Exploring DMart Sales & Understanding Key Fields

You have recently joined DMart, one of India’s leading retail chains, as a Junior Data Analyst Intern. Today is your first day at work, and you’ve been assigned to the Sales Analytics Team.

Before you start analysing anything, you need to understand the business and the data we work with. 

In real-world analytics, jumping directly into analysis without understanding the dataset often leads to incorrect insights.

DMart operates across Multiple regions and sells products from different categories. Every Transaction - From Customer.

Purchase to delivery is recorded in the company internal system.This raw data is shared with the Analytics Team to track performance, identify trends and support business decisions.

Your Manager Provides you with the sales Dataset and explains that

• This Dataset contains Transactional-Level Sales data.
• It includes details like order_id, customer_type, regions, sales,profit, delivery timelines, city etc.

Before Performing any analysis, your first responsibility is to:

• Understand what each column represents
• Identify important business metrics
• Get familiar with how retail data is structured.

1) Introduction to Data

2) Introduction to Git

In this task, you will begin by understanding the business problem and the structure of the dataset provided to you. This is a crucial first step in any data analysis workflow.

• Download the dataset from the link provided by your manager

Access the Dataset

Data set link - DeMart Dataset

• Save the file on your system in a dedicated folder (e.g., DMart_Sales_Analysis)

• Open the dataset using Microsoft Excel or Google Spreadsheet

Observe the Dataset Layout

Once the dataset is open in Excel or Google Spreadsheet.Observe how the data is structured:

• Each row = one order/transaction

• Each column = one type of information 

Identify Dataset Components

Carefully observe the dataset and understand what each column represents: 

Order Information: order_id, order_date, delivery_date 

Location Information: region, city

Product Information: Category

Sales Metrics: sales, quantity, discount, profit

Customer Details: Customer_type

Transaction Details: payment_mode

In this task, you will actively explore the dataset using Excel to extract basic but important information.

This step helps you understand the scale, coverage, and structure of the data before performing any analysis.

Find Total Number of Records

Select the Dataset and Click on any cell inside the dataset.

Press Ctrl + A to select the entire data

Count Total Rows

• Look at the row numbers on the left side
• Identify the last row number

Total Records = (Last Row Number - 1)  [(Exclude header row)]

Alternatively Click on any column with continuous data (e.g., order_id)

Press Ctrl + Shift + ↓ and Observe the row number at the bottom

Locate Region Column and Find the column named region

Identify Unique Regions

Select the header row

 Go to Data Tab → Filter

Click on filter, filter will be added to region column

To View Unique Values, Click the filter dropdown as shown in above image  on region and Observe all unique region names

Locate Category Column

Apply Filter as discussed above and Click the filter dropdown on category

Identify Product Categories

Observe Values and note all unique categories available such as Clothing,Electronics,Grocery,Home Care and Personal Care.

Observe Header Row of the dataset

Read Column Names and Categorise Fields 

Identify Available Fields (Columns)

• Sales Metrics: sales, profit, discount, quantity
• Location: region, city
• Customer Info: customer_type
• Order Info: order_id, dates
• Transaction Info: payment_mode

After explaining the dataset, your manager adds that,

“In our team, we don’t just work on data locally. Every dataset, report, and analysis is managed and shared

through version control systems like Git. This helps us track changes, collaborate efficiently,

and maintain proper documentation.”

You are informed that:

• Teams regularly upload datasets and work files to Git repositories
• This ensures transparency, version tracking, and easy collaboration
• It also allows other team members to access and review your work anytime

Search on google how to install Git

Select the Git install showed in the below image , it will redirect you to install section.

Select the Git install as per the operating system. In this case we are assuming Operating system to be Windows hence selecting install Git for windows Option.

After downloading you can See Git on your Machine

After clicking on that follow the instruction,Click on Next button

Click On this

Click on install Button

After Click on the Install you See Installation starts  

 Open CMD as shown below

Type "git --version" as shown below and hit enter button. If you are able to see version number of install git software then its signify that installation was successfully completed on your Machine.

Go to GitHub or Open: https://github.com and click on Sign up

 Enter: Email, Username, Password

After Installing Git Properly we need Git Hub Repository. Follow below Steps to create Repository On Git Hub.

Verify Account by entering the code received on your registered email address.

Verify Account by entering the code received on your registered email address.

Click on “New” Repository to create new repository

Enter Repository Name: dmart-sales-analysis

Select Public and Click Create Repository

Create a project Folder named as Dmart_sales_analysis on C Drive or any other drive. Once Created, open that folder .

Right Click inside that folder to open that folder in terminal as shown below.

Initialize Git by executing a command

This will create git Repository in your folder as shown below.

Add Files

Commit Files

This save your files in GIT.

Connect to GitHub Repository

 Copy Repository URL- Form Github Copy Your Repository URL

Link Local to GitHub

Push Files to GitHub

Great job!
You have successfully completed your first lab.

In this Lab you have explored the DeMart Business domain,

Explored the dataset provided and Gained knowledge of Git and Github to save the work in Repository.

Topic : Merging and Retrieving Data Using Lookup Functions

1) Merging and Retrieving Data Using Lookup Functions
2) Reports that can be filtered by region or product