Course Outline


  • Overview of Dask features and advantages
  • Parallel computing in Python

Getting Started

  • Installing Dask
  • Dask libraries, components, and APIs
  • Best practices and tips

Scaling NumPy, SciPy, and Pandas

  • Dask arrays examples and use cases
  • Chunks and blocked algorithms
  • Overlapping computations
  • SciPy stats and LinearOperator
  • Numpy slicing and assignment
  • DataFrames and Pandas

Dask Internals and Graphical UI

  • Supported interfaces
  • Scheduler and diagnostics
  • Analyzing performance
  • Graph computation

Optimizing and Deploying Dask

  • Setting up adaptive deployments
  • Connecting to remote data
  • Debugging parallel programs
  • Deploying Dask clusters
  • Working with GPUs
  • Deploying Dask on cloud environments


Summary and Next Steps


  • Experience with data analysis
  • Python programming experience


  • Data scientists
  • Software engineers
 14 Hours

Number of participants

Price per participant

Testimonials (2)

Related Courses

ArcGIS for Spatial Analysis

14 Hours

ArcMap in ArcGIS

14 Hours

ArcGIS Pro for Spatial Analysis

14 Hours

ArcGIS with Python Scripting

14 Hours

QGIS for Geographic Information System

21 Hours

Advanced Data Analysis with TIBCO Spotfire

14 Hours

Introduction to Spotfire

14 Hours

AI-Driven Data Analysis with TIBCO Spotfire X

14 Hours

Data Analysis with SQL, Python and Spotfire

14 Hours

Sensu: Beginner to Advanced

14 Hours

Monitoring Your Resources with Munin

7 Hours

Automated Monitoring with Zabbix

14 Hours

Fluentd for Log Data Unification

14 Hours

Nagios Certified Administrator Preparation

21 Hours

Advanced Nagios

21 Hours

Related Categories