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IBM Integrated Analytics System (IIAS) for Data Scientists (V1.0)
Course Description
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.
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
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
Topics
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
Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
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Self-Paced Training Terms & Conditions
IBM Web Based Training courses are sold on a per-user basis. WBT courses provide a training advantage for you and your teams, helping you get up to speed quickly. Take the courses you need, at your convenience and at your own pace.
You can start the course at any time within 12 months of enrolling for the course. After you register/start the course, you have 30 days to complete your course. Within this 30 days, the self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day.
System Requirements
To participate in this course, the student workstation must meet the following hardware requirements:- Minimum of 256 MB of memory
- Windows Vista or Windows 7-10 (32-bit or 64-bit edition)
- Internet Explorer 6 or higher or Firefox ESR
- 128-bit encryption
- Citrix Receiver for connection to the IBM Remote Lab Platform
- Java
- Access to Internet with at least 128 kbps down and 128 kbps up
- Other platforms/combinations may work but are not officially supported by IBM.
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