If what you are looking for is a 100% online academic experience that provides you with everything you need to master the programming of Statistical Software, as well as existing applications, this University Expert is perfect for you”

The contribution that Informatics has made to the statistical field is incalculable, based on the development of increasingly specialized software capable of carrying out complex processes related to research and the understanding of the data that supports the actions that a certain subject (whether individual, a company, for a study, etc.) wishes to undertake. In this way, it is possible to work with larger information flows, while considerably reducing the time spent on statistical analysis and improving results.

Based on this and considering the latest advances in programming and algorithms as a reference, TECH and its team of specialists in Computer Engineering and Finance have developed this Postgraduate diploma in Statistical Software, a 100% online academic experience that promises to become an exclusive and exhaustive guide to develop detailed knowledge of this field. It is a degree that, during 450 hours of diverse content, will enable graduates to delve into the elements of a program and its structuring, in the documentation and in the resourcefulness of financial applications.In addition, you will acquire a top-level mastery of the SPSS and R environment to perform effective and efficient object operations.

In this way, in just 6 months of multidisciplinary education, you will improve your professional skills through a program that includes the latest developments in the sector. You will also have access to high-quality use cases and additional material: in-depth videos, research articles, further reading, news, self-awareness exercises, and much more. Everything will be available on the Virtual Campus from the startof the academic experience, which the graduate can access from any device with an Internet connection. In this way, graduates will specialize independently and through a degree in which they will decide when and where to take it themselves.

A degree with which you will acquire an exhaustive mastery of the SPSS and R environments managed by the best experts”

This Postgraduate diploma in Statistical Software contains the most complete and up-to-date program on the market. The most important features include:

• The development of case studies presented by experts in Applied Statistics
• The graphic, schematic and practical contents of the book provide technical and practical information on those 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 for 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

You will have the opportunity to carry out program tests, as well as the black and white box, using the most sophisticated and modern tools for its documentation and conformation”

Includes a team of professionals from the field in its teaching staff who bring the experience of their work to this program, in addition to recognized specialists from prestigious reference societies and universities.

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

The design of this program focuses on Problem-Based Learning, by means of which the professional must try to solve different professional practice situations that are presented throughout the academic course. This will be done with the help of an innovative system of interactive videos made by renowned experts.

Without schedules or face-to-face classes, you will work exhaustively on perfecting your skills in a program that adapts to you and your availability"

You will be able to know in detail the characteristics of the static and dynamic data structures, delving into matrixes and looking for patterns"

The development of this Postgraduate diploma in Statistical Software has been created by following the guidelines of the prestigious and effective methodology Relearning, whose application TECH pioneers. This pedagogical technique consists of the reiteration of the most important concepts throughout the entire syllabus, in such a way that the graduate acquires the knowledge in a natural and progressive manner, without the need to invest extra hours in memorizing. In addition, it is supported by the inclusion of hours of high-quality material, with which the student will be able to delve into the different sections of the agenda according to their expectations and interests.

Thanks to the use of Relearning methodology in the development of these programs, students will not have to invest extra hours in memorizing, but rather, they will attend to a innovative, natural and progressive learning process"

Module 1. Programming

1.1. Introduction to Programming

1.1.1. Basic Structure of a Computer
1.1.2. Software
1.1.3. Programming Languages
1.1.4. Life Cycle of a Software Application

1.2. Algorithm Design

1.2.1. Problem Solving
1.2.2. Descriptive Techniques
1.2.3. Algorithm Elements and Structure

1.3. Elements of a Program

1.3.1. C++ Origin and Features
1.3.2. Development Environment
1.3.3. Concept of Program
1.3.4. Types of Fundamental Data
1.3.5. Operators
1.3.6. Expressions
1.3.7. Statements
1.3.8. Data Input and Output

1.4. Control Sentences

1.4.1. Statements
1.4.2. Branches
1.4.3. Loops

1.5. Abstraction and Modularity: Functions

1.5.1. Modular Design
1.5.2. Concept of Function and Utility
1.5.3. Definition of a Function
1.5.4. Execution Flow in a Function Call
1.5.5. Function Prototypes
1.5.6. Results Return
1.5.7. Calling a Function: Parameters
1.5.8. Passing Parameters by Reference and by Value
1.5.9. Scope Identifier

1.6. Static Data Structures

1.6.1. Matrices
1.6.2. Matrices. Polyhedra
1.6.3. Searching and Sorting
1.6.4. Chaining: I/O Functions for Chains
1.6.5. Structures. Unions
1.6.6. New Types of Data

1.7. Dynamic Data Structures: Pointers

1.7.1. Concept. Definition of Pointer
1.7.2. Pointer Operators and Operations
1.7.3. Arrays of Pointers
1.7.4. Pointers and Arrays
1.7.5. Chain Pointers
1.7.6. Structure Pointers
1.7.7. Multiple Indirection
1.7.8. Function Pointers
1.7.9. Passing Functions, Structures and Arrays as Function Parameters

1.8. Files

1.8.1. Basic Concepts
1.8.2. File Operations
1.8.3. Types of Files
1.8.4. File Organization
1.8.5. Introduction to C++ Files
1.8.6. Managing Files

1.9. Recursion

1.9.1. Definition of Recursion
1.9.2. Types of Recursion
1.9.3. Advantages and Disadvantages
1.9.4. Considerations
1.9.5. Recursive-Iterative Conversion
1.9.6. Recursion Stack

1.10. Testing and Documentation

1.10.1. Program Testing
1.10.2. White Box Testing
1.10.3. Black Box Testing
1.10.4. Testing Tools
1.10.5. Program Documentation

Module 2. Statistical Software I

2.1. Introduction to the SPSS Environment

2.1.1. How SPSS Works
2.1.2. Creating, Listing and Removing Objects in Memory

2.2. Consoles in SPSS

2.2.1. Console Environments in SPSS
2.2.2. Main Controls

2.3. Script Mode in SPSS

2.3.1. Script Environments in SPSS
2.3.2. Main Commands

2.4. Objects in SPSS

2.4.1. Objects
2.4.2. Reading Data From a File
2.4.3. Saving Data
2.4.4. Generating Data

2.5. Execution Flow Control Structures

2.5.1. Conditional Structures
2.5.2. Repetitive/Iterative Structures
2.5.3. Vectors and Arrays

2.6. Operations with Objects

2.6.1. Creation of Objects
2.6.2. Converting Objects
2.6.3. Operators
2.6.4. How to Access the Values of an Object: the Indexing System?
2.6.5. Accessing an Object's Values with Names
2.6.6. The Data Editor
2.6.7. Simple Arithmetic Functions
2.6.8. Calculations With Arrays

2.7. SPSS Functions

2.7.1. Loops and Vectorization
2.7.2. Creating Your Own Functions

2.8. Graphics in SPSS

2.8.1. Handling Graphics

2.8.1.1. Opening Multiple Graphics Devices
2.8.1.2. Graph Layouts

2.8.2. Graphical Functions
2.8.3. Graph Parameters

2.9. SPSS Packages

2.9.1. SPSS Libraries
2.9.2. SPSS Packages

2.10. SPSS Statistics

2.10.1. A Simple Example of Analysis of Variance
2.10.2. Formulas
2.10.3. Generic Functions

Module 3. Statistical Software II

3.1. Introduction to the R Environment

3.1.1. How R Works?
3.1.2. Creating, Listing and Removing Objects in Memory

3.2. Console in R

3.2.1. Console Environment in R
3.2.2. Main Controls

3.3. Script Mode in R

3.3.1. Console Environment in R
3.3.2. Main Commands

3.4. Objects in R

3.4.1. Objects
3.4.2. Reading Data From a File
3.4.3. Saving Data
3.4.4. Generating Data

3.5. Execution Flow Control Structures

3.5.1. Conditional Structures
3.5.2. Repetitive/Iterative Structures
3.5.3. Vectors and Arrays

3.6. Operations with Objects

3.6.1. Creation of Objects
3.6.2. Converting Objects
3.6.3. Operators
3.6.4. How to Access the Values of an Object: the Indexing System
3.6.5. Accessing an Object's Values with Names
3.6.6. The Data Editor
3.6.7. Simple Arithmetic Functions
3.6.8. Calculations With Arrays

3.7. Functions in R

3.7.1. Loops and Vectorization
3.7.2. Writing a Program in R
3.7.3. Creating Your Own Functions

3.8. Graphics in R

3.8.1. Handling Graphics

3.8.1.1. Opening Multiple Graphics Devices
3.8.1.2. Graph Layouts

3.8.2. Graphical Functions
3.8.3. Low-Level Graphing Commands
3.8.4. Graph Parameters
3.8.5.  Grid and Lattice Packages

3.9. R Packages

3.9.1. R Library
3.9.2. R Packages

3.10. Statistics in R

3.10.1. A Simple Example of Analysis of Variance
3.10.2. Formulas
3.10.3. Generic Functions

Get on the bandwagon of progress and implement the most innovative computer strategies and techniques for the development of cutting-edge statistical software of the highest quality to your professional practice”