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Cloudera Data Science Workbench Training

Course outline

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Duration

On-demand

Overview

Cloudera Data Science Workbench Training prepares learners to complete data science and machine learning projects using Cloudera Data Science Workbench (CDSW).

Get Hands-On Experience

Through narrated demonstrations and hands-on exercises, learners achieve proficiency in CDSW and develop the skills required to:

  • Navigate CDSW’s options and interfaces with confidence
  • Create projects in CDSW and collaborate securely with other users and teams
  • Develop and run reproducible Python and R code
  • Customize projects by installing packages and setting environment variables
  • Connect to a secure (Kerberized) Cloudera or Hortonworks cluster
  • Work with large-scale data using Apache Spark 2 with PySpark and sparklyr
  • Perform end-to-end machine learning workflows in CDSW using Python or R (read, inspect, transform, visualize, and model data)
  • Measure, track, and compare machine learning models using CDSW’s Experiments capability
  • Deploy models as REST API endpoints serving predictions using CDSW’s Models capability
  • Work collaboratively using CDSW together with Git

What to Expect

This OnDemand course is designed for learners at organizations using CDSW under a trial license or a commercial license. The learner must have access to a CDSW environment on a Cloudera or Hortonworks cluster running Apache Spark 2. Some experience with data science using Python or R is helpful but not required. No prior knowledge of Spark or other Hadoop ecosystem tools is required.

Course Contents

  • 1. Introduction to The Course
  • 2. Overview of CDSW
  • Introduction to Cloudera Data Science Workbench
  • Who Can Use CDSW
  • How to Access CDSW
  • Navigating around CDSW
  • User Settings
  • Hadoop Authentication
  • 3. Projects in CDSW
  • Creating a New Project
  • Navigating around a Project
  • Project Settings
  • 4. The CDSW Workbench Interface
  • The Workbench Interface
  • Using the Sidebar
  • Using the Code Editor
  • Engines and Sessions
  • 5. Running Python and R Code in CDSW
  • Running Code
  • Using the Session Prompt
  • Using the Terminal
  • Installing Packages
  • Using Markdown in Comments
  • 6. Using Apache Spark 2 in CDSW
  • Scenario and Dataset
  • Copying Files to HDFS
  • Introducing PySpark (Python track)
  • Introducing sparklyr (R track)
  • Connecting to Spark
  • Reading Data
  • Inspecting Data
  • 7. Data Science and Machine Learning in CDSW
  • Transforming Data (Python track)
  • Transforming Data Using dplyr Verbs (R track)
  • Using SQL Queries
  • Spark DataFrames Functions (R track)
  • Visualizing Data from Spark
  • Machine Learning with MLlib
  • Session History
  • 8. Experiments and Models in CDSW
  • Machine Learning Workflow
  • Running Experiments
  • Using Packages in Experiments
  • Deploying Models
  • Calling Models
  • Using Packages in Models
  • 9. Teams and Collaboration in CDSW
  • Collaboration in CDSW
  • Teams in CDSW
  • Cloning a Git Repository with SSH
  • Using Git for Collaboration
  • 10. What’s New in CDSW 1.6
  • Changes to the User Interface
  • Using Third-Party Editors
  • Desktop-Based Third-Party Editors
  • 11. Conclusion

Programme scope, delivery arrangements and suitability are confirmed during enquiry.