Fundamentals of Watson Analytics

Explore Watson Analytics, a cloud-based cognitive analytics tool that uses AI, machine learning, and natural language dialogue to uncover unbiased insights from structured data with an intuitive, automated workflow.

Compare free, plus, and professional Watson Analytics versions, noting core capabilities—data discovery, predictive modeling, and visualization—plus storage, connectors, data shaping, display tools, and user management options.

Open Watson Analytics, load and refine data, discover insights, and display discoveries in interactive dashboards—the three d's of the analytics workflow.

Explore the Watson Analytics account settings and overview, including user info, subscription details, data connections, secured gateways, and help options with getting started tutorials.

Learn to structure raw data for Watson Analytics with a rectangular table, variables as columns and observations as rows, ensuring consistent data types and a single raw table, readable headers.

Explore Watson Analytics add-ons for social media, a cloud based self-service tool on a monthly subscription with a ten day trial, plus analytics exchange offering data sets and expert storybooks.

Identify the drivers of customer satisfaction using Watson Analytics on a 130,000-response airline survey, analyzing flight data, demographics, and behaviors to profile high vs. low satisfaction and report findings.

Import the airline satisfaction survey data into Watson Analytics from a CSV or database, then use Dataworks to shape data, improve data quality, and define calculated fields, groups, and hierarchies.

Learn to create calculations, build hierarchies and groups in Watson Analytics, drill path with destination state/city and origin state/city hierarchies, compute total travel time, and categorize shopping spend levels.

Discover cognitive starting points to reveal price sensitivity and insights from airline satisfaction data set by creating visualizations in the discovery set, and drill down by origin state to city.

Explore Watson Analytics' natural language queries to compare average satisfaction by airline name using bar charts and other visuals, with the how to ask a question interface guiding syntax.

Create customized visualizations in Watson Analytics by building a combination chart, dragging fields into data trays to compare satisfaction by status and gender and reveal insights.

Explore how key drivers influence satisfaction through a built-in linear regression model, using spiral charts and heatmaps to drill into travel type, age, gender, and airline.

Use predictive models to generate decision rules and decision trees that reveal customer profiles associated with high or low satisfaction, based on travel type, airline status, and delays.

Explore the display interface to build the airline satisfaction dashboard using discoveries, widgets, and filters. Create a multi-tab dashboard with four equal sections, using global, tab, and visualization filters.

Learn to assemble a flexible multi-tabbed dashboard in Watson Analytics by configuring the discoveries pane visuals, applying global and local filters, and creating tabs for key insights, drivers, and profiles.

Create a line chart of satisfaction by age and status for female travelers, import from discoveries, color by status, customize lines as smooth with triangles, and add to the dashboard.

Save and manage your Watson Analytics dashboard, switch between view and edit modes, share via email or links in image, PDF, or PowerPoint formats, and move to a shared folder.