Every day, organisations generate more data than most teams know what to do with: transactions, sensor readings, customer clicks, support tickets. The gap between organisations that thrive and the ones that drown isn't how much data they collect, it's whether they can turn that flood of information into insight fast enough to act on it, and use it to support smarter, more strategic decisions.

This programme from the Arab British Fellowship Training Academy builds that capability end to end: collecting, processing, storing and analysing big data using current tools and technologies, alongside the AI and predictive analytics techniques that turn raw numbers into decisions. It also covers how big data strengthens organisational performance, fosters innovation, and increases competitiveness in a dynamic, fast-changing business environment.

Who Should Attend?

• Data analysts and information specialists.
• IT and information systems managers.
• Professionals interested in artificial intelligence and data analytics.
• Students and graduates in data science and information technology.

Programme Objectives

• Understand the fundamentals of big data and its key components.
• Work with the tools and techniques used to collect and process big data.
• Analyse data and extract strategic insights from it.
• Explore how big data is applied across different sectors.
• Strengthen the ability to support data-driven decision-making.

Module 1: Why Big Data Is Reshaping How Organisations Compete

• Defining big data and its key characteristics: volume, velocity and variety
• Its role in organisational digital transformation
• How big data management has evolved, and where it's headed
• Current trends, challenges and opportunities

Module 2: The Infrastructure and Tools Behind Big Data

• The components of a big data architecture
• Traditional versus modern storage systems
• Data platforms such as Hadoop and Spark
• Batch and real-time processing tools, and choosing the right one for the task

Module 3: Storing and Managing Data at Scale

• Distributed storage methods and non-traditional (NoSQL) databases
• Optimising storage and processing performance
• Using cloud computing to scale storage and speed up analysis

Module 4: Preparing Data You Can Trust

• Techniques for cleaning and correcting data
• Handling missing data and outliers
• Preparing data so it's ready for effective analysis

Module 5: From Raw Data to Insight — Analytics and Visualization

• Descriptive and exploratory analysis techniques
• Statistical and programming tools for analysis
• Extracting key indicators and insights
• Visualising data through charts and dashboards to simplify decision-making

Module 6: Predictive Analytics, Machine Learning and AI

• Fundamentals of predictive analysis, and statistical and machine-learning models
• Applying predictive analytics to big data
• The role of AI in big data analytics, and common machine-learning models
• Integrating AI with predictive analytics, including deep learning techniques, with practical business examples

Module 7: Securing and Governing Data Responsibly

• Security challenges specific to big data
• Privacy policies and protecting personal data
• Tools and techniques for securing data
• Data governance concepts, quality standards and audit procedures that ensure reliability and accuracy

Module 8: Turning Data Into Everyday Business Decisions

• Analysing customer behaviour and improving internal organisational processes
• Supporting strategic decision-making with data
• Driving innovation through data-derived insights, and measuring their impact on organisational outcomes

Module 9: Big Data Across Industries — Marketing, Healthcare and Finance

• Using data in digital marketing, personalisation and measuring campaign effectiveness
• Improving healthcare services, disease monitoring and resource management with data
• Detecting fraud, improving customer experience and managing risk in finance

Module 10: Overcoming the Technical Challenges of Big Data

• Handling extremely large datasets
• Real-time data processing challenges
• Managing the complexity and diversity of big data sources

Module 11: Planning and Delivering Big Data Projects

• Designing and implementing big data projects
• Selecting the right tools and technologies for each project
• Measuring performance and achieving project objectives

Module 12: Where Big Data Is Heading Next

• Emerging tools and technologies in big data
• Continuous innovation and the challenges still ahead
• Preparing organisations to adapt as digital transformation reaches every sector