Enterprise Big Data Professional EBDP

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.

• 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