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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.

Book the course

How would you like to train?

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

Cloudera has not only prepared us for success today, but has also trained us to face and prevail over our big data challenges in the future.

Persado

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