Hi! I am Sunam Niroula,
Data Analyst & Aspiring AI Engineer
Data Analyst & Aspiring AI Engineer
I'm passionate about turning data into insights. I use tools like SQL, Excel, Python, and Tableau to clean, analyze, and visualize real-world datasets.
I’m currently building a strong portfolio of end-to-end projects based on real data, and this is where I document my learning, results, and growth.
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