The British Academy for Training and Development offers this training program in Big Data in response to the growing need to manage and analyze massive volumes of data across various sectors. Handling big data presents both a challenge and an opportunity, enabling organizations to uncover valuable insights that support smarter and more strategic decision-making.

The program focuses on providing theoretical and practical knowledge on how to collect, process, and analyze big data using the latest tools and technologies, as well as understanding supporting techniques such as artificial intelligence and predictive analytics. It also addresses the importance of big data in enhancing organizational performance, fostering innovation, and increasing competitiveness in a dynamic and rapidly 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 fields.

Knowledge and Benefits:

After completing the program, participants will be able to master the following:

• Understand the fundamentals of big data and its key components.

• Become familiar with tools and techniques for collecting and processing big data.

• Develop the ability to analyze data and extract strategic insights.

• Learn applications of big data across various sectors.

• Enhance the ability to support data-driven decision-making.

• Introduction to Big Data

  • Definition and significance of big data.

  • Characteristics of big data (Volume, Velocity, Variety).

  • The role of big data in organizational digital transformation.

• Evolution of Big Data

  • History and development stages of big data.

  • Current trends in data management.

  • Challenges and opportunities of big data.

• Big Data Infrastructure

  • Components of big data architecture.

  • Traditional vs. modern storage systems.

  • Data management platforms such as Hadoop and Spark.

• Data Processing Tools

  • Batch and real-time processing tools.

  • Comparison of different data processing tools.

  • Selecting the appropriate tools for different tasks.

• Big Data Storage and Management

  • Distributed storage methods.

  • Non-traditional databases (NoSQL).

  • Optimizing storage and data processing performance.

• Data Cleaning and Preparation

  • Techniques for cleaning and correcting data.

  • Handling missing data and outliers.

  • Preparing data for effective analysis.

• Big Data Analytics

  • Descriptive and exploratory analysis techniques.

  • Statistical and programming analysis tools.

  • Extracting key indicators and insights.

• Data Visualization

  • Techniques for visual representation of data.

  • Using charts and dashboards effectively.

  • Simplifying complex data for decision-making.

• Predictive Analytics

  • Fundamentals of predictive analysis.

  • Statistical models and machine learning techniques.

  • Applying predictive analytics to big data.

• Machine Learning and AI

  • Role of AI in big data analytics.

  • Common machine learning models.

  • Enhancing organizational performance using AI.

• Data Security and Privacy

  • Security challenges in big data.

  • Privacy policies and protecting personal data.

  • Tools and techniques for securing data.

• Data Governance and Quality Management

  • Concepts of data governance.

  • Data quality standards and audit procedures.

  • Ensuring data reliability and accuracy.

• Practical Applications of Big Data

  • Analyzing customer behavior.

  • Improving internal organizational processes.

  • Supporting strategic decision-making.

• Big Data in Marketing and Business

  • Using data in digital marketing strategies.

  • Personalization and targeted campaigns.

  • Measuring strategy effectiveness through data.

• Big Data in Healthcare

  • Enhancing healthcare services using data.

  • Disease monitoring and data-driven decision-making.

  • Efficient management of healthcare resources.

• Big Data in Finance

  • Fraud detection and risk management.

  • Improving customer financial experiences.

  • Managing financial risks using data insights.

• Technical Challenges of Big Data

  • Handling extremely large datasets.

  • Real-time data processing challenges.

  • Overcoming complexity and diversity issues.

• Advanced Big Data Frameworks and Techniques

  • Introduction to Hadoop, Spark, and NoSQL.

  • Utilizing cloud computing technologies.

  • Optimizing performance and enabling fast analysis.

• AI and Big Data Analytics

  • Integrating AI with predictive analytics.

  • Deep learning techniques for big data analysis.

  • Practical examples of improving business operations.

• Innovation and Data-Driven Decision Making

  • Driving innovation through insights derived from data.

  • Making strategic decisions based on analytics.

  • Measuring the impact of big data on organizational outcomes.

• Planning Big Data Projects

  • Designing and implementing big data projects.

  • Selecting appropriate tools and technologies for each project.

  • Measuring performance and achieving project objectives.

• Future Trends in Big Data

  • Emerging tools and technologies in big data.

  • Continuous innovation and future challenges.

  • Preparing to adapt to digital transformation across sectors.