Oracle Analytics Data Visualization (DV) Mastery

Install and configure Oracle analytics server 6.4 by installing Java development kits, setting JAVA_HOME, and verifying Java, then deploy OAS binaries and create the RCU repository for project-domain setup.

Set the java_home environment variable on Windows by defining java_home, entering the jdk path, and verifying the installation with a command-line check that shows the java version.

Install Oracle Fusion Middleware infrastructure by setting up Java home and JDK, then running Oracle Universal Installer. The video covers prerequisites checks, installation steps, and locating WebLogic middleware home.

Install OAS binaries into the existing goproject middleware home with the Oracle analytics installer, using Java JDK and Visual C++ components. The next video covers creating the registry with RCU.

Learn to run the repository creation utility (RCU) to create required schemas for Oracle Analytics, including configuring the database connection and creating div_stb, div_ops, and div_VMS schemas.

Configure Oracle analytics server into a WebLogic domain by running the configuration assistant, selecting analytics components, defining domain, credentials, schema, and ports, and verify services via Enterprise Manager.

Install and configure the Oracle client tool to connect Oracle BI server via ODBC. Open the RPD online and offline, using OBIS1 service ports for BI connectivity.

Install Oracle Linux desktop on Windows with administrator privileges, download from Oracle, extract, run the installer, and launch to create datasets, workbooks, reports, and dashboards.

install dvml data visualization machine learning on windows by locating the installer in the start menu, running as administrator, and completing the download and setup.

Log into Oracle Analytics Desktop, Server, and Cloud to navigate the home page for projects, datasets, and workbooks and customize the adaptive data visualization home page.

Create and manage datasets in Oracle analytics dv mastery by importing xlsx, csv, or txt files. Enrich and augment data, apply recommendations, profile columns, and build visualizations from projects.

Master data visualization workflows by editing datasets with preparation scripts that capture transformations, enable versioning, search and modify steps, and ensure data consistency and transparency.

Discover data profiling in Oracle Analytics to understand data quality before analysis. Identify missing values, duplicates, and inconsistencies, analyze distributions and patterns, and prepare reliable data for reporting and visualization.

Explore Oracle Analytics' Recommendation Augmented Enrichment, a machine learning-driven feature that suggests data transformations to enrich data and speed preparation for high-quality analysis and reporting.

Master column formatting attributes to control how attribute and measure columns appear in visualizations, with data types, aggregation, and number formats.

Explore remaining dataset features, including formatting, metadata, and global filters, and learn to update profiling results, edit definitions, upload new files, and create calculations for preparing reports and charts.

Create and save workbooks to organize and visualize data sets using the element, grammar, and visualization panels, then drag, drop, and customize charts for end-user previews.

Learn to create and customize a part chart in Oracle Analytics Data Visualization (DV) Mastery by selecting attributes and measures, choosing visualization, and adjusting axis, colors, legend, and data labels.

Learn chart assignments, sorting, and visualization changes in Oracle Analytics DV Mastery. Discover how to place attributes on axes, adjust colors, filters, and chart types.

Create and customize pie, donut, and sunburst charts in Oracle analytics data visualization (DV) mastery by selecting attributes and measures, applying visual properties, and formatting data labels and tiles.

Create and customize tile charts in Oracle Analytics by selecting the major, applying tile visuals, and adjusting layout, fonts, currency formatting, and background images for revenue and customers.

Design and insert titles and logos in Oracle Data Visualization using drag-and-drop, to create clear, professional dashboards that follow branding guidelines and enhance clarity.

Create and customize tables and pivot tables in Oracle Data Visualization to summarize data with dynamic, interactive layouts using rows, columns, and values.

Create a tree map in Oracle Data Visualization to visualize hierarchical data. Drag and drop onto canvas, assign age, gender, and marital status, and view average monthly consumption as rectangles.

Create a map in Oracle Data Visualization to visualize geographic data, add regions and measures like number of customers and total revenue, and apply top 10 filters and map properties.

Learn how to configure bubble size in Oracle Antix Maps to visually represent data magnitude, adjust min and max size ranges, change colors, and create interactive maps for data-driven insight.

Create and customize a line chart in Oracle Data Visualization to track trends over time using date attributes and derived columns, with measures like total revenue.

Create overlay charts in Oracle Data Visualization to compare two datasets on a single chart, such as revenue by offer and gender.

Learn to create a compo chart in Oracle Data Visualization by combining bar and line charts to compare total revenue and number of customers across offers and contracts.

Learn to create and apply filters in Oracle Data Visualization to focus data, using auto-apply, pin to all canvases, and expression filters for advanced conditions.

Learn to use filter options in Oracle Analytics Data Visualization, including use as filter, keep selected, and remove selected across charts, to refine and focus your data insights.

Explore the filter types in Oracle Analytics DV: list, list box, checkbox/radio, and slider. Learn to customize dashboard filters with defaults, multiple selections, and range controls to boost interactivity.

Learn to build drill-down reporting in Oracle Analytics Data Visualization. Create analytics link actions to pass table values like offer and gender to detail canvases, enabling interactive filtering across canvases.

Learn to create calculations in Oracle Data Visualization, using average, sum, and current date. Organize, validate, and test numeric calculations (NVL, case) in a canvas bar chart.

Master workbook properties in Oracle Data Visualization to customize color series, continuous color, brushing, viewer mode, thumbnails, background, no data text, and overlay opacity for clearer insights.

Explore how to customize canvas properties in Oracle DV to manage layouts and brush synchronization. Enable filters and auto-refresh for clearer visualizations.

learn to create conditional formatting in oracle analytics data visualization to highlight data points with color-coded thresholds, applying rules to charts and tables for clearer, interactive insight.

Learn to create and apply parameters in Oracle Data Visualization using the Visualize tab to drive dynamic filtering, placeholders, and dashboard and expression filters for interactive visualizations.

Explore what if analysis with parameters in Oracle Analytics DV Mastery to forecast customer counts by adjusting a percentage increase and updating visualizations on the dashboard.

Create a column selector with parameters in Oracle Analytics to build dynamic bar charts and dashboards that switch among gender, married status, offers, internet service, and contract types.

Explore present mode in Oracle Analytics Data Visualization to deliver dashboards like a presentation, with focused data, auto-sync of canvases, and export options to PowerPoint, PDF, or image.

Navigate the workbook properties in present mode, controlling undo, redo, toolbar options, zoom, notes, and presentation layout. Tune zoom scale and letterbox alignment to tailor image, text, and header placement.

Explore workbook properties and filter bar options to control full interactivity, filter visibility, and drill-down features across canvases, with distinctions between edit and run modes, temporary versus permanent filters.

Explore present mode workbook properties and the visualization toolbar, including sort, show assignments, change visualization type, watch list, maximize, map actions, copy data, and export options.

Explore canvas-specific properties in the workbook, including filter bar options, filter actions, and expression filters, and learn how to apply visualizations, zoom, and export controls to a single active canvas.

Master clustering in Oracle data visualization to group similar data, reveal patterns, and compare clusters using k-means or hierarchical methods, with interactive visualizations and adjustable cluster counts.

Identify and handle outliers in oracle analytics data visualization using built-in outlier detection, with sales data examples, and explore k-means and hierarchical clustering while investigating results.

Learn to add reference lines in Oracle Analytics Data Visualization, showing targets or averages on charts, with line or band options and axis and function customization.

Learn to add and analyze trend lines in Oracle Data Visualization, choosing linear, exponential, or polynomial options to reveal patterns, confidence intervals, and overall data direction.

Forecast and analyze time-series data in Oracle Data Visualization using ARIMA, seasonal ARIMA, or ETS, with line charts and shaded prediction intervals.

Explore the Explain feature in Oracle Analytics Data Visualization Mastery. It reveals insights for attributes and measures with correlations, drivers, and anomalies in visuals you can add to your workbook.

Explore how to explain a feature in Oracle data visualization by selecting major, viewing basic fact with descriptive statistics, and inspecting anomalies to uncover outliers.

Export and import DBA files to migrate reports, workbooks, and datasets across development, QA, pre-production, and production environments, including OAS, OAC, and Oracle Antix desktop, server, and cloud.

Learn how Oracle Analytics Server and Oracle Analytics Cloud manage access through users, groups, and predefined or user-defined application rules, enabling precise data visibility and permissions.

Discover how Oracle Analytics enables one-click advanced analytics and machine learning for predictive insights, including clustering, outlier detection, reference and trend lines, forecasting, and scenario analysis with Dataflow-trained models.

Explore the four key analytics types—descriptive, diagnostic, predictive, and prescriptive—and learn how Oracle Analytics turns historical data into actionable insights to explain past trends, forecast outcomes, and optimize decisions.

Compare traditional programming with machine learning, highlighting how traditional uses predefined rules and linear if-then logic, while machine learning learns from data to make predictions.

Explore supervised, unsupervised, and reinforcement learning, and learn algorithms like classification, regression, clustering, anomaly detection, and association rules to drive data-driven decisions.

Import and explore historical data in Oracle Analytics to build predictive analytics, train the model, optionally evaluate it, apply the model, and add scenarios.

Train a numeric prediction model in Oracle Analytics using sales 2025 data, building a data flow with elastic net linear regression, targeting sales count and 80/20 train-test split.

Evaluate the linear regression model in Oracle Analytics by inspecting specs, residuals, and key metrics—mean absolute error, RMSE, and coefficient of determination—and explore datasets like drivers for deeper analysis.

Apply the model to a new dataset in Oracle Analytics Data Visualization (DV) Mastery by creating a data flow and a target dataset, generating predictions, and visualizing the results.

Apply scenario modeling in the visualization workbook to add the sales count prediction from a machine learning model directly to datasets, without using a data flow.