Big Data Science Professional


Workshops not currently scheduled

We are currently planning the workshops for this course. You may purchase an eLearning kit for immediate access or contact us for further details.

USD $360.67 $180.33 eLearning Kit only

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: 3 Modules

  • Module 1: Fundamental Big Data
  • Module 2: Big Data Analysis & Technology Concepts
  • Module 3: 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.

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…

 Exams & Certification

  • The exams required for certification can be taken at any Pearson VUE testing center in the world or online via Pearson VUE Online Proctoring.
  • Please ensure you purchase the correct exam voucher as they are not exchangable.
  • Those that pass the exams required for the Certified Big Data Science Professional designation will receive official certificate for this designation and will have access to the benefits associated with this certification.


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

Module 1: Fundamental Big Data

A foundational course that establishes a basic understanding of Big Data from business and technology perspectives, including common benefits, challenges and adoption issues.

The following primary topics are covered: 

•Fundamental Terminology and Concepts
•A Brief History of Big Data
•Business Drivers that Have Led to Big Data Innovations
•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
•New Roles Introduced by Big Data Projects
•Emerging Trends

Module 2: Big Data Analysis & Technology Concepts

Explores contemporary analysis practices, technologies, and tools for Big Data environments at a conceptual level, focusing on common analysis functions and features of Big Data solutions.

The following primary topics are covered:

•The Big Data Analysis Lifecycle (from dataset identification to integration, analysis, and visualization)
•Common Analysis and Analytics Techniques•A/B testing, Regression, Correlation, Text Analytics
•Sentiment Analysis, Time Series Analysis
•Network Analysis, Spatial Analysis
•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
•Foundational Big Data Technology Mechanisms
•Big Data Storage (Query Workload, Sharding, Replication, CAP, ACID, BASE)
•Big Data Processing (Parallel Data Processing, Distributed Data Processing, SharedEverything/Nothing Architecture, SCV)

Module 3: Big Data Analysis & Technology Lab

This course module presents participants with a series of exercises and problems designed to test their ability to apply knowledge of topics covered previously in course modules 1 and 2. Completing this lab will help highlight areas that require further attention, and will further prove hands-on proficiency in Big Data analysis and technology and practices as they are applied and combined to solve real-world problems.

As a hands-on lab, this module provides a set of detailed exercises that require participants to solve a number of inter-related problems, with the goal of fostering a comprehensive understanding of how Big Data environments work from both front and back-ends, and how they are used to solve real-world analysis and analytics problems.


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

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