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Learning IBM Watson analytics : make the most advanced predictive analytical processes easy using Watson analytics with this easy-to-follow practical guide /
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 Incorpo...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
[2016]
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| Colección: | Professional expertise distilled.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 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 analyticsSearching; Discovery; Mining; Collections; Facets; Frequency; Correlation; Deviation; Generally good advice; Hints; Join in; Summary; Chapter 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; Summary; Chapter 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 dataExtracting 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; Chapter 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
- ValidationA 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; Chapter 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; Chapter 6: Customizing and Extending
- Meeting the requirementsReasons 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; Chapter 7: Taking It to the Enterprise; Introducing an enterprise perspective; Definition of Watson knowledge; Data interpretation; Classification or grouping of data