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 Duration 42 hours

Course Outline

Introduction to Big Data Ecosystems

  • Survey of big data technologies and architectural designs
  • Comparing batch processing with real-time processing
  • Strategies for scalable data storage

Advanced Data Processing with Apache Spark

  • Tuning Spark jobs for optimal performance
  • Complex transformations and actions
  • Handling structured streaming

Machine Learning at Scale

  • Techniques for distributed model training
  • Hyperparameter tuning across large datasets
  • Deploying models within big data environments

Deep Learning for Big Data

  • Combining TensorFlow and PyTorch with Spark
  • Pipelines for distributed deep learning training
  • Applications in image, text, and time-series analysis

Real-Time Analytics and Data Streaming

  • Using Apache Kafka for streaming data ingestion
  • Frameworks for stream processing
  • Monitoring and alerting mechanisms in real-time systems

Data Governance, Security, and Ethics

  • Compliance and data privacy requirements
  • Implementing access control and encryption in big data systems
  • Ethical implications of large-scale analytics

Integrating Big Data with Business Intelligence

  • Data visualization and dashboarding strategies for big data
  • Linking big data pipelines to BI tools
  • Achieving business goals through advanced analytics

Summary and Next Steps

Requirements

  • A solid grasp of data analysis and statistical modeling principles
  • Proficiency with data processing tools and programming languages including Python, R, or Scala
  • Knowledge of distributed computing frameworks such as Hadoop or Spark

Target Audience

  • Data scientists looking to excel in large-scale data processing and predictive analytics
  • Senior analysts interested in building and executing advanced analytical workflows
  • R&D specialists focused on developing innovative, data-driven solutions

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