Big Data Training

Big Data is big business. There are quintillion bytes of data being generated every day. While companies are using Big Data Science to sift through this avalanche of data and find the information that is relevant to the needs of the company, it is the role of the IT Professionals to build the architecture that supports this process. In this course, you will learn the mechanisms, approaches and design patterns that are used to architect Big Data solutions.

Get a sample of the contents of this course with this short video lesson that provides a non-technical introduction to Big Data Analysis. This second video lesson is an introduction to Big Data Analytics. Both of these topics are part of Module 1 of the course.

The eLearning kit bundle provides access to a 5 Module course bundle that will be available for 1 year from the date of purchase. The course outline is provided in the Details tab, you can also download a pdf version here. You can also take the on-line exam to get a Digital Badge to show your expertise in this field.

Course contents: 5 Modules

• Module 1: Fundamental Big Data
• Module 2: Big Data Analysis & Technology Concepts
• Module 10: Fundamental Big Data Architecture
• Module 11: Advanced Big Data Architecture
• Module 12: Big Data Architecture Lab

Intended for
IT Professionals, Solution Architects, Enterprise Architects, Big Data specialists

Pre-requisites
An understanding of IT concepts and IT architecture is required for this course.

Learning Outcomes

A comprehensive understanding of the approaches, design patterns and the mechanisms that are essential for architecting Big Data systems. Some of the topics covered include:

• Fundamental Terminology and Concepts
• Basic Big Data Analytics
• Big Data and Traditional Business Intelligence and Data Warehouses
• Common Analysis and Analytics Techniques
• Automated Recommendation, Classification, Clustering
• Machine Language, Natural Language, Semantics
• Big Data Technology Mechanisms
• Big Data Storage (Query Workload, Sharding, Replication, CAP, ACID, BASE)
• Big Data Processing
• New Big Data Mechanisms
• Enterprise Data Warehouse and Big Data Integration Approaches
• Architectural Big Data Environments
• Cloud Computing & Big Data Architectural Considerations
• Big Data Solution Architectural Layers
• Big Data Solution Design Patterns
• Big Data Architectural Compound Patterns

And more

 Note: All quoted pricing is excluding GST. For customers in Australia GST will be added during the check-out process.

Have you completed any other Big Data course from our selection? If so, this upgrade kit will allow you to extend your knowledge without having to repeat the first two modules.

Big Data is big business. There are quintillion bytes of data being generated every day. While companies are using Big Data Science to sift through this avalanche of data and find the information that is relevant to the needs of the company, it is the role of the IT Professionals to build the architecture that supports this process. In this course, you will learn the mechanisms, approaches and design patterns that are used to architect Big Data solutions.

Intended for
IT Professionals, Solution Architects, Enterprise Architects, Big Data specialists

Pre-requisites
An understanding of IT concepts and IT architecture is required for this course.

Learning Outcomes

A comprehensive understanding of the approaches, design patterns and the mechanisms that are essential for architecting Big Data systems. Some of the topics covered include:

• New Big Data Mechanisms
• Enterprise Data Warehouse and Big Data Integration Approaches
• Architectural Big Data Environments
• Cloud Computing & Big Data Architectural Considerations
• Big Data Solution Architectural Layers
• Big Data Solution Design Patterns
• Big Data Architectural Compound Patterns

And more

 In the Details tab you can find more information about this eLearning Kit

Note: All quoted pricing is excluding GST. For customers in Australia GST will be added during the check-out process.

What is Big Data? It is data that your current systems and techniques are unable to process. Processing this data requires new techniques that also adds to the processing and storage demands of your IT systems. In this 5-module course you’ll learn the patterns, mechanisms and approaches that will enable you to Engineer Big Data solutions.

Get a sample of the contents of this course with this short video lesson that provides a non-technical introduction to Big Data Analysis. This second video lesson is an introduction to Big Data Analytics. Both of these topics are part of Module 1 of the course.

The eLearning kit bundle provides access to a 5 Module course bundle that will be available for 1 year from the date of purchase. The course outline is provided in the Details tab, you can also download a pdf version here. You can also take the on-line exam to get a Digital Badge to show your expertise in this field.

Course contents: 5 Modules

• Module 1: Fundamental Big Data
• Module 2: Big Data Analysis & Technology Concepts
• Module 7: Fundamental Big Data Engineering
• Module 8: Advanced Big Data Engineering
• Module 9: Big Data Engioneering Lab

Intended for
Intended for architects and IT Professionals that are responsible for the engineering Big Data solutions as well as Solution Architects, Enterprise Architects, Data Architects, BI Specialists.

Learning Outcomes

A comprehensive understanding of the approaches, design patterns and the mechanisms that are essential for architecting Big Data systems. Some of the topics covered include:

• Fundamental Terminology and Concepts
• Basic Big Data Analytics
• Big Data and Traditional Business Intelligence and Data Warehouses
• Common Analysis and Analytics Techniques
• Automated Recommendation, Classification, Clustering
• Machine Language, Natural Language, Semantics
• Big Data Technology Mechanisms
• Big Data Storage Terminologies (including sharding, replication, CAP theorem, ACID, BASE)
• Big Data Storage Requirements
• Introduction to NoSQL – NewSQL
• NoSQL Database Types (including key-value, document, column-family and graph databases)
• Big Data Processing Requirements
• MapReduce Explained (including map, combine, partition, shuffle and sort, and reduce)
• Advanced Big Data Engineering Mechanisms (including serialization & compression engines)
• In-Memory Storage Devices, In-Memory Data Grids & In-Memory Databases
• Read-Through, Read-Ahead, Write-Through & Write-Behind Integration Approaches
• Polyglot Persistence (including Explanation, Issues & Recommendations)
• Realtime Big Data Processing Concepts (including Speed Consistency Volume (SCV), Event Stream Processing (ESP) & Complex Event Processing (CEP))
• General Realtime Big Data Processing & Realtime Big Data Processing & MapReduce
• Bulk Synchronous Parallel (BSP) Processing Engine & BSP vs. MapReduce
• Big Data Solutions (including Characteristics, Design Considerations & Design Process)

And more…

 The elearning kit is accessible for one year from date of purchase. In the Details tab you can find more information about this eLearning Kit.

Note: All quoted pricing is excluding GST. For customers in Australia GST will be added during the check-out process.

Have you completed any other Big Data course from our selection? If so, this upgrade kit will allow you to extend your knowledge without having to repeat the first two modules.

What is Big Data? It is data that your current systems and techniques are unable to process. Processing this data requires new techniques that also adds to the processing and storage demands of your IT systems. In this 3-module course you’ll learn the patterns, mechanisms and approaches that will enable you to Engineer Big Data solutions.

Intended for
Intended for architects and IT Professionals that are responsible for the engineering Big Data solutions as well as Solution Architects, Enterprise Architects, Data Architects, BI Specialists.

Learning Outcomes

A comprehensive understanding of the approaches, design patterns and the mechanisms that are essential for architecting Big Data systems. Some of the topics covered include:

• Big Data Storage Terminologies (including sharding, replication, CAP theorem, ACID, BASE)
• Big Data Storage Requirements
• Introduction to NoSQL – NewSQL
• NoSQL Database Types (including key-value, document, column-family and graph databases)
• Big Data Processing Requirements
• MapReduce Explained (including map, combine, partition, shuffle and sort, and reduce)
• Advanced Big Data Engineering Mechanisms (including serialization & compression engines)
• In-Memory Storage Devices, In-Memory Data Grids & In-Memory Databases
• Read-Through, Read-Ahead, Write-Through & Write-Behind Integration Approaches
• Polyglot Persistence (including Explanation, Issues & Recommendations)
• Realtime Big Data Processing Concepts (including Speed Consistency Volume (SCV), Event Stream Processing (ESP) & Complex Event Processing (CEP))
• General Realtime Big Data Processing & Realtime Big Data Processing & MapReduce
• Bulk Synchronous Parallel (BSP) Processing Engine & BSP vs. MapReduce
• Big Data Solutions (including Characteristics, Design Considerations & Design Process)

And more…

 The elearning kit is accessible for one year from date of purchase. In the Details tab you can find more information about this eLearning Kit.

Note: All quoted pricing is excluding GST. For customers in Australia GST will be added during the check-out process.

There are quintillion bytes of data being generated every day. Big Data Science techniques provide the necessary approaches to sift through this avalanche of data and find the information that is relevant to the needs of your organisation. In this course, you will learn the algorithms, mathematical and statistical techniques and application of analytics to process Big Data.

Get a sample of the contents of this course with this short video lesson that provides a non-technical introduction to Big Data Analysis. This second video lesson is an introduction to Big Data Analytics. Both of these topics are part of Module 1 of the course.

The eLearning kit bundle provides access to a 5 Module course bundle that will be available for 1 year from the date of purchase. The course outline is provided in the Details tab, you can also download a pdf version here. You can also take the on-line exam to get a Digital Badge to show your expertise in this field.

Course contents: 5 Modules

• Module 1: Fundamental Big Data
• Module 2: Big Data Analysis & Technology Concepts
• Module 4: Big Data Analysis and Technology Lab
  

Intended for
Data Scientists, BI Specialists, Business Analysts, Data Architects and other IT Professionals that are responsible for the creating the algorithms and approaches that will be used to analyse Big Data.

 Pre-requisites
An understanding of Data Analytics or analytical mathematics and IT systems is recommended.

Learning Outcomes
A good understanding of Big Data and some of the techniques and approaches required to analyse it will be gained throughout this course. Some of the topics include:

• Fundamental Terminology and Concepts
• Characteristics of Big Data
• Benefits of Adopting Big Data
• Challenges and Limitations of Big Data
• Basic Big Data Analytics
• Big Data and Traditional Business Intelligence and Data Warehouses
• Big Data Visualization
• Common Adoption Issues
• Planning for Big Data Initiatives
• The Big Data Analysis Lifecycle
• Common Analysis and Analytics Techniques
• Machine Language, Natural Language, Semantics
• Data Visualization and Visual Analysis
• Assessing Hierarchies, Part-to-Whole Relationships
• Plotting Connections and Relationships, Mapping Geo-Spatial Data
• Foundational Big Data Technology Mechanisms
• Big Data Storage
• Big Data Processing
• Big Data & Cloud Computing

And more…

Note: All quoted pricing is excluding GST. For customers in Australia GST will be added during the check-out process.

There are quintillion bytes of data being generated every day. Big Data Science techniques provide the necessary approaches to sift through this avalanche of data and find the information that is relevant to the needs of your organisation. In this course, you will learn the algorithms, mathematical and statistical techniques and application of analytics to process Big Data.

Get a sample of the contents of this course with this short video lesson that provides a non-technical introduction to Big Data Analysis. This second video lesson is an introduction to Big Data Analytics. Both of these topics are part of Module 1 of the course.

The eLearning kit bundle provides access to a 5 Module course bundle that will be available for 1 year from the date of purchase. The course outline is provided in the Details tab, you can also download a pdf version here. You can also take the on-line exam to get a Digital Badge to show your expertise in this field.

Course contents: 5 Modules

• Module 1: Fundamental Big Data
• Module 2: Big Data Analysis & Technology Concepts
• Module 4: Fundamental Big Data Analysis & Science
• Module 5: Advanced Big Data Analysis & Science
• Module 6: Big Data Analysis & Science Lab

Intended for
Data Scientists, BI Specialists, Business Analysts, Data Architects and other IT Professionals that are responsible for the creating the algorithms and approaches that will be used to analyse Big Data.

Pre-requisites
An understanding of Data Analytics or analytical mathematics and IT systems is recommended.

Learning Outcomes
A solid understanding of Big Data and the techniques and approaches required to analyse it will be gained throughout this course. Some of the topics include:

• Fundamental Terminology and Concepts
• Big Data and Traditional Business Intelligence and Data Warehouses
• Common Analysis and Analytics Techniques
• Automated Recommendation, Classification, Clustering
• Machine Language, Natural Language, Semantics
• Data Visualization and Visual Analysis
• Assessing Hierarchies, Part-to-Whole Relationships
• Plotting Connections and Relationships, Mapping Geo-Spatial Data
• Data Science, Data Mining & Data Modeling
• Exploratory Data Analysis (EDA)
• Statistics Analysis (including descriptive, inferential, correlation, covariance & hypothesis testing)
• Data Munging & Machine Learning
• Statistical Measures & Statistical Inference
• Distributions & Data Processing Techniques
• Data Discretization, Binning, Clustering
• Visualization Techniques & Numerical Summaries
• Machine Learning Algorithms, Pattern Identification
• Advanced Statistical Techniques
• Linear Regression & Logistic Regression for Big Data
• Decision Trees for Big Data
• Classification Rules for Big Data
• K Nearest Neighbor (kNN) for Big Data
• Naïve Bayes for Big Data
• Association Rules for Big Data
• K-means for Big Data
• Text Analytics for Big Data
• Outlier Detection for Big Data

And more…

Note: All quoted pricing is excluding GST. For customers in Australia GST will be added during the check-out process.