Mastering Dask: Scale Python Workflows Like a Pro

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