Course Outline


  • Overview of Dask features and advantages
  • Parallel computing in Python

Getting Started

  • Installing Dask
  • Dask libraries, components, and APIs
  • Best practices and tips

Scaling NumPy, SciPy, and Pandas

  • Dask arrays examples and use cases
  • Chunks and blocked algorithms
  • Overlapping computations
  • SciPy stats and LinearOperator
  • Numpy slicing and assignment
  • DataFrames and Pandas

Dask Internals and Graphical UI

  • Supported interfaces
  • Scheduler and diagnostics
  • Analyzing performance
  • Graph computation

Optimizing and Deploying Dask

  • Setting up adaptive deployments
  • Connecting to remote data
  • Debugging parallel programs
  • Deploying Dask clusters
  • Working with GPUs
  • Deploying Dask on cloud environments


Summary and Next Steps


  • Experience with data analysis
  • Python programming experience


  • Data scientists
  • Software engineers
  14 Hours


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