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Master in Big Data Architecture

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About this course

This Master in Big Data Architecture is designed for IT or business management professionals looking for an updated training to learn how to use Big Data in the digital transformation of any company using the main tools and technologies to analy...

University degree

1500 h

Online

Qualification of Master in Big Data Architecture

DEGREE awarded by EUROINNOVA INTERNATIONAL ONLINE EDUCATION, member of the AEEN (Spanish Association of Business Schools) and recognized with academic excellence in online education by QS World University Rankings.

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Objectives

Establish the criteria and steps to follow in order to carry out an effective digital transformation.

Learn how Big Data can positively affect all processes and tasks of a company.

Understand Big Data architecture and how to carry out effective data mining and analysis.

Develop professional data visualization reports using different tools such as PowerBI or Tableau.

Use artificial intelligence algorithms, machine learning and Deep Learning applied to Big Data.

Know how to apply natural language processing (NLP) for the creation of chatbots.

Content

MODULE 1. DIGITAL TRANSFORMATION

UNIT 1. INTRODUCTION TO DIGITAL TRANSFORMATION
Introduction to digital transformation
Concept of innovation
Concept of technology
Typology of technology
Competitive advantage point of view
According to its disposition in the company
From the point of view of a project
Other types of technology
Technological innovation
Core competencies of technological innovation
The process of technological innovation
Tools to innovate
Competitiveness and innovation
UNIT DIDACTIC 2. SOCIETY 3.0
Web 3.0 philosophy and its impact on the business world
Socialization of the Web
Adaptation of the business world to new technologies
UNIT 3. NEW DIGITAL ECOSYSTEM
Community Manager
Chief Data Officer
Data Protection Officer
Data Scientist
Other profiles
IT Skills Development
The Role of the CEO as a Leader in Transformation
UNIT DIDACTIC 4. NEW BUSINESS MODELS IN THE DIGITAL ENVIRONMENT
The Digital Transition from the Traditional Business Model
New Business Models
Freemium
Long Tail Model
Cloud and SaaS Model
Subscription Model
Dropshipping
Affiliation
Infoproducts and E-Learning
Others
UNIT DIDACTIC 5. DIGITAL TRANSFORMATION PLAN
Diagnosis of the digital maturity of the company
Analysis of innovation in the company
Preparation of the roadmap
Provision of financing and technological resources
Implementation of the digital transformation plan
Follow-up of the digital transformation plan
UNIT DIDACTIC 6. SUCCESS CASES IN DIGITAL TRANSFORMATION
BBVA and the smart company
DKV Salud y #MédicosfrentealCOVID
El Corte Inglés
Cepsa y su apuesta por los servicios cloud de AWS
UNIDAD DIDÁCTICA 7. THE NEW DIGITAL CUSTOMER
Redesigning the customer experience
The transformation of distribution channels: omnichannel
Digital marketing plan
Buyer's Journey
Growth Hacking: growth strategy
The new role of marketing in the conversion funnel
UNIT DIDACTIC 8. NEW MARKETS, NEW OPPORTUNITIES
Innovation opportunities arising from globalization
How to Invent Markets through Innovation
Development stages and life cycles
Market entry
Development methodologies
UNIT DIDACTIC 9. INNOVATION IN ORGANIZATIONAL PROCESSES
The digital transformation of the value chain
Industry 4.0
Organizational adaptation through talent and innovation
Innovation process models
Innovation management
Innovation system
How to reinvent companies by innovating in processes
Innovation in Processes through ICT
E-Commerce: innovating in distribution channels
Voluntary case study: Innovation according to Steve Jobs
Helvex case: continuous change
Business automation: RPA, RBA and RDA

MODULE 2. BIG DATA INTRODUCTION

UNIT 1. INTRODUCTION TO BIG DATA
What is Big Data?
The era of big data. History of big data
The importance of storing and extracting information
Big Data focused on business
Open Data
Public information
IoT (Internet of Things-Internet of Things)
UNIT DIDACTIC 2. DATA SOURCES
Definition and relevance of data source selection
Nature of Big Data sources
DIDACTIC UNIT 3. OPEN DATA
Definition, Benefits and Features
Example of using Open Data
UNIT DIDACTIC 4. PHASES OF A BIG DATA PROJECT
Initial diagnosis
Project design
Implementation process
Project monitoring and control
Responsible and available resources
Scheduling
Scope and economic valuation of the project
UNIT 5. BUSINESS INTELLIGENCE AND THE INFORMATION SOCIETY
Defining the concept of Business Intelligence and the information society
Architecture of a Business Intelligence solution
Business Intelligence in the company's departments
Concepts of Master Plan, Strategic Plan and Annual Operating Plan
Operational Systems and ETL Processes in a BI system
Advantages and Risk Factors of Business Intelligence
UNIT 6. MAIN BUSINESS INTELLIGENCE PRODUCTS
Balanced Scorecards (BSC)
Decision Support Systems (DSS)
Executive Information Systems (EIS)
UNIT 7. BIG DATA AND MARKETING
Big Data support in the decision making process
Operational decision making
Strategic marketing and Big Data
New trends in management
UNIT DIDACTIC 8. FROM BIG DATA TO LINKED OPEN DATA
Semantic Web Concept
Linked Data Vs Big Data
SPARQL Query Language
UNIT DIDACTIC 9. INTERNET OF THINGS
Internet of Things (IoT) Context
What is IoT?
Elements that make up the IoT ecosystem
IoT architecture
Devices and elements used
Examples of use
Challenges and future lines of work

MODULE 3. BIG DATA ARCHITECTURE

UNIT 1. BATCH PROCESSING
Hadoop
Pig
Hive
Sqoop
Flume
Spark Core
Spark 2.0
UNIDAD DIDÁCTICA 2. STREAMING PROCESSING
Streaming Processing Fundamentals
Spark Streaming
Kafka
Pulsar and Apache Apex
Implementing a real-time system
TEACHING UNIT 3. NOSQL SYSTEMS
Hbase
Cassandra
MongoDB
NeoJ
Redis
Berkeley DB
TEACHING UNIT 4. INTERACTIVE QUERY
Lucene + Solr
TEACHING UNIT 5. HYBRID COMPUTING SYSTEMS
Lambda Architecture
Kappa Architecture
Apache Flink and practical implementations
Druid
ElasticSearch
Logstash
Kibana
UNIT DIDACTIC 6. CLOUD COMPUTING
Amazon Web Services
Google Cloud Platform
TEACHING UNIT 7. BIG SYSTEMS ADMINISTRATION
Cluster Administration and Installation: Cloudera and Hortonworks
Service Optimization and Monitoring
Security: Apache Knox, Ranger and Sentry
UNIT DIDACTIC 8. DATA VISUALIZATION
Visualization tools: Tableau and CartoDB
Visualization libraries: D, Leaflet, Cytoscape

MODULE 4. DATA MINING AND ANALYSIS

UNIT 1. DATA MINING OR DATA MINING AND AUTOMATIC LEARNING
Introduction to Data Mining and Machine Learning
KDD Process
Data Mining Models and Techniques
Application Areas
Text Mining and Web Mining
Data mining and marketing
UNIT DIDACTIC 2. DATAMART. DEPARTMENTAL DATABASE CONCEPT
Approach to the DataMart concept
Data extraction, transformation and loading (ETL) processes
Data Warehou
Exploitation tools
Tools for OLAP cube development
UNIT DIDACTIC 3. DATAWAREHOUSE OR CORPORATE DATA WAREHOUSE.
Overview. Why DataWarehouse?
Structure and Construction
Implementation Phases
Features
Data Warehouse in the Cloud
UNIT DIDACTIC 4. BUSINESS INTELLIGENCE AND ANALYTICS TOOLS
Types of BI tools
Commercial BI products
Open Source BI products
Benefits of BI tools
UNIT DIDACTIC 5. HADOOP ECOSYSTEM
What is Hadoop? Relationship with Big Data
Installation and configuration of Hadoop infrastructure and ecosystem
HDFS file system
MapReduce with Hadoop
Apache Hive
Apache Hue
Apache Spark
DIDACTIC UNIT 6. WEKA AND DATA MINING
What is Weka?
Data Mining Techniques in Weka
Weka Interfaces
Attribute Selection
UNIT 7. PENTAHO
An approach to Pentaho
Solutions offered by Pentaho
MongoDB & Pentaho
Hadoop & Pentaho
Weka & Pentaho
UNIT DIDACTIC 8. INTRODUCTION TO DATA SCIENCE.
What is data science?
Tools needed for the data scientist
Data Science & Cloud Computing
Legal Aspects in Data Protection
UNIT DIDACTIC 9. RELATIONAL DATABASES
Introduction
The Relational Model
SQL Query Language
MySQL. A relational database
UNIT 10. NOSQL DATABASES AND SCALABLE STORAGE
What is a NoSQL database?
Relational databases Vs NoSQL databases
Type of NoSQL databases. CAP Theorem
NoSQL Database Systems
UNIT 11. INTRODUCTION TO A NOSQL DATABASE SYSTEM. MONGODB
What is MongoDB?
How MongoDB works and how to use it
First steps with MongoDB. Installation and command shell
Creating our first NoSQL Database.Model and Data Insertion
Updating data in MongoDB. Set and update statements
Working with indexes in MongoDB for data optimization
Querying data in MongoDB
UNIT DIDACTIC 12. PYTHON AND DATA ANALYSIS
Introduction to Python
What do you need?
Libraries for data analysis in Python
MongoDB, Hadoop and Python. Dream Team of Big Data
UNIT 13. R AS A TOOL FOR BIG DATA
Introduction to R
What do you need?
Data Types
Descriptive and Predictive Statistics with R
Integrating R into Hadoop
UNIT TEACHING 14. PRE-PROCESSING & DATA PROCESSING
Obtaining and cleaning data (ETL)
Statistical inference
Regression models
Hypothesis testing
TEACHING UNIT 15. DATA ANALYSIS
Analytical business intelligence
Graph theory and social network analysis
Presentation of results

MODULE 5. DATA VISUALIZATION

UNIT 1. INTRODUCTION TO DATA VISUALIZATION
What is data visualization?
Importance and tools of data visualization
Data visualization: Basic Principles
UNIT 2. TABLEAU
What is Tableau? Uses and Applications
Tableau Server: Architecture and Components
Tableau Installation
Workspace and Navigation
Data Connections in Tableau
Types of filters in Tableau
Sorting data, groups, hierarchies and sets
Tables and charts in Tableau
UNIT DIDACTIC 3. D3 (DATA DRIVEN DOCUMENTS)
D3 Fundamentals
D3 Installation
D3 Operation
SVG
Data types in D3
Bar chart with D3
Scatter plot with D3
UNIT DIDACTIC 4. LOOKER STUDIO (GOOGLE DATA STUDIO)
Data visualization
Chart typologies
Data sources
Report creation
UNIT 5. QLIKVIEW
Installation and architecture
Loading data
Reporting
Data transformation and modeling
Data analysis
UNIT 6. POWER BI
Introduction to Power BI
Installing Power BI
Data modeling
Data visualization
Dashboards
Data sharing
UNIT DIDACTIC 7. CARTO
CartoDB
What is CARTO?
Loading and using data. Types of analysis
Programming a viewer with the CARTO.js library
Using examples and help from the API documentation

MODULE 6. ARTIFICIAL INTELLIGENCE (AI), MACHINE LEARNING (ML) AND DEEP LEARNING (DL)

MODULE 7. PLN, CHATBOTS AND ARTIFICIAL INTELLIGENCE
UNIT 1. INTRODUCTION TO PLN
DIDACTIC UNIT 2. PLN IN PYTHON
TEACHING UNIT 3. COMPUTING SYNTAXIS FOR PLN
TEACHING UNIT 4. COMPUTING SEMANTICS FOR PLN
TEACHING UNIT 5. INFORMATION RETRIEVAL AND EXTRACTION
UNIT DIDACTIC 6. WHAT IS A CHATBOT?
TEACHING UNIT 7. RELATIONSHIP BETWEEN AI AND CHATBOTS
TEACHING UNIT 8. CHATBOTS APPLICATION AREAS
MODULE 8. ARTIFICIAL VISION IN INDUSTRY 4.0 WITH PYTHON AND OPENCV
UNIT 1. ARTIFICIAL VISION: DEFINITION AND MAIN ASPECTS
UNIT 2. COMPONENTS OF AN ARTIFICIAL VISION SYSTEM
UNIT DIDACTIC 3. IMAGE PROCESSING USING ARTIFICIAL VISION
TEACHING UNIT 4. APPLICATIONS OF VISION IN INDUSTRY 4.0
UNIT DIDACTIC 5. OPENCV INTRODUCTION AND INSTALLATION
UNIT DIDACTIC 6. FILE HANDLING, CAMERAS AND GRAPHIC INTERFACES
UNIT DIDACTIC 7. IMAGE PROCESSING
UNIT DIDACTIC 8. HISTOGRAMS AND TEMPLATE MATCHING
TEACHING UNIT 9. COLORS AND COLOR SPACES
TEACHING UNIT 10. FACE DETECTION AND FEATURE EXTRACTION.
TEACHING UNIT 11. SELF-LEARNING

Methodology

Entre el material entregado en este curso se adjunta un documento llamado Guía del Alumno dónde aparece un horario de tutorías telefónicas y una dirección de e-mail dónde podrá enviar sus consultas, dudas y ejercicios. Además recibirá los materiales didácticos que incluye el curso para poder consultarlos en cualquier momento y conservarlos una vez finalizado el mismo.La metodología a seguir es ir avanzando a lo largo del itinerario de aprendizaje online, que cuenta con una serie de temas y ejercicios. Para su evaluación, el alumno/a deberá completar todos los ejercicios propuestos en el curso. La titulación será remitida al alumno/a por correo una vez se haya comprobado que ha completado el itinerario de aprendizaje satisfactoriamente.

What does it prepare you for?

Thanks to this Master in Big Data Architecture you will see how, thanks to the mining and analysis of massive information through different tools that Big Data offers, you will be able to effectively carry out the digital transformation of any company. In addition, you will learn to apply artificial intelligence algorithms, machine learning and deep learning that allow you to create updated and effective decision, choice and recommendation systems.

Who is it aimed at?

Career opportunities

Big Data is one of the labor sectors with the highest demand today, so if you take this Master in Big Data Architecture, you will open the doors of many profiles such as Big Data Architect, Data Scientist, IA Engineer, Machine Learning Developer, Machine Vision Programmer or Expert in Business Intelligence solutions.