Improve your skills on Database Application Development, thanks to the best online university in the world according to Forbes, thanks to TECH”

Data analysis is fundamental to engineering decision making. These professionals use data to design and develop products and systems, identify problems, or evaluate the performance of existing systems. If data is not analyzed correctly, incorrect decisions can be made or good opportunities can be lost.

For this reason, TECH has designed a Postgraduate certificate in Data Analysis and Processing with which it seeks to provide students with the necessary skills and competencies to be able to perform their work as specialists, with the highest possible efficiency and quality. Therefore, throughout this program, aspects such as Database Design Stages, Missing Value Imputation, Homoscedasticity Tests, Data Files or Notions of HTML and Regular Expressions will be addressed.

All this, through a convenient 100% online format that allows students to organize their schedules and studies, combining them with their other day-to-day work and interests. In addition, this qualification has the most complete theoretical and practical materials on the market, which facilitates the student's study process and allows them to achieve their goals quickly and efficiently.

Become the successful professional you have always wanted to be in the field of Data Analysis and Processing, in just 12 weeks and with total freedom of organization”

This Postgraduate certificate in Data Analysis and Processing 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 Data Analysis and Processing
• The graphic, schematic, and practical contents with which they are created provide practical information on the 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 to the expert, debate forums on controversial topics, and individual reflection assignments
• The availability of access to the contents from any device fixed or portable device with Internet connection
• Content that is accessible from any fixed or portable device with an Internet connection

Enhance your professional profile in one of the most promising areas in the field of Economic Statistics, thanks to TECH and the most innovative materials”

The program’s teaching staff includes professionals from sector who contribute their work experience to this educational program, as well as renowned specialists from leading societies and prestigious universities.

The 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 immersive education programmed to learn in real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the academic year For this purpose, the student will be assisted by an innovative interactive video system created by renowned and experienced experts.

Delve into the Stages of Database Design from the comfort of your home and at any time of the day"

Access all the content on HTML Notions and Regular Expressions, from your Tablet, cell phone or computer"

The structure and all the didactic resources of this syllabus have been designed by the renowned professionals that make up TECH's team of experts in this area of engineering. These specialists have used their extensive experience and their most advanced knowledge to create practical and completely up-to-date contents. All this, based on the most efficient teaching methodology, TECH's Relearning.

Expand your knowledge of Statistics Applied to Economics, thanks to the most innovative teaching materials and a wide variety of additional content available on the Virtual Campus”

Module 1. Databases: Design and Management

1.1. Introduction to Databases

1.1.1. What is a Database?
1.1.2. History of Database Systems

1.2. Information System and Databases

1.2.1. Concepts
1.2.2. Features
1.2.3. Evolution of Databases

1.3. Definition and Characteristics of a Database Management System

1.3.1. Definition
1.3.2. Features

1.4. Architecture of Database Management Systems

1.4.1. Centralized and Client-Server Architectures
1.4.2. Server Systems Architectures
1.4.3. Parallel Systems
1.4.4. Distributed Systems
1.4.5. Types of Networks

1.5. Main Database Management Systems

1.5.1. Types of DBMS

1.6. Development of Database Applications

1.6.1. Web Interfaces for Databases
1.6.2. Performance Tuning
1.6.3. Performance Testing
1.6.4. Standardization
1.6.5. E-Commerce
1.6.6. Inherited Systems

1.7. Database Design Stages

1.7.1. Conceptual Design
1.7.2. Logical Design
1.7.3. Application Design

1.8. Database Implementation

1.8.1. Structured Query Language (SQL)
1.8.2. Data Processing
1.8.3. Data Query
1.8.4. SQL Database Management
1.8.5. Working with SQLite Databases

1.9. Notions of HTML and Regular Expressions

1.9.1. Structure and Code of a Web Page
1.9.2. HTML and CSS Tags and Attributes
1.9.3. Text Searching with Regular Expressions
1.9.4. Special Characters, Sets, Groups and Repetitions

1.10. Collecting and Storing Data from Web Pages

1.10.1. Introduction to Web Scraping Tools
1.10.2. Programming Web Scraping Tools in Python
1.10.3. Searching and Obtaining Information with Regular Expressions
1.10.4. Searching and Obtaining Information with Beautiful Soup
1.10.5. Storing in Databases
1.10.6. Exporting Results in Comma-Separated Value Files

Module 2. Data Analysis and Debugging

2.1. Data Files: Encoding and Transformation

2.1.1. Data Coding
2.1.2. Data Transformation

2.2. Data Integrity Control: Univariate Study

2.2.1. Models
2.2.2. Properties

2.3. Data Integrity Control: Bivariate Study

2.3.1. Models
2.3.2. Properties

2.4. Data Integrity Control: Multivariate Study

2.4.1. Models
2.4.2. Properties

2.5. Missing Value Detection

2.5.1. Missing Data Problems

2.6. Treatment of Missing Values

2.6.1. Missing Value Analysis

2.7. Imputation of Missing Values

2.7.1. Imputation of Missing Values in One-Dimensional Variables
2.7.2. Multiple Imputation Methods

2.8. Normality Tests for the Assessment of Starting Assumptions for Data Analysis

2.8.1. Types of Tests
2.8.2. Examples

2.9. Homoscedasticity Tests for the Assessment of Starting Assumptions for Data Analysis

2.9.1. Types of Tests
2.9.2. Examples

2.10. Independence Tests for the Assessment of Starting Assumptions for Data Analysis

2.10.1. Types of Tests
2.10.2. Examples

Thanks to TECH Relearning metholodgy, you will be able to acquire new knowledge in a precise and natural way, without spending too much time studying"