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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
Testimonials (2)
Examples/exercices perfectly adapted to our domain
Luc - CS Group
Course - Scaling Data Analysis with Python and Dask
The fact of having more practical exercises using more similar data to what we use in our projects (satellite images in raster format)