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 Duration 42 hours

Course Outline

Introduction to Big Data Ecosystems

  • Survey of big data technologies and architectural designs
  • Comparison of batch processing versus real-time processing
  • Data storage approaches for achieving scalability

Advanced Data Processing with Apache Spark

  • Performance optimization of Spark jobs
  • Advanced transformations and actions
  • Utilizing structured streaming

Machine Learning at Scale

  • Techniques for distributed model training
  • Hyperparameter tuning on large datasets
  • Deploying models in big data environments

Deep Learning for Big Data

  • Integrating 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
  • Stream processing frameworks
  • Monitoring and alerting in real-time systems

Data Governance, Security, and Ethics

  • Data privacy and regulatory compliance requirements
  • Access control and encryption within big data systems
  • Ethical implications in large-scale analytics

Integrating Big Data with Business Intelligence

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

Summary and Next Steps

Requirements

  • A robust understanding of data analysis and statistical modeling concepts
  • Proficiency with data processing tools and programming languages such as Python, R, or Scala
  • Knowledge of distributed computing frameworks like Hadoop or Spark

Target Audience

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

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