Dask: Data Scientist’s Power Tool

Description:

This course will provide a broad idea of a powerful Python library for parallel and distributed computing. It is tailored to help data scientists and engineers efficiently process and analyse large datasets. Participants will learn to leverage Dask’s capabilities to scale their pandas, NumPy, and machine learning workflows, enabling them to handle datasets that are larger than memory and optimize their computation processes.

The course emphasizes practical, hands-on learning, covering core Dask features, task scheduling, and integration with popular Python libraries. By the end of the course, participants will have the confidence to integrate Dask into their workflows for scalable data processing and analysis.

For Certification based Assistance and Mock quizzes please visit: https://certify360.ai/

Duration: 1 Days

Course Code: BDT414

Learning Objectives:

After completing this course, participants will be able to:

• Understand the core components and architecture of Dask.
• Utilize Dask Data Frames, Arrays, and Bags for scalable data processing.
• Perform parallel and distributed computations efficiently.
• Optimize workflows using Dask’s dynamic task scheduler.
• Integrate Dask with libraries like pandas, NumPy, and scikit-learn.
• Set up and manage distributed computing clusters for large-scale computations.

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

• 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.

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.

• Distributed Computing with Dask
  • Setting Up Distributed Computing Clusters
    1. Local clusters vs. distributed clusters.
    2. Connecting to cloud-based or on-premise clusters.
  • Using Dask’s Distributed Scheduler
    1. Scaling computations across multiple nodes.
    2. Monitoring tasks with the Dask dashboard.

• Real-World Applications and Best Practices
  • End-to-End Workflow with Dask
    1. Building a complete pipeline: from data ingestion to analysis.
    2. Case studies: data preprocessing, exploratory analysis, and modeling at scale.
  • Debugging and Performance Tuning
    1. Common pitfalls and how to avoid them.
    2. Tips for improving Dask performance.
  • Project, Hands-on And Assessment
    1. Capstone Project
    2. Hands-on project to apply learned skills

Training material provided: Yes (Digital format, including presentations, datasets, and sample codes)

Any Additional Information

Participants are encouraged to bring sample datasets or use provided data for practical exercises. The course will use tools like Jupyter Notebooks for demonstrations and require installation of Dask and related libraries. Setup instructions will be shared prior to the course.