Scaling Data Analysis with Python and Dask Training Course
Dask is a versatile and high-performance Python library designed for parallel computing. It enhances and accelerates big data processing in conjunction with other Python-based data science libraries such as Pandas, Numpy, and Scikit-Learn.
This instructor-led, live training (available both online and onsite) is tailored for data scientists and software engineers who aim to utilize Dask within the Python ecosystem to build, scale, and analyze large datasets.
By the end of this training, participants will be able to:
- Set up the environment necessary to start building big data processing systems with Dask and Python.
- Explore the features, libraries, tools, and APIs provided by Dask.
- Understand how Dask accelerates parallel computing within Python.
- Learn how to scale the Python ecosystem (including Numpy, SciPy, and Pandas) using Dask.
- Optimize the Dask environment to ensure high performance in handling large datasets.
Format of the Course
- Interactive lectures and discussions.
- Plenty of exercises and practice sessions.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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
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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)
Matthieu - CS Group
Course - Scaling Data Analysis with Python and Dask
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