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コース概要
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
要求
- Experience with data analysis
- Python programming experience
Audience
- Data scientists
- Software engineers
14 時間
お客様の声 (2)
私たちの分野に完璧に適合した例/演習
Luc - CS Group
コース - Scaling Data Analysis with Python and Dask
Machine Translated
私たちのプロジェクトで使用しているデータ(ラスター形式の衛星画像)とより類似したデータを使用して、より実践的な演習を行えること
Matthieu - CS Group
コース - Scaling Data Analysis with Python and Dask
Machine Translated