Data Analytics Course
About Lesson
  • Tableau Basics
    • Introduction to Tableau and its components
    • Tableau Desktop vs. Tableau Public
    • Key features and benefits of Tableau
    • Understanding the Tableau interface
    • Basic concepts and terminology in Tableau
    • Connecting to data sources
    • Creating your first visualization
  • Design Features
    • Overview of Tableau design principles
    • Customizing visualizations (colors, labels, titles)
    • Using shapes and images in dashboards
    • Applying themes and templates
    • Formatting numbers, dates, and text
    • Enhancing aesthetics with backgrounds and borders
  • Time Series Analysis
    • Understanding time series data
    • Creating time series visualizations
    • Using date functions in Tableau
    • Time series forecasting
    • Analyzing trends and seasonality
    • Implementing moving averages and running totals
    • Comparing time periods
  • Aggregation and Granularity
    • Understanding aggregation in Tableau
    • Adjusting granularity of data
    • Using calculated fields for aggregation
    • Aggregating data in visualizations
    • Customizing aggregation settings
    • Drilling down and rolling up data
    • Aggregation best practices
  • Filters
    • Types of filters in Tableau
    • Applying filters to visualizations
    • Using quick filters and filter actions
    • Creating dynamic and cascading filters
    • Managing filter hierarchies
    • Filter optimization techniques
    • Best practices for using filters
  • Maps and Scatter Plots
    • Creating and customizing maps
    • Using geographic data in Tableau
    • Implementing map layers and controls
    • Creating scatter plots and bubble charts
    • Analyzing data with scatter plots
    • Adding interactivity to maps and scatter plots
    • Combining maps with other visualizations
  • Joining and Union
    • Understanding joins and unions in Tableau
    • Types of joins (inner, left, right, full)
    • Performing data joins in Tableau
    • Using unions to combine datasets
    • Managing data relationships
    • Handling data blending
    • Best practices for joins and unions

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