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Learning IBM Watson Analytics

Book Description

Make the most advanced predictive analytical processes easy using Watson Analytics with this easy-to-follow practical guide

About This Book

  • This is the first and the only book on IBM Watson Analytics, and it shows you how to leverage Watson in an enterprise environment through rich use cases

  • Incorporate Watson Analytics into your business strategy and confidently add this cutting edge expertise to your resume

  • This book is written by James D Miller, IBM-certified expert and accomplished Director and Sr. Project Leader

  • Who This Book Is For

    If you want to perform data discovery and analysis and make sense of data you have, this book for you. Data scientists can also use this book to explore a new way to perform data analysis tasks on cloud with ease. This book does not require a programming background.

    What You Will Learn

  • Study the language of Watson while you discover how easy it is to access and configure

  • Review what a Watson use case is, why it’s important, and how to identify one

  • Design Watson Analytical solutions based upon your use cases

  • Understand the basic concepts behind the content analysis cycle and where Watson fits in

  • Explore all the features of Watson, such as Explore, Predict, and Assemble

  • Customize and extend your Watson solutions

  • Use Watson at the Enterprise level

  • Integrate Watson with other toolsets

  • In Detail

    Today, only a small portion of businesses actually use a real analytical tool as part of routine decision making. IBM Watson Analytics is changing that making the most advanced and predictive analytical techniques understandable and usable for any industry.

    This book will be the vital tour guide for your trip, starting with what IBM Watson Analytics is. We'll start off with introduction to Watson Analytics and then quickly move on to various use cases under which one can use the different analytics functionalities offered by Watson. During the course of the book, you will learn how to design solutions, and customize and extend Watson analytics. We will conclude by taking Watson Analytics to enterprise and integrating it with other solutions (other IBM solutions and analytics). Now is the time for you to learn IBM Watson to compete in the world.

    Style and approach

    Watson provides individuals with the ability to perform sophisticated data discovery and analysis without all of the complexity that usually goes along with it. This book will get you started with Watson analytics and how you can use it in day-to-day data analysis. The book introduces the key concepts and terminology and then uses practical use case examples to reinforce your understanding.

    Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.

    Table of Contents

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