Course Overview
- Course Title: Mastering Dask: Scale Python Workflows Like a Pro
- Instructor: Start-Tech Trainings
- Target Audience: Data analysts, Python enthusiasts, data engineers, or anyone working with large datasets
- Prerequisites: A PC with Python and Jupyter Notebook installed, basic understanding of Python and data handling is helpful but not required
Curriculum Highlights
- Key Topics Covered:
- Understanding and implementing parallel computing concepts using Dask in Python
- Working with large datasets using Dask DataFrames for scalable data manipulation
- Performing advanced numerical computations using Dask Arrays and lazy evaluation
- Building and optimizing machine learning workflows with Dask-ML and joblib integration
- Using Dask schedulers effectively for performance tuning and distributed computing
- Profiling performance, handling memory spilling, and applying best practices with Dask
- Practicing with real-world datasets like flight delays to build scalable ML models
- Key Skills Learned:
- Scalable parallel computing
- Efficient data wrangling and transformation
- Parallel numerical computations
- Managing parallelism effectively
- Building scalable machine learning workflows
- Profiling computations and optimizing memory usage
Course Format
- Duration: 2.5 hours on-demand video
- Format: Self-paced online course
- Resources:
- 3 articles
- 1 downloadable resource
- Access on mobile and TV
- Certificate of completion