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 Duration 42 hours

Course Outline

Foundations of 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 via Apache Spark

  • Performance optimization for Spark jobs
  • Utilizing advanced transformations and actions
  • Managing structured streaming workloads

Machine Learning at Scale

  • 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 and Data Streaming

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

Data Governance, Security, and Ethics

  • Navigating data privacy and compliance obligations
  • Implementing access control and encryption in big data systems
  • Ethical implications in large-scale analytics

Linking Big Data with Business Intelligence

  • Data visualization and dashboarding for big data
  • Bridging big data pipelines with BI tools
  • Driving business results through advanced analytics

Recap and Future Directions

Requirements

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

Target Audience

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

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