Introduction:

SAS Visual Analytics is a powerful, web-based tool designed to help organizations explore, analyze, and visualize their data in real-time. With advanced data visualizations, dynamic reports, and predictive analytics capabilities, SAS Visual Analytics empowers users to turn raw data into actionable insights. This 5-day course will provide participants with a comprehensive understanding of how to use SAS Visual Analytics to analyze business data, create engaging visualizations, and share interactive reports, enabling data-driven decision-making across the organization.

Objectives:

By the end of this course, participants will:

• Understand the SAS Visual Analytics interface and key features.
• Learn how to prepare and load data for analysis in SAS Visual Analytics.
• Create dynamic reports, dashboards, and data visualizations.
• Gain proficiency in using advanced analytics features such as forecasting, trend analysis, and predictive modeling.
• Understand how to share and collaborate on reports and visualizations effectively.
• Apply best practices in data visualization and reporting to communicate insights clearly.

Who Should Attend:

This course is designed for:

• Data analysts, business analysts, and data visualization professionals looking to enhance their skills with SAS Visual Analytics.
• Business intelligence professionals who want to use SAS for effective data exploration and reporting.
• Executives and decision-makers who need to interpret data visualizations to drive business decisions.
• IT professionals involved in the implementation or support of SAS Visual Analytics.

Day 1: Introduction to SAS Visual Analytics and Data Exploration

• Morning:

  • Overview of SAS Visual Analytics:

    • Introduction to the SAS Visual Analytics platform and its components: data exploration, reporting, and advanced analytics.
    • Understanding the SAS Visual Analytics user interface and workspace.
    • Exploring the various data types and sources supported by SAS Visual Analytics (e.g., SAS datasets, CSV files, relational databases).

  • Connecting and Loading Data:

    • Loading data into SAS Visual Analytics from various sources.
    • Data preparation and cleaning within SAS Visual Analytics.
    • Understanding data metadata and setting data relationships.

• Afternoon:

  • Basic Data Exploration:

    • Exploring data using tables, graphs, and summary statistics.
    • Using filters, sorting, and grouping for deeper insights.
    • Identifying trends and patterns in data through visual exploration.

  • Hands-on Session:

    • Load sample datasets into SAS Visual Analytics and perform basic data exploration (filters, graphs, and summary statistics).

Day 2: Creating Visualizations and Interactive Reports

• Morning:

  • Creating Basic Visualizations:

    • Introduction to visualization types: bar charts, line charts, pie charts, scatter plots, etc.
    • Creating basic visualizations in SAS Visual Analytics.
    • Customizing visualizations: adjusting colors, axes, labels, and chart formatting.

  • Using Graphs to Convey Insights:

    • Designing clear and effective graphs to communicate data trends.
    • Creating visualizations for categorical and continuous variables.
    • Using annotations to highlight important data points.

• Afternoon:

  • Creating Interactive Reports:

    • Designing reports with multiple visualizations.
    • Adding interactivity: filters, drop-down menus, and drill-down capabilities.
    • Customizing report layouts, themes, and styles for better presentation.

  • Hands-on Session:

    • Create an interactive report using various visualizations, such as bar charts and line charts, and apply interactive features like filters and drill-downs.

Day 3: Advanced Visualization Techniques and Data Transformation

• Morning:

  • Advanced Visualizations:

    • Working with advanced charts: heatmaps, geo maps, waterfall charts, and histograms.
    • Understanding when to use different visualization types for specific business cases.
    • Creating complex visualizations with multiple dimensions and measures.

  • Data Transformation and Calculated Items:

    • Creating calculated columns, measures, and custom expressions in SAS Visual Analytics.
    • Data aggregation and transformation techniques for preparing data for advanced analysis.
    • Working with time-based data and creating time-series visualizations.

• Afternoon:

  • Forecasting and Trend Analysis:

    • Using SAS Visual Analytics to perform trend analysis and forecasting.
    • Applying time series forecasting models to predict future trends.
    • Visualizing forecast results and interpreting predictive insights.

  • Hands-on Session:

    • Create an advanced visualization such as a heatmap or geo map and perform trend analysis with forecasting techniques.

Day 4: Sharing, Collaboration, and Report Distribution

• Morning:

  • Collaborating with Reports:

    • Sharing reports with others through SAS Visual Analytics.
    • Setting up report permissions and user access control.
    • Collaborating on reports by adding comments, annotations, and feedback.

  • Publishing Reports and Dashboards:

    • Publishing and scheduling reports for automated distribution.
    • Exporting reports to different formats (PDF, Excel, HTML).
    • Embedding reports and visualizations into web applications.

• Afternoon:

  • Creating Dashboards:

    • Building interactive dashboards for executive decision-making.
    • Organizing visualizations in a dashboard layout for easy navigation.
    • Using dynamic filters and controls to update dashboards in real-time.

  • Hands-on Session:

    • Create a dashboard with multiple visualizations and implement sharing and collaboration features, such as comment and permission settings.

Day 5: Advanced Analytics and Deployment in SAS Visual Analytics

• Morning:

  • Predictive Analytics and Modeling:

    • Introduction to predictive analytics in SAS Visual Analytics.
    • Creating and evaluating models using regression, classification, and clustering techniques.
    • Visualizing predictive results and model outputs.

  • Integration with Other SAS Products:

    • Integrating SAS Visual Analytics with other SAS tools like SAS Viya, SAS Enterprise Miner, and SAS Studio.
    • Using external data sources (Big Data, cloud, and APIs) for enriched analytics.

• Afternoon:

  • Best Practices in Visualization and Reporting:

    • Data visualization best practices: ensuring clarity, accuracy, and usability.
    • Designing dashboards and reports for different audiences (executives, analysts, operational teams).
    • Implementing performance optimization techniques in large-scale deployments.

  • Final Hands-On Project:

    • Build an advanced report or dashboard using SAS Visual Analytics, incorporating predictive analytics and a variety of visualizations.
    • Present the final project for peer review and feedback.

Key Takeaways:

• In-depth understanding of SAS Visual Analytics and its tools for data visualization and analysis.
• Ability to create interactive, dynamic reports and dashboards for data exploration.
• Proficiency in advanced visualization techniques like heatmaps, geo maps, and forecasting.
• Hands-on experience in applying predictive analytics models and interpreting their results.
• Knowledge of best practices in reporting, data visualization, and collaboration.