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Course Outline
Introduction to Big Data Ecosystems
- Overview of big data technologies and architectures.
- Batch processing vs. real-time processing.
- Data storage strategies for scalability.
Advanced Data Processing with Apache Spark
- Optimizing Spark jobs for performance.
- Advanced transformations and actions.
- Working with structured streaming.
Machine Learning at Scale
- Distributed model training techniques.
- Hyperparameter tuning on large datasets.
- Model deployment in big data environments.
Deep Learning for Big Data
- Integrating TensorFlow and PyTorch with Spark.
- Distributed deep learning training pipelines.
- Use cases in image, text, and time-series analysis.
Real-Time Analytics and Data Streaming
- Apache Kafka for streaming data ingestion.
- Stream processing frameworks.
- Monitoring and alerting 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 strong understanding of data analysis and statistical modeling concepts.
- Experience with data processing tools and programming languages such as Python, R, or Scala.
- Familiarity with distributed computing frameworks such as Hadoop or Spark.
Audience
- Data scientists aiming to master large-scale data processing and predictive analytics.
- Senior analysts seeking to design and implement advanced analytical workflows.
- R&D professionals focusing on innovative data-driven solutions.
42 Hours
Testimonials (2)
Doing Exercise
Joe Pang - Lands Department, Hong Kong
Course - QGIS for Geographic Information System
Hands-on examples allowed us to get an actual feel for how the program works. Good explanations and integration of theoretical concepts and how they relate to practical applications.
