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 Duration 42 hours

Course Outline

Introduction to Big Data Ecosystems

  • Survey of big data technologies and architectural models
  • Comparison of batch processing versus real-time processing
  • Strategies for scalable data storage

Advanced Data Processing with Apache Spark

  • Techniques for optimizing Spark job performance
  • Advanced transformations and actions
  • Managing structured streaming workloads

Machine Learning at Scale

  • Methods for distributed model training
  • Hyperparameter tuning for large-scale datasets
  • Deploying models within big data environments

Deep Learning for Big Data

  • Integration of TensorFlow and PyTorch with Spark
  • Building distributed deep learning training pipelines
  • Applications in image, text, and time-series analysis

Real-Time Analytics and Data Streaming

  • Utilizing Apache Kafka for streaming data ingestion
  • Overview of stream processing frameworks
  • Monitoring and alerting mechanisms for real-time systems

Data Governance, Security, and Ethics

  • Data privacy standards and compliance requirements
  • Access control and encryption protocols in big data systems
  • Ethical considerations in large-scale analytics

Integrating Big Data with Business Intelligence

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

Conclusion and Future Steps

Requirements

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

Target Audience

  • Data scientists seeking to excel in large-scale data processing and predictive analytics
  • Senior analysts looking to create and deploy sophisticated analytical workflows
  • R&D experts dedicated to developing innovative data-driven solutions

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