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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 in data analysis.
- Programming proficiency in Python.
Audience
- Data scientists.
- Software engineers.
14 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 2600 € + VAT*
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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)
