Overview:

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

Delivery Method and Course Duration:

OnDemand: 180 days

Objectives:

Through narrated demonstrations and hands-on exercises, learners gain familiarity with 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 cluster
• Work with large-scale data using Apache Spark 2 with PySpark and sparklyr
• Perform full exploratory data science and machine learning workflows in CDSW using Python or R—read, inspect, transform, visualize, and model data
• Work collaboratively using CDSW together with Git

What To Expect:

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 outlines:

1.      Overview of CDSW

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

2.      Projects in CDSW

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

3.      The CDSW Workbench Interface

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

4.      Running Python and R Code in CDSW

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

5.      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

6.      Exploratory Data Science in CDSW

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

7.      Teams and Collaboration in CDSW

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

8.      Conclusion