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Course Outline
Introduction
- 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
Troubleshooting
Summary and Next Steps
Requirements
- Experience with data analysis
- Python programming experience
Audience
- Data scientists
- Software engineers
14 Hours
Testimonials (3)
Dużo cierpliwości
Mateusz - WestWind Energy Polska Sp. z o.o.
Course - ArcGIS for Spatial Analysis
The trainer adapted the materials and contents to what he thought would be best for us and he succeeded. The quality of the training was excellent.
Jorge Sanchez Hernandez - CSMART - Carnival
Course - QGIS for Geographic Information System
I genuinely enjoyed the lots of labs and practices.