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Learning IBM Watson Analytics
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Learning IBM Watson Analytics
by James D Miller
Learning IBM Watson Analytics
Learning IBM Watson Analytics
Table of Contents
Learning IBM Watson Analytics
Credits
About the Author
About the Reviewer
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Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Errata
Piracy
Questions
1. A Quick Start
Step by step
Signing up
Logging in
The welcome page
Things to know
Your account
Upgrading
Learning more
The shortcut panel bar
Explore, Predict, Assemble, and Refine
The content analytics architecture
The main components
Crawlers
Document processors
Indexers
Search engine
Miner (content analytics)
Administration console
The flow of data
Exiting the flow
Deep inspection
Important concepts and terminologies
Structured versus unstructured
Text analytics
Searching
Discovery
Mining
Collections
Facets
Frequency
Correlation
Deviation
Generally good advice
Hints
Join in
Summary
2. Identifying Use Cases
Defining a use case
Importance of use cases
Working with Watson
What to ask of your data
Building questions
Putting data into context
Importance of data context
Use case examples
NFL stadium sales
Profitable slot machines
Quality
Refining data
Viewing metrics
More questions
Crime recording
Context
Sharing an insight
Saving your work
Summary
3. Designing Solutions with Watson Analytics
Data considerations
The Content Analytics data model
A relational mindset
Structured and unstructured sources
Data categorized
Multiple data sources
Date-sensitive data
Extracting information from textual data
Multiple collections
Building collections
The collection process – step by step
Adding to collections from assemble
Planning for iteration
Programming interfaces
Programming with Watson Analytics
Summary
4. Understanding Content Analysis
Basic concepts of Content Analytics
Manual or automation
Difficulties with textual analysis
Frequency and deviation
Precision and recall
Cycle of analysis with Watson Analytics
Defining a purpose
Obtaining the data
Performing the analysis
Determining actions to take
Validation
A sample use case
Step 1: Define the purpose
Step 2: Obtaining the data
Step 3: Performing the analysis
Step 4: Determining actions to take
Step 5: Validation
Text data
Data metrics
Search and Filter
Summary
5. Watson Analytics Predict and Assemble
Predict
Creating a Watson Analytics prediction
Viewing the results of a prediction
Predictor visualization bar
Main Insights
Details
Customization
Assemble
Views
Dashboards
Using templates
A simple use case
Some points of interest
Versioning
Assemble
Summary
6. Customizing and Extending
Meeting the requirements
Reasons to customize or extend
Customizing Watson
Subscriptions
Data
Changing column types
Custom reaggregation
Customizing column names
Persistence
Views
Changing themes and presentation styles
Changing properties
Changing the media
Tabs, grouping, and new data
Extending Watson
Data quality
Watson data metrics
Using IBM SPSS
Handling missing values
An example use case
Summary
7. Taking It to the Enterprise
Introducing an enterprise perspective
Definition of Watson knowledge
Data interpretation
Classification or grouping of data
Data enrichment
Normalization and modeling
Collections
Watson object management
Naming for documentation
Developing the naming conventions
Organized naming conventions
Object naming conventions
Hints
Testing
Test before sharing
The enterprise vision
Evaluation and experimentation
Predicting and assembling
Management and optimization
More on the vision
Enterprise Watson roadmap
Upgrading Watson
The free version
The personal version
Professional
Next steps
An enterprise use case
Enterprising suggestions
Gather the files
Modeler streaming
Upload to Watson
Predicting with Watson
Watson versions
Summary
8. Adding Value with Integration
Upgrading to Watson Professional
Watson Professional
Available space
Administration – Account, Users, and Data connections
Upgrade – related products
Docs
Conversations
Starting a conversation
Polls
Folders
Adding data source connections
Select a source
Or select a connection created for you
Creating connections
A sample connection – Microsoft SQL server
Twitter
IBM Cognos BI
A Cognos data connection
The integration steps
More learning opportunities
Using you own data
Available references and material
Summary
Index
Search in book...
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Learning IBM Watson Analytics
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