Hi! I am Sunam Niroula,
Data Analyst & Aspiring AI Engineer

Skills : 

• SQL | Excel | Python | Tableau | GitHub

• Skills Used: SQL, PostgreSQL (or MySQL), Data Analysis, Business Intelligence

• Tools: SQL (MySQL)

📌 Project Overview:

Analyzed a real-world Walmart sales dataset using structured SQL queries to uncover trends, seasonal effects, and sales performance across various stores and departments. Focused on extracting business insights and building queries that simulate reporting tasks used in real jobs.

🔍 Key Tasks & Insights:

• Calculated total revenue and average sales across different stores and departments

• Analyzed the impact of holidays, fuel price, and temperature on sales

• Identified high-performing stores and underperforming ones

• Explored sales trends over time using GROUP BY and date functions

• Investigated how CPI (inflation) and unemployment rate affect store revenue

🧠 What I Learned:

• Writing complex JOIN, GROUP BY, and CASE statements for business questions

• How to interpret messy retail data and turn it into actionable insights

• Thinking like a business analyst, not just a coder

• Skills Used: Excel, Data Cleaning, Financial Analysis, Formula Building

• Tools: Microsoft Excel

📌 Project Overview:

Worked with a messy financial Excel file that included Coca-Cola’s Profit & Loss, Balance Sheet, and Cash Flow statements all mixed together. This project focused on extracting, cleaning, and transforming the three statements into an analysis-ready format.

🧹 Key Cleaning & Structuring Tasks:

• Started by isolating the Profit & Loss section from the raw multi-table Excel dataset

• Removed blank rows, merged cells, redundant totals, and unnecessary formatting

• Standardized all line item names (e.g., "NET OPERATING REVENUES" → "Revenue")

• Categorized rows into Revenue, Expense, and Profit for easier filtering and analysis

• Converted text-based numbers to numeric format

• Added calculated totals using Excel formulas to ensure data integrity

• Similar analysis for other two datasets

📊 Analysis-Ready Features:

• Fully structured table with consistent rows and fiscal year columns

• Grouped line items for trend analysis and future dashoarding

• Final cleaned sheet ready for PivotTables, or visual charts

• Published the cleaned version to GitHub as part of a growing analytics portfolio

🧠 What I Learned:

• Best practices for cleaning financial datasets in Excel

• How to turn messy business data into something analysis-ready and presentable

• How to think like an analyst when identifying what to keep, group, or discard

• Preparing data for storytelling — not just calculation