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CDSW - CLOUDERA DATA SCIENCE WORKBENCH TRAINING

Course overview and objectives

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Duration

8 hours

Overview

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

Course objectives

By the end of this course, you will be able to:

  • Navigate CDSW’s web user interface
  • Create projects in CDSW and edit them according to your needs
  • Edit and run Python code using the built-in editor, JupyterLabs, or a third-party editor
  • Load and transform data in CDSW using popular Python or R package, and using Apache Spark
  • Work with large-scale data using Apache Spark with PySpark
  • Schedule workloads and implement a job dependency chain
  • Implement a machine learning workflow, starting with analyzing and visualizing data, then training and testing a model
  • Measure and track versions of a model using CDSW's Experiments capability
  • Deploy models as REST API endpoints serving predictions using CDSW’s Models capability
  • Create custom applications and dashboards that can be shared in and outside your team
  • Work collaboratively using CDSW together with Git

Audience and Prerequisites

This course is designed for learners at organizations using CDSW under a Cloudera Enterprise license or a trial license. The learner must have access to a CDSW environment on a Cloudera 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 Details

Overview of CDSW

  • Introduction to CDSW
  • How to Access CDSW
  • Navigating around CDSW
  • User Settings
  • Hadoop Authentication

Projects in CDSW

  • Creating a New Project
  • Navigating around a Project
  • Project Settings

The CDSW Workbench Interface

  • Using the Workbench
  • Using the Sidebar
  • Using the Code Editor
  • Engines and Sessions

Running Python and R Code in CDSW

  • Running Code
  • Using the Session Prompt
  • Using the Terminal
  • Installing Packages
  • Using Markdown in Comments

Using Apache Spark 2 in CDSW

  • Scenario and Dataset
  • Copying Files to HDFS
  • Interfaces to Apache Spark 2
  • Connecting to Spark
  • Reading Data
  • Inspecting Data

Exploratory Data Science in CDSW

  • Transforming Data
  • Using SQL Queries
  • Visualizing Data from Spark
  • Machine Learning with MLlib
  • Session History

Teams and Collaboration in CDSW

  • Collaboration in CDSW
  • Teams in CDSW
  • Using Git for Collaboration
  • Conclusion

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