Get in Touch
 Duration 42 hours

Course Outline

Introduction to Big Data Ecosystems

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

Advanced Data Processing with Apache Spark

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

Machine Learning at Scale

  • Distributed model training methodologies
  • Hyperparameter tuning for large datasets
  • Deploying models in big data environments

Deep Learning for Big Data

  • Integration of TensorFlow and PyTorch with Spark
  • Constructing distributed deep learning pipelines
  • Applications in image, text, and time-series analysis

Real-Time Analytics and Data Streaming

  • Apache Kafka for ingesting streaming data
  • Stream processing frameworks
  • Real-time system monitoring and alerting

Data Governance, Security, and Ethics

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

Integrating Big Data with Business Intelligence

  • Visualisation and dashboarding for big data
  • Linking big data pipelines to BI tools
  • Driving business outcomes through advanced analytics

Summary and Next Steps

Requirements

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

Audience

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

Testimonials (3)

Upcoming Courses

Related Categories