IBM Integrated Analytics System (IIAS) for Data Scientists (V1.0) eLearning – 1W710G WBT

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Course Code: 1W710G Brand: HDM – IBM Integrated Analytics Systems Category: Analytics Skill Level: Intermediate Duration: 5H Modality: WBT     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

  Short Summary

This course teaches data scientists how to use the data science capabilities of IBM Integrated Analytics System.   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. 

  Topic

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

  Objectives

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

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