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

Course Outline

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