vishalbansal28/Advance-SQL-Sales-Analysis

Data Analyst portfolio project that deals with business KPIs, cleaning and transforming data and visualizing it to create a dashboard. The tools used are: SQL Server, T-SQL, Excel and Power BI.

Advance SQL Sales Analysis: Dive into Data and Drive Insights

This repository provides a hands-on project focused on data analysis and visualization using SQL and Power BI. We'll explore sales data from the AdventureWorks sample database to uncover valuable insights and create a comprehensive dashboard for business decision-making.

Project Goals:

• Clean and Prepare Data: Employ T-SQL techniques to cleanse, transform, and extract relevant sales data from the AdventureWorksDW database.
• Build a Data Model: Connect tables in Power BI to establish relationships and create a solid foundation for analysis.
• Visualize Insights: Craft an interactive dashboard in Power BI showcasing key metrics, trends, and geographical breakdowns.

Getting Started:

1. Prerequisites:

   • SQL Server: Ensure you have SQL Server (SQL Express) installed and configured.
   • Power BI Desktop: Download and install the latest version of Power BI Desktop.
   • AdventureWorks Data: Obtain and restore the AdventureWorksDW and AdventureWorksLT databases. You can find instructions on https://docs.microsoft.com/en-us/sql/samples/adventureworks-install-configure?view=sql-server-ver15&tabs=ssms.
   • Update AdventureWorksDW data: Run the SQL script https://github.com/techtalkcorner/SampleDemoFiles/blob/master/Database/AdventureWorks/Update_AdventureWorksDW_Data.sql to enhance your data.

2. Understanding the Data:

   • Explore the AdventureWorksDW database and differentiate between FACT tables and DIMENSION tables.
   • Identify tables relevant to your business request and user stories.

3. Data Cleaning and Transformation:

   • Utilize T-SQL to perform the following operations:
     • Rename columns.
     • Combine columns using techniques like CONCAT.
     • Apply filtering using the WHERE clause.
     • Sort data with ORDER BY.
     • Use LEFT JOIN to combine related tables.
     • Implement conditional logic with the CASE() function.
     • Handle null values with ISNULL().

4. Power BI Dashboard Construction:

   • Import Data: Load the prepared data from SQL Server into Power BI Desktop.
   • Data Model: Connect tables to establish relationships and create a logical model.
   • Measures: Define calculations using DAX to analyze data and provide insights.
   • Visualization: Create a visually engaging dashboard using various chart types (pie charts, line charts, bar charts, maps, etc.) and custom visuals.
   • Interactive Elements: Allow users to filter and drill down into data for detailed exploration.

Project Resources:

• Power BI Dashboard File: https://github.com/vishalbansal28/Advance-SQL-Sales-Analysis/blob/main/Sales%20Report.pbix (Download and open in Power BI Desktop for interactive exploration)
• Dashboard Screenshot: https://github.com/vishalbansal28/Advance-SQL-Sales-Analysis/blob/main/Sales%20Report.pdf

Let's get started! Dive into the world of data analysis and explore the power of SQL and Power BI to uncover hidden insights and drive business success.