Data Analytics classes Fees

In this lesson, we will explore a real-world data analytics use case where data is scattered across Excel files, CSVs, and a SQL database. We’ll demonstrate how to use Advanced Excel, Power Query, SQL, Tableau, and Power BI to create a seamless data solution—from extraction and transformation to visualization and actionable insights.

retail company wants to analyze its sales performance. The data is stored across multiple formats:

1. Excel Files: Monthly sales data by region.
2. CSV Files: Product master data.
3. SQL Database: Customer purchase history.

The goal is to create an interactive dashboard that provides insights into:

• Regional sales performance.
• Top-performing products.
• Customer purchasing behavior.

Step 1: Data Extraction

• Advanced Excel: Quickly review and clean the Excel and CSV files using formulas, filters, and conditional formatting.
• SQL: Query the database to extract relevant customer purchase data using SELECT statements and JOINS.

Step 2: Data Transformation

• Power Query: Combine and transform the Excel and CSV files into a unified format. Merge this data with the SQL output, removing duplicates and handling missing values.

Step 3: Data Modeling

• SQL: Use database tools to optimize data structure, applying relationships and indexing for efficient querying.
• Power Query/Power BI: Create relationships between datasets and build calculated columns where necessary.

Step 4: Data Visualization

• Tableau: Design an interactive dashboard showing sales trends, regional comparisons, and top products. Use advanced visualization techniques like heat maps and bullet charts.
• Power BI: Create dynamic dashboards with slicers and drill-through capabilities for deep insights into customer behavior and sales metrics.