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About

Entry-level Data Analyst with hands-on experience across four end-to-end projects spanning data cleaning, exploratory data analysis, SQL querying, and interactive dashboard development using Python, SQL, Power BI, and Excel. Skilled at turning raw datasets — including one with over 381,000 rows — into clear, data-driven business insights through statistical analysis and visualization. Currently completing a virtual Data Analytics internship at CodSoft (Sep–Oct 2026) alongside a Data Analytics certification program. Seeking an entry-level Data Analyst role to apply analytical, visualization, and reporting skills to real business problems.

Experience1

  1. Data Analytics Virtual Internship

    Sep 2026 — Present

    CodSoft

    Participating in a virtual internship focused on data analytics, gaining practical experience alongside a certification program.

Projects4

  • Motorola Sales Dashboard

    Built a 2-page interactive Power BI dashboard analyzing ₹769M in sales, 19K units, and 4K transactions across cities, products, and payment methods, enabling faster performance tracking by region and product line. Designed 4 KPI cards and 5 interactive slicers to let stakeholders drill into sales, customer ratings, and geographic performance without manual filtering.

    • Power BI
    • Power Query
    • DAX

  • Vehicle Insurance Cross-Sell Analysis

    Performed EDA on a 381,109-row insurance dataset and engineered Age_Group, Tenure_Group, and Premium_Category features to support customer segmentation. Found a ~25% response rate among damaged-vehicle, previously uninsured customers — nearly double the 12.3% baseline — highlighting a high-value target segment. Built 15 visualizations to support data-driven targeting recommendations for cross-sell campaigns.

    • Python
    • Pandas
    • NumPy
    • Matplotlib
    • Seaborn

  • Loan Sanction Data Analysis

    Cleaned 367 loan applications using mode/median imputation and engineered Total_Income and Loan_Amount_Term_Years features. Found 39% higher reported income among graduates vs. non-graduates, alongside larger average loan amounts (141K vs. 118K), informing income-based risk segmentation. Identified that 83.9% of applicants had a positive credit history, a key factor in loan approval patterns.

    • Python
    • Pandas

  • SQL Hospital Data Analysis

    Wrote 10 SQL queries using aggregations, grouping, and ranking to analyze hospital operations data. Identified the top-3 departments by patient count, longest patient stay, city-wise patient distribution, and monthly medical expense trends to support resource-planning decisions.

    • SQL
    • MySQL

Education2

  • B.Com (Hons)

    2023 — 2026

    School of Open Learning, University of Delhi

    Commerce

  • Class 12 (CBSE)

    2023

    Govt. Sarvodaya Kanya Vidyalaya No. 2, Shakurpur, Delhi