Course Outline

Introduction

  • Overview of Dask features and advantages for government use
  • Parallel computing in Python for government applications

Getting Started

  • Installing Dask for government systems
  • Dask libraries, components, and APIs for government workflows
  • Best practices and tips for government users

Scaling NumPy, SciPy, and Pandas for Government

  • Dask arrays examples and use cases for government data processing
  • Chunks and blocked algorithms in government datasets
  • Overlapping computations for efficient government operations
  • SciPy stats and LinearOperator for government analytics
  • Numpy slicing and assignment for government data manipulation
  • DataFrames and Pandas for government data management

Dask Internals and Graphical UI for Government

  • Supported interfaces for government platforms
  • Scheduler and diagnostics for government systems
  • Analyzing performance in government applications
  • Graph computation for government workflows

Optimizing and Deploying Dask for Government

  • Setting up adaptive deployments for government projects
  • Connecting to remote data for government use
  • Debugging parallel programs in government environments
  • Deploying Dask clusters for government operations
  • Working with GPUs for government computing tasks
  • Deploying Dask on cloud environments for government needs

Troubleshooting

Summary and Next Steps

Requirements

  • Proficiency in data analysis
  • Experience with Python programming

Audience for Government

  • Data scientists
  • Software engineers
 14 Hours

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