Learn Dask arrays, dataframes & streaming with scikit-learn integration, real-time dashboards etc.
Sub Category
- Programming Languages
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Objectives
- Master Dask's core data structures: arrays, dataframes, bags, and delayed computations for parallel processing
- Build scalable ETL pipelines handling massive CSV, Parquet, JSON, and HDF5 datasets beyond memory limits
- Integrate Dask with scikit-learn for distributed machine learning and hyperparameter tuning at scale
- Develop real-time streaming applications using Dask Streams, Streamz, and RabbitMQ integration
- Optimize performance through partitioning strategies, lazy evaluation, and Dask dashboard monitoring
- Create production-ready parallel computing solutions for enterprise-scale data processing workflows
- Build interactive real-time dashboards processing live cryptocurrency and stock market data streams
- Deploy Dask clusters locally and in cloud environments for distributed computing applications
Pre Requisites
- Basic Python programming knowledge (variables, functions, loops, data structures)
- Familiarity with Pandas for data manipulation and NumPy for array operations
- Understanding of fundamental data science concepts and workflow processes
- No prior experience with parallel computing or distributed systems required - we'll cover everything from scratch
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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