Get in Touch
 Duration 42 hours

Course Outline

Introduction to Big Data Ecosystems

  • Overview of big data technologies and underlying architectures
  • Comparing batch processing with real-time processing
  • Scalable data storage strategies

Advanced Data Processing with Apache Spark

  • Optimizing Spark jobs for peak performance
  • Advanced transformations and actions
  • Implementing structured streaming

Machine Learning at Scale

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

Deep Learning for Big Data

  • Integrating TensorFlow and PyTorch with Spark
  • 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
  • Stream processing frameworks
  • Monitoring and alerting mechanisms in real-time systems

Data Governance, Security, and Ethics

  • Data privacy and compliance requirements
  • 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 with advanced analytics

Summary and Next Steps

Requirements

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

Audience

  • Data scientists seeking to excel in large-scale data processing and predictive analytics.
  • Senior analysts looking to design and execute sophisticated analytical workflows.
  • R&D professionals dedicated to developing innovative, data-driven solutions.

Number of participants


Price per participant

Testimonials (3)

Upcoming Courses

Related Categories