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Course Outline

Foundations of Big Data Ecosystems

  • Survey of big data technologies and architectural models
  • Comparing batch processing with real-time processing
  • Data storage strategies optimized for scalability

Advanced Data Processing Using Apache Spark

  • Optimizing Spark job performance
  • Executing complex transformations and actions
  • Managing structured streaming workflows

Scaling Machine Learning

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

Deep Learning for Big Data

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

Real-Time Analytics & Data Streaming

  • Utilizing Apache Kafka for streaming data ingestion
  • Exploring stream processing frameworks
  • Implementing monitoring and alerting in real-time systems

Data Governance, Security & Ethics

  • Addressing data privacy and compliance requirements
  • Managing access control and encryption in big data systems
  • Ethical considerations in large-scale analytics

Aligning Big Data with Business Intelligence

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

Wrap-Up and Future Directions

Requirements

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

Target Audience

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

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