With this Professional master’s degree, you will contribute to the advancement of Computational Statistics through the most exhaustive knowledge based on the best computing and programming techniques”

The advances made in the field of Statistics have contributed to accurate and effective decisions based on massive data collection, analysis and conclusions drawn. However, if there is one element that has considerably promoted the evolution of this science, it is its coordinated action with Computing, thanks to which it has been possible to automate tasks, optimize actions and handle excessive amounts of information in a few seconds. Programming complex algorithms and designing static and dynamic data structures has allowed professionals in this field to work more safely and with more guarantees in estimating trends and making different social, economic and political predictions in the current environment.

Based on this and the high level of knowledge required in the field, TECH and its team of experts have decided to launch a program that will initiate graduates in Computational Statistics through a comprehensive tour of its main areas. As a result, this Professional Master's Degree is an educational experience of 1,500 hours that covers the latest developments related to the description and exploration of data, programming and the use of the main statistical software (SPSS and R). It also focuses on using statistics in current industrial settings and the sampling of designs for different fields. Finally, it highlights the main multivariate techniques for improving the quality of the results and, therefore, of the prediction.

All this, 100% online and through a program designed by real experts in the field, who have not only actively participated in shaping the syllabus, but have also selected hundreds of hours of varied additional material: use cases, detailed videos, research articles, additional readings, and much more! Everything will be available on the Online Campus from the very beginning and can be downloaded to any device with an Internet connection. In this way, TECH offers a comprehensive and flexible program that adapts to the needs of its students and to the most demanding requirements of the current labor market in Computational Statistics.

Achieving excellence and the highest professional level will not be complicated thanks to this program and the high degree of specialization that you will acquire by completing it”

This Professional master’s degree in Computational Statistics contains the most complete and up-to-date program on the market. The most important features include:

• The development of practical cases presented by experts in Computational 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

A program that approaches Computational Statistics from its foundation up to comprehensive management, through the acquisition of the key concepts and a mastery of the main computer software”

It includes in its teaching staff a team of professionals from the field who bring to this program the experience of their work, 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 the 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.

You will work on the design of complex algorithms through the most innovative and efficient descriptive techniques in current computational environments"

In the Online Campus you will find 1,500 hours of diverse content, which you can access from wherever and whenever you want, through any device with an Internet connection"

The syllabus of this program has been developed by a team of experts in the area of Computer Science and Statistics, who, following TECH’s strict quality criteria, have selected the most cutting-edge and comprehensive information in the sector. In addition, this has been adapted to the Relearning methodology, which consists of reiterating the most important concepts throughout the syllabus, favoring a gradual and progressive learning without the need to invest extra hours in memorization. In this way, graduates will receive high-level academic specialization that will enable them to acquire professional skills in the use of tools and techniques in Computational Statistics.

You will have a specific module dedicated to the Six Sigma methodology, with which you will be able to reduce defects or failures in the delivery of a product or service to the customer/user”

Module 1. Data Description and Exploration

1.1. Introduction to Statistics

1.1.1. Conceptos básicos Estadística
1.1.2. Objetivo del análisis exploratorio de datos o Estadística descriptiva
1.1.3. Types of Variables and Measurement Scales
1.1.4. Rounding and Scientific Notation

1.2. Summary of Statistical Data

1.2.1. Frequency Distributions: Tables
1.2.2. Grouping in Intervals
1.2.3. Graphical Representations
1.2.4. Differential Diagram
1.2.5. Integral Diagram

1.3. One-Dimensional Descriptive Statistics

1.3.1. Central Position Characteristics: Mean, Median, Mode
1.3.2. Other Position Characteristics: Quartiles, Deciles and Percentiles
1.3.3. Características de dispersión: varianza y desviación típica (muestrales y poblacionales), rango, rango intercuartil
1.3.4. Relative Dispersion Characteristics
1.3.5. Typical Scores
1.3.6. Shape Characteristics: Symmetry and Kurtosis

1.4. Complements in the Study of a Variable

1.4.1. Exploratory Analysis: Box Plots and Other Graphs
1.4.2. Transforming Variables
1.4.3. Other Averages: Geometric, Harmonic, Quadratic
1.4.4. Chebyshev's Inequality

1.5. Two-Dimensional Descriptive Statistics

1.5.1. Two-Dimensional Frequency Distributions
1.5.2. Double-Entry Statistical Tables. Marginal and Conditional Distributions
1.5.3. Concepts of Independence and Functional Dependence
1.5.4. Graphical Representations

1.6. Complements in the Study of Two Variables

1.6.1. Numerical Characteristics of a Two-Dimensional Distribution
1.6.2. Joint, Marginal and Conditional Moments
1.6.3. Relationship between Marginal and Conditional Measures

1.7. Regression

1.7.1. General Regression Line
1.7.2. Regression Curves
1.7.3. Linear Adjustment
1.7.4. Prediction and Error

1.8. Correlation

1.8.1. Concept of Correlation
1.8.2. Correlation Ratios
1.8.3. Pearson's Correlation Coefficient
1.8.4. Correlation Analysis

1.9. Correlation between Attributes

1.9.1. Coeficiente de Spearman
1.9.2. Kendall Coefficient
1.9.3. Chi-Squared Coefficient

1.10. Introduction to Time Series

1.10.1. Time Series
1.10.2. Stochastic Processes

1.10.2.1. Stationary Processes
1.10.2.2. Non-Stationary Processes

1.10.3. Models
1.10.4. Applications

Module 2. Programming

2.1. Introduction to Programming

2.1.1. Basic Structure of a Computer
2.1.2. Software
2.1.3. Programming Languages
2.1.4. Life Cycle of a Software Application

2.2. Algorithm Design

2.2.1. Problem Solving
2.2.2. Descriptive Techniques
2.2.3. Algorithm Elements and Structure

2.3. Elements of a Program

2.3.1. C++ Origin and Features
2.3.2. Development Environment
2.3.3. Concept of Program
2.3.4. Types of Fundamental Data
2.3.5. Operators
2.3.6. Expressions
2.3.7. Statements
2.3.8. Data Input and Output

2.4. Control Sentences

2.4.1. Statements
2.4.2. Branches
2.4.3. Loops

2.5. Abstraction and Modularity: Functions

2.5.1. Modular Design
2.5.2. Concept of Function and Utility
2.5.3. Definition of a Function
2.5.4. Execution Flow in a Function Call
2.5.5. Function Prototypes
2.5.6. Results Return
2.5.7. Calling a Function: Parameters
2.5.8. Passing Parameters by Reference and by Value
2.5.9. Scope Identifier

2.6. Static Data Structures

2.6.1. Matrices
2.6.2. Matrices. Polyhedra
2.6.3. Searching and Sorting
2.6.4. Chaining: I/O Functions for Chains
2.6.5. Structures. Unions
2.6.6. New Types of Data

2.7. Dynamic Data Structures: Pointers

2.7.1. Concept. Definition of Pointer
2.7.2. Pointer Operators and Operations
2.7.3. Arrays of Pointers
2.7.4. Punteros y matrices
2.7.5. Chain Pointers
2.7.6. Structure Pointers
2.7.7. Multiple Indirection
2.7.8. Function Pointers
2.7.9. Passing Functions, Structures and Arrays as Function Parameters

2.8. Files

2.8.1. Basic Concepts
2.8.2. File Operations
2.8.3. Types of Files
2.8.4. File Organization
2.8.5. Introduction to C++ Files
2.8.6. Managing Files

2.9. Recursion

2.9.1. Definition of Recursion
2.9.2. Types of Recursion
2.9.3. Advantages and Disadvantages
2.9.4. Considerations
2.9.5. Recursive-Iterative Conversion
2.9.6. Recursion Stack

2.10. Testing and Documentation

2.10.1. Program Testing
2.10.2. White Box Testing
2.10.3. Black Box Testing
2.10.4. Testing Tools
2.10.5. Program Documentation

Module 3. Statistical Software I

3.1. Introduction to the SPSS Environment

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

3.2. Consoles in SPSS

3.2.1. Console Environments in SPSS
3.2.2. Main Controls

3.3. Modo Script en SPSS

3.3.1. Entorno Script en SPSS
3.3.2. Main Commands

3.4. Objects in SPSS

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. SPSS Functions

3.7.1. Loops and Vectorization
3.7.2. Creating Your Own Functions

3.8. Graphics in SPSS

3.8.1. Handling Graphics

3.8.1.1. Opening Multiple Graphics Devices
3.8.1.2. Graph Layouts

3.8.2. Graph Functions
3.8.3. Graph Parameters

3.9. SPSS Packages

3.9.1. SPSS Libraries
3.9.2. SPSS Packages

3.10. SPSS Statistics

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

Module 4. Statistical Software II

4.1. Introduction to the R Environment

4.1.1. How Does R Work?
4.1.2. Creating, Listing and Removing Objects in Memory

4.2. Console in R

4.2.1. Console Environment in R
4.2.2. Main Controls

4.3. Modo Script en R

4.3.1. Console Environment in R
4.3.2. Main Commands

4.4. Objects in R

4.4.1. Objects
4.4.2. Reading Data From a File
4.4.3. Saving Data
4.4.4. Generating Data

4.5. Execution Flow Control Structures

4.5.1. Conditional Structures
4.5.2. Repetitive/Iterative Structures
4.5.3. Vectors and Arrays

4.6. Operations with Objects

4.6.1. Creation of Objects
4.6.2. Converting Objects
4.6.3. Operators
4.6.4. How to Access the Values of an Object: the Indexing System
4.6.5. Accessing an Object's Values with Names
4.6.6. The Data Editor
4.6.7. Simple Arithmetic Functions
4.6.8. Calculations With Arrays

4.7. Functions in R

4.7.1. Loops and Vectorization
4.7.2. Writing a Program in R
4.7.3. Creating Your Own Functions

4.8. Graphics in R

4.8.1. Handling Graphics

4.8.1.1. Opening Multiple Graphics Devices
4.8.1.2. Graph Layouts

4.8.2. Graph Functions
4.8.3. Low-Level Graphing Commands
4.8.4. Graph Parameters
4.8.5. Grid and Lattice Packages

4.9. R Packages

4.9.1. R Library
4.9.2. R Packages

4.10. Statistics in R

4.10.1. A Simple Example of Analysis of Variance
4.10.2. Formulas
4.10.3. Generic Functions

Module 5. Statistical Applications in Industry

5.1. Queuing Theory

5.1.1. Introduction
5.1.2. Queuing Systems
5.1.3. Measures of Effectiveness
5.1.4. Poisson Processes
5.1.5. Exponential Distributions
5.1.6. Birth and Death Processes
5.1.7. Queuing Models with One Server
5.1.8. Models with Multiple Servers
5.1.9. Capacity-Limited Queuing Models
5.1.10. Finite Source Models
5.1.11. General Models

5.2.Introduction to Graphs

5.2.2. Basic Concepts
5.2.3. Oriented and Non-Oriented Graphs
5.2.4. Array Representations: Adjacency and Incidence Arrays

5.3. Graph Applications

5.3.1. Trees: Properties
5.3.2. Rooted Trees
5.3.3. Deep Search Algorithm
5.3.4. Application to Block Determination
5.3.5. Wide Search Algorithm
5.3.6. Minimum Weight Overlay Tree

5.4. Paths and Distances

5.4.1. Distance in Graphs
5.4.2. Critical Path Algorithm

5.5. Maximum Flow

5.5.1. Transport Networks
5.5.2. Minimum Cost Flow Distribution

5.6. Program Evaluation and Review Technique (PERT)

5.6.1. Definition
5.6.2. Method
5.6.3. Applications

5.7. Critical Path Method (CPM)

5.7.1. Definition
5.7.2. Method
5.7.3. Applications

5.8. Project Management

5.8.1. Differences and Advantages between PERT and CPM Methods
5.8.2. Procedure to Draw Network Models
5.8.3. Applications with Random Durations

5.9. Deterministic Inventories

5.9.1. Costs Associated with Flows
5.9.2. Costs Associated with Stocks or Storage
5.9.3. Costs Associated with Processes. Replenishment Planning
5.9.4. Inventory Management Models

5.10. Probabilistic Inventories

5.10.1. Service Level and Safety Stock
5.10.2. Optimal Order Size
5.10.3. One Period
5.10.4. Several Periods
5.10.5. Continuous Review
5.10.6. Periodic Review

Module 6. Sampling Designs

6.1. General Considerations on Sampling

6.1.1. Introduction
6.1.2. Historical Background
6.1.3. Concept of Population, Frame and Sample
6.1.4. Advantages and Disadvantages of Sampling
6.1.5. Stages in a Sampling Process
6.1.6. Sampling Applications
6.1.7. Types of Sampling
6.1.8. Sampling Designs

6.2. Simple Random Sampling

6.2.1. Introduction
6.2.2. MAS (N, n), MASR Sample Design Definition and Associated Parameters
6.2.3. Estimation of Population Parameters
6.2.4. Determining Sample Sizes (without Replenishment)
6.2.5. Determining Sample Sizes (with Replenishment)
6.2.6. Comparison between Simple Random Sampling without and with Replacement
6.2.7. Estimating Subpopulations

6.3. Probability Sampling

6.3.1. Introduction
6.3.2. Sampling Design or Procedure
6.3.3. Statistics, Estimators and Properties
6.3.4. Estimator Distribution in Sampling
6.3.5. Selecting Units without and with Replenishment. Equal Probabilities
6.3.6. Simultaneous Variable Estimation

6.4. Probability Sampling Applications

6.4.1. Main Applications
6.4.2. Examples

6.5. Stratified Random Sampling

6.5.1. Introduction
6.5.2. Definition and Characteristics
6.5.3. Estimators under M.A.E(n)
6.5.4. Bindings
6.5.5. Determining Sample Size
6.5.6. Other Aspects of the M.A.E.

6.6. Stratified Random Sampling Applications

6.6.1. Main Applications
6.6.2. Examples

6.7. Systematic Sampling

6.7.1. Introduction
6.7.2. Estimates in Systematic Sampling
6.7.3. Variance Decomposition in Systematic Sampling
6.7.4. Efficiency of Systematic Sampling Compared to MAS
6.7.5. Variance Estimation: Replicate or Interpenetrating Samples

6.8. Systematic Sampling Applications

6.8.1. Main Applications
6.8.2. Examples

6.9. Indirect Estimation Methods

6.9.1. Ratio Methods
6.9.2. Regression Methods

6.10. Indirect Estimation Methods Applications

6.10.1. Main Applications
6.10.2. Examples

Module 7. Multivariate Statistical Techniques I

7.1. Factor Analysis

7.1.1. Introduction
7.1.2. Fundamentals of Factor Analysis
7.1.3. Factor Analysis
7.1.4. Factor Rotation Methods and Factor Analysis Interpretation

7.2. Factor Analysis Modeling

7.2.1. Examples
7.2.2. Statistical Software Modeling

7.3. Main Component Analysis

7.3.1. Introduction
7.3.2. Main Component Analysis
7.3.3. Systematic Principal Component Analysis

7.4. Principal Component Analysis Modeling

7.4.1. Examples
7.4.2. Statistical Software Modeling

7.5. Correspondence Analysis

7.5.1. Introduction
7.5.2. Independence Test
7.5.3. Row and Column Profiles
7.5.4. Inertia Analysis of a Point Cloud
7.5.5. Multiple Correspondence Analysis

7.6. Correspondence Analysis Modeling

7.6.1. Examples
7.6.2. Statistical Software Modeling

7.7. Discriminant Analysis

7.7.1. Introduction
7.7.2. Decision Rules for Two Groups
7.7.3. Classification over Several Populations
7.7.4. Fisher's Canonical Discriminant Analysis
7.7.5. Choice of Variables: Forwrad and Backward Procedure
7.7.6. Systematic Discriminant Analysis

7.8. Discriminant Analysis Modeling

7.8.1. Examples
7.8.2. Statistical Software Modeling

7.9. Cluster Analysis

7.9.1. Introduction
7.9.2. Distance and Similarity Measures
7.9.3. Hierarchical Classification Algorithms
7.9.4. Non-Hierarchical Classification Algorithms
7.9.5. Procedures to Determine the Appropriate Number of Clusters
7.9.6. Characterization of Clusters
7.9.7. Cluster Analysis Systematics

7.10. Modeling Cluster Analysis

7.10.1. Examples
7.10.2. Statistical Software Modeling

Module 8. Multivariate Statistical Techniques II

8.1. Introduction
8.2. Nominal Scale

8.2.1. Measures of Association for 2x2 Tables

8.2.1.1. Phi Coefficient
8.2.1.2. Relative Risk
8.2.1.3. Razón de productos cruzados (Odds Ratio)

8.2.2. Measures of Association for IxJ Tables

8.2.2.1. Contingency Ratio
8.2.2.2. Cramer's V
8.2.2.3. Lambdas
8.2.2.4. Tau of Goodman and Kruskal
8.2.2.5. Uncertainty Coefficient

8.2.3. El coeficiente Kappa

8.3. Ordinal Scale

8.3.1. Coeficiewntes Gamma
8.3.2. Kendall's Tau-B and Tau-C
8.3.3. Sommers' D

8.4. Interval or Ratio Scale

8.4.1. Eta Coefficient
8.4.2. Pearson's and Spearman's Correlation Coefficients

8.5. Stratified Analysis in 2x2 Tables

8.5.1. Stratified Analysis
8.5.2. Stratified Analysis in 2x2 Tables

8.6. Problem Formulation in Log-linear Models

8.6.1. The Saturated Model for Two Variables
8.6.2. The General Saturated Model
8.6.3. Other Types of Models

8.7. The Saturated Model

8.7.1. Calculation of Effects
8.7.2. Goodness of Fit
8.7.3. Test of K effects
8.7.4. Partial Association Test

8.8.  The Hierarchical Model

8.8.1. The Backward Method

8.9. Probit Response Models

8.9.1. Problem Formulation
8.9.2. Parameter Estimation
8.9.3. Chi-Square Goodness-of-Fit Test
8.9.4. Parallelism Test for Groups
8.9.5. Estimation of the Dose Required to Obtain a Given Response Ratio

8.10. Binary Logistic Regression

8.10.1. Problem Formulation
8.10.2. Qualitative Variables in Logistic Regression
8.10.3. Selection of Variables
8.10.4. Parameter Estimation
8.10.5. Goodness of Fit
8.10.6. Classification of Individuals
8.10.7. Prediction

Module 9. Six Sigma Methodology for Quality Improvement

9.1. Statistical Quality Assurance

9.1.1. Introduction
9.1.2. Statistical Quality Assurance

9.2. Six Sigma Methodology

9.2.1. Quality Standards
9.2.2. Six Sigma Methodology

9.3. Control Charts

9.3.1. Introduction
9.3.2. Processes in Statistical Control and Out-of-Control Processes
9.3.3. Control Charts and Hypothesis Testing
9.3.4. Statistical Basis of Control Charts. General Models
9.3.5. Types of Control Charts

9.4. Other Basic SPC Tools

9.4.1. Case Study
9.4.2. The Rest of the "Magnificent Seven"

9.5. Attribute Control Charts

9.5.1. Introduction
9.5.2. Control Charts for Non-Conforming Fractions
9.5.3. Control Charts for the Number of Non-Conformities
9.5.4. Control Charts for Defects

9.6. Control Charts for Variables

9.6.1. Introduction
9.6.2. Mean and Range Control Charts
9.6.3. Control Charts for Individual Units
9.6.4. Control Charts Based on Moving Averages

9.7. Lot-By-Lot Acceptance Sampling by Attributes

9.7.1. Introduction
9.7.2. Simple Sampling by Attributes
9.7.3. Double Sampling by Attributes
9.7.4. Multiple Sampling by Attributes
9.7.5. Sequential Sampling
9.7.6. Inspection with Rectification

9.8. Process and Measurement System Capability Analysis

9.8.1. Process Capacity Analysis
9.8.2. Capacity Studies of Measuring Systems

9.9. Introduction to Taguchi Methods for Process Optimization

9.9.1. Introduction to Taguchi Methods
9.9.2. Quality through Process Optimization

9.10. Practical Cases

9.10.1. Practical Cases for Control Charts for Attributes
9.10.2. Practical Cases for Control Charts for Variables
9.10.3. Practical Cases for Lot-by-Lot Acceptance Sampling by Attributes
9.10.4. Practical Cases for Process Capability Analysis and Measurement System Capability Analysis
9.10.5. Illustrative Practical Cases for Introduction to Taguchi Methodology for Process Optimization

Module 10. Advanced Prediction Techniques

10.1. General Linear Regression Model

10.1.1. Definition
10.1.2. Properties
10.1.3. Examples

10.2. Partial Least Squares Regression

10.2.1. Definition
10.2.2. Properties
10.2.3. Examples

10.3. Principal Component Regression

10.3.1. Definition
10.3.2. Properties
10.3.3. Examples

10.4. RRR Regression

10.4.1. Definition
10.4.2. Properties
10.4.3. Examples

10.5. Ridge Regression

10.5.1. Definition
10.5.2. Properties
10.5.3. Examples

10.6. Lasso Regression

10.6.1. Definition
10.6.2. Properties
10.6.3. Examples

10.7. Elasticnet Regression

10.7.1. Definition
10.7.2. Properties
10.7.3. Examples

10.8. Non-Linear Prediction Models

10.8.1. Non-Linear Regression Models
10.8.2. Non-Linear Least Squares
10.8.3. Conversion to a Linear Model

10.9. Parameter Estimation in a Non-Linear System

10.9.1. Linearization
10.9.2. Other Parameter Estimation Methods
10.9.3. Initial Values
10.9.4. Computer Programs

10.10. Statistical Inference in Non-Linear Regression

10.10.1. Statistical Inference in Non-Linear Regression
10.10.2. Approximate Inference Validation
10.10.3. Examples

You have before you the perfect opportunity to give your career a 180º turn and specialize in a booming area with future expectations, such as Computational Statistics. Don’t let it pass you by”