular Values and Determinants
2.3.4.11. Forming Partitioned Matrices, Cbind () and Rbind ()
2.3.4.12. The Concatenation Function, c (), With Matrices

2.3.5. Factor Frequency Tables
2.3.6. Lists.

2.3.6.1. Creating and Modifying Lists
2.3.6.2. Concatenation Lists

2.3.7. Dataframes

2.3.7.1. How to Create Dataframes?
2.3.7.2. Attach () and Separate ()
2.3.7.3. Working With Dataframes

2.4. Reading and Writing Data

2.4.1. The Read.Table () Function
2.4.2. The Scan () Function
2.4.3. Access to the Sets of Incorporated Data
2.4.4. Loading Data From Other R. Packages
2.4.5. Editing Data

2.5. Grouping, Loops and Conditional Execution

2.5.1. Grouped Expressions
2.5.2. Control Statements

2.5.2.1. Conditional Execution: If Sentences
2.5.2.2. Repetitive Execution: For Loops, Repetition and Time

2.6. Writing Your Own Functions

2.6.1. Simple Examples
2.6.2. Defining New Binary Operators
2.6.3. Arguments With Name and Default Value
2.6.4. Argument “...”
2.6.5. Assignments Within Functions

Module 3. Statistical Analysis in R

3.1. Discrete Probability Distributions
3.2. Continuous Probability Distributions
3.3. Introduction to Inference and Sampling (Point Estimate)
3.4. Confidence Intervals
3.5. Hypothesis Testing
3.6. ANOVA of a Factor
3.7. Adjustment Bondat (Chi-Square Test)
3.8. QFitdist Package
3.9. Introduction to Multivariant Statistics

Module 4. The Graphical Environment in R

4.1. Graphical Procedures

4.1.1. High-Level Plotting Commands

4.1.1.1. The Plot () Function
4.1.1.2. Multivariate Data Visualization
4.1.1.3. Screen Graphics
4.1.1.4. High-Level Plotting Arguments

4.1.2. Low-Level Plotting Commands

4.1.2.1. Mathematical Annotation
4.1.2.2. Hershey Vectorial Sources

4.1.3. Interacting With Graphics
4.1.4. The Use of Graphic Parameters

4.1.4.1. Permanent Changes: the Par () Function
4.1.4.2. Temporary Changes: Arguments to Graphic Functions

4.1.5. List of Graphic Parameters

4.1.5.1. Graphical Elements
4.1.5.2. Axles and Markings
4.1.5.3. Figure Margins
4.1.5.4. Multi-Figure Environment

4.1.6. Descriptive Statistics: Graphical Representations

A unique, key and decisive training experience to boost your professional development"