Business Intelligence Course

course overview

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This course is designed for organisations and individuals that have at least 2 years of IT experience and have a passion to further their knowledge in the data domain.

No specific pre-requisite experience in data mining or analytics is required.

Skills Gained

  • Identify and explain the Big Data concepts and definitions based on the NIST standards.
  • Understand the difference between machine learning and deep learning.
  • Explain what Artificial Intelligence is and demonstrate common use cases.
  • Appreciate the use of Big Data Technologies in solving complex problems.
  • Able to formulate and execute a Big Data Strategy.
  • Understand and be able to build a Big Data Practice and Centre of Excellence.
  • Communicate the importance of a Big Data Reference Architecture.
  • Gain a good understanding of Statistics and Data Visualisation.
  • Understand the role of key tools, such as R, Python, R Studio and GitHub in your Data Science journey.
  • Confidence in understanding the influence of the Internet of Things (IoT) and Data Science in our modern digital world.


1. Big Data Key Concepts
  • Definition and Characteristics of Big Data
  • Supervised and Unsupervised Machine Learning
  • History of Big Data
  • Pattern Identification
  • Types of Analytics
  • Data Type Characteristics
  • Role of Hadoop
2. The Big Data Framework
  • Key Capabilities
  • Establishing a Big Data function
  • Big Data Maturity Model
3. Big Data Strategy
  • How to Formulate a Big Data Strategy
  • Business Drivers
  • Gaining a Competitive Advantage
4. Big Data Architecture
  • The Importance of Reference Architectures
  • NIST Big Data Reference Architecture
  • Hadoop Architecture
  • System Orchestration
  • Data, Application and Framework Providers
  • Data Consumers
  • Local and Distributed Storage & Processing
  • Data Storage Systems
  • Storage Mechanisms
  • Analysis Architectures
  • Function of Hadoop
5. Big Data Algorithms
  • Descriptive Statistics
  • Classification
  • Regression
  • Clustering
  • Correlation
  • Skew and Standardisation
  • Distributions
  • Sampling & Bias
  • Visualisation
6. Big Data Processes
  • Key Big Data Processes
  • Tools and Techniques
  • Problem Types
  • Data Analysis
  • Data Governance
  • Data Management
7. Big Data Functions
  • Centre or Excellence
  • Big Data Team
  • Big Data Lab
  • Proof of Concepts
  • Agile Methods
  • Charging Models
  • Roles & Responsibilities
  • Critical Success Factors
8. Artificial Intelligence (AI)
  • What is AI
  • Measuring AI
  • Cognitive Analytics
  • Natural Language Processing (NLP)
  • Knowledge Representation
  • Automated Reasoning
  • Machine Learning
  • Deep Learning

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Thinking about Onsite?

If you need training for 3 or more people, you should ask us about onsite training. Putting aside the obvious location benefit, content can be customised to better meet your business objectives and more can be covered than in a public classroom. Its a cost effective option. One on one training can be delivered too, at reasonable rates.

Submit an enquiry from any page on this site, and let us know you are interested in the requirements box, or simply mention it when we contact you.

All $ prices are in USD unless it’s a NZ or AU date

SPVC = Self Paced Virtual Class

LVC = Live Virtual Class

Please Note: All courses are availaible as Live Virtual Classes

Trusted by over 1/2 million students in 15 countries

Our clients have included prestigious national organisations such as Oxford University Press, multi-national private corporations such as JP Morgan and HSBC, as well as public sector institutions such as the Department of Defence and the Department of Health.