• Machine Learning Engineers, Data Analysts, Business Analyst, Decision Makers and Data Scientists, Data Engineers, Python Developers, Analysts working with large datasets and anyone seeking to leverage data to drive strategic business outcomes.

• Proficiency in Python programming
• Basic knowledge of pandas, NumPy, and data manipulation concepts
• Familiarity with fundamental data science workflows.

Course Outline:

• Introduction to Dask
  • What is Dask? Why is it Important ?
    1. Overview of Dask and its applications in data science.
    2. Comparison with pandas, NumPy, and other Python tools..
  • Core Concepts and Architecture
    1. Dask collections: DataFrames, Arrays, Bags, and delayed objects.
    2. How Dask handles parallelism and memory management.

• Dask Data Frames and Arrays
  • Dask Data Frames
    1. Working with large datasets using Dask DataFrames.
    2. Key operations: filtering, aggregations, joins, and group by.

• Converting pandas workflows to Dask DataFram.

• Dask Arrays
  1. Handling larger-than-memory numerical datasets.
  2. Operations: slicing, reshaping, and computations.

• Advanced Features of Dask
  • Dask Bags
    1. Processing unstructured or semi-structured data.
    2. Use cases: JSON logs, text files, and more.
  • Task Scheduling and Optimization
    1. Understanding Dask’s task graph and scheduler.
    2. Visualizing and optimizing computations.
  • Dask with Machine Learning
    1. Integrating Dask with scikit-learn for distributed machine learning tasks.