Overview

This course teaches data scientists how to use the data science capabilities of IBM Integrated Analytics System, using Watson Studio, RStudio, Spark, and in-database analytics. 

Audience

Data scientists, data miners, statisticians, researchers, business analysts performing statistical modeling

Prerequisites

• Familiarity with basic concepts in data science (machine learning models, scoring, deployment)
• Basic knowledge of notebooks
• Basic knowledge of Python and/or R

Objective

Unit 1 Introduction to IBM Integrated Analytics System 
• IIAS software overview 
• IIAS hardware overview 
• IIAS technologies overview 
• IIAS architecture overview 

Unit 2 Introduction to Watson Studio on IBM Integrated Analytics System 
• Explore the community 
• Identify the role of projects 
• Identify analytic assets 
• Identify environments 
• Identify jobs 
• Identify collaborators 

Unit 3 Work with notebooks 
• Work with notebooks 
• Load data into a notebook 
• Build a model 
• Save a model 
• Deploy a model 

Unit 4 Work with R and RStudio 
• Describe the RStudio component of IBM Integrated Analytics System 
• Describe the data science capabilities of the RStudio component 
• Use RStudio to create and deploy a model 

Unit 5 Optimize performance 
• In-database analytics versus in-application analytics 
• Explore in-database analytics using R and Python 
• Identify analytic stored procedures