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Course Outline

Introduction to Big Data Ecosystems

  • Overview of big data technologies and architectures
  • Comparison between batch processing and real-time processing
  • Strategies for scalable data storage

Advanced Data Processing with Apache Spark

  • Optimizing Spark jobs for peak performance
  • Utilizing advanced transformations and actions
  • Managing structured streaming

Machine Learning at Scale

  • Techniques for distributed model training
  • Tuning hyperparameters on large 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

  • Using Apache Kafka for streaming data ingestion
  • Implementing stream processing frameworks
  • Monitoring and alerting within real-time systems

Data Governance, Security, and Ethics

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

Integrating Big Data with Business Intelligence

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

Summary and Next Steps

Requirements

  • A solid grasp 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 seeking to master large-scale data processing and predictive analytics
  • Senior analysts looking to design and implement advanced analytical workflows
  • R&D professionals focused on developing innovative data-driven solutions
 42 Hours

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