Summary of "Full Project in Excel with Interactive Dashboard | Excel Tutorial for Beginners"
Summary of "Full Project in Amazon.com/s?k=Excel&tag=dtdgstoreid-20">Excel with Interactive Dashboard | Amazon.com/s?k=Excel&tag=dtdgstoreid-20">Excel Tutorial for Beginners"
This video tutorial by Rishabh demonstrates how to complete an end-to-end data analytics project in Amazon.com/s?k=Excel&tag=dtdgstoreid-20">Excel, focusing on creating an Interactive Dashboard to solve a business problem for a retail store named Vrindavan Store. The tutorial covers data cleaning, processing, analysis, visualization, and sharing insights with clients.
Main Ideas, Concepts, and Lessons
- Project Overview and Objective
- The goal is to analyze sales data from Vrindavan Store, which sells online through multiple channels.
- Create an annual report for 2022 to understand customer behavior and help grow the business in 2023.
- The final output is an Interactive Dashboard with slicers that filter data dynamically across charts.
- Data Understanding and Cleaning
- Initial review of columns such as Order ID, Customer ID, Gender, Date, Status, Category, Size, Amount, City, State, and Country.
- Check for:
- Duplicate or null values (none found in Order ID and Customer ID).
- Consistency in categorical data (e.g., unify gender labels from “M”, “man”, “Woman”, “W” to standardized values).
- Correct date formats and valid ranges.
- Numeric data validation for size, amount, quantity.
- Replace inconsistent or incorrect data using Amazon.com/s?k=Excel&tag=dtdgstoreid-20">Excel’s Find & Replace.
- Remove unnecessary columns or data if irrelevant.
- Data Processing
- Create new calculated columns for better analysis:
- Age group classification using IF formula:
- Senior: age ≥ 50
- Adult: age between 31 and 49
- Teenager: age ≤ 30 (used loosely)
- Extract month names from date column using
TEXT()formula for monthly analysis.
- Age group classification using IF formula:
- Convert formulas to values to improve performance.
- Create new calculated columns for better analysis:
- Data Analysis Using Pivot Tables and Charts
- Create pivot tables to answer business questions:
- Compare sales amount and order count by month in a combined chart.
- Identify the month with highest sales and orders (March).
- Analyze sales by gender using pie charts.
- Examine order status distribution (Delivered, Canceled, Refund, Return).
- Determine top 5 states by sales.
- Analyze sales by age group and gender.
- Identify sales contribution by sales channel (Amazon, Flipkart, Myntra).
- Formatting tips for charts:
- Use combo charts with secondary axis for sales vs orders.
- Format axis numbers to show values in millions with decimals.
- Remove gridlines and grand totals for cleaner visuals.
- Customize pie chart data labels and legend for clarity.
- Use horizontal bar charts for top states visualization.
- Create pivot tables to answer business questions:
- Creating an Interactive Dashboard
- Insert slicers for Month, Channel, and Category to filter all pivot tables/charts simultaneously.
- Connect slicers to multiple pivot tables using "Report Connections" to sync filters.
- Demonstrate filtering dashboard dynamically by selecting different slicer values.
- Clear filters easily with the slicer’s clear button.
- Insights and Sharing with Client
- Summarize key insights from the dashboard:
- Maximum sales occur in March.
- Women contribute the most to sales.
- Majority of orders are delivered successfully.
- Top states for sales: Maharashtra, Karnataka, Telangana, Tamil Nadu.
- Adult women (ages 30-49) are the prime customers.
- Amazon is the leading sales channel (~35% contribution).
- Prepare a written summary of insights alongside the dashboard for client presentation.
- Provide actionable recommendations:
- Highlight that reports can be generated on different time scales (weekly, monthly, quarterly, annual).
- Summarize key insights from the dashboard:
Detailed Methodology / Instructions
- Data Cleaning:
- Check each column for nulls, duplicates, and inconsistencies.
- Standardize categorical data using Find & Replace.
- Validate numeric and date columns.
- Remove or ignore irrelevant columns.
- Data Processing:
- Add new columns for age groups using IF formula.
- Extract month names from dates using
=TEXT(date_cell, "mmm"). - Convert formulas to values to optimize workbook performance.
- Pivot Table Creation:
- Insert pivot tables for different analyses:
- Sales and orders by month.
- Sales by gender.
- Order status counts.
- Top states by sales.
- Sales by age group and gender.
- Sales by channel.
- Remove grand totals for cleaner presentation.
- Insert pivot tables for different analyses:
- Chart Creation and Formatting:
- Use combo charts for sales and orders with secondary axis.
- Use pie charts for gender and order status distribution.
- Use horizontal bar charts for top states.
- Format axis numbers to display in millions with two decimals.
Category
Educational
