Data analysis using Power BI involves several steps to transform, visualize, and gain insights from your data. Here's a general overview of the process:
Data Collection and Import: Start by collecting and organizing your data from various sources, such as Excel files, and CSV files. Open Power BI Desktop, and from the Home tab, select "Get Data" to import your data.
Data Transformation and Cleaning: After importing your data, you'll likely need to clean and shape it. Use the Power Query Editor to perform tasks like filtering, merging, renaming columns, and handling missing values.
Data Modeling: Create relationships between tables if your data consists of multiple tables. Define primary and foreign keys to enable data modeling.
Data Visualization: Build visualizations that help you understand your data. Drag and drop fields onto the report canvas to create charts, tables, maps, and other visuals.
Data Analysis and Calculation: Use DAX (Data Analysis Expressions) to create calculated columns and measures that provide deeper insights into your data. DAX is a formula language specific to Power BI.
Dashboard Creation: Assemble your visualizations into a cohesive dashboard. Arrange visuals, add slicers, and create a user-friendly layout for easy consumption.
Interactivity: Make your report interactive by adding filters, slicers, and drill-through actions. Allow users to explore the data from different angles.
Project Learnings:
• Created an interactive dashboard to track and analyze online sales data.
• Used complex parameters to drill down in worksheet and customization using filters and slicers
• Created connections, joined new tables, calculations to manipulate data and enable user-driven parameters for visualizations
• Used different types of customized visualization (bar chart, pie chart, donut chart, clustered bar chart, scatter chart, line chart, area chart, map, slicers, etc.)