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About

A motivated fresher and certified Data Analyst skilled in transforming multi-source datasets into dashboards and forecasts that drive decisions. Delivered end-to-end analytics across EV demand prediction, computer-vision monitoring, and stakeholder reporting consistently on-time, with measurable impact aligned to business goals.

Experience1

  1. Data Analyst Intern

    2025-06-03 — 2025-07-05

    Elevate Labs

    Data Analyst Intern – Elevate Labs (June 2025 to July 2025) During this internship, i worked with real datasets and carried out end-to-end data analysis to support ongoing projects. Cleaned, merged, and transformed large datasets using Python and Excel, and built clear visual reports and dashboards to highlight patterns, trends, and performance metrics. Uused tools like pandas, NumPy, Excel, and Tableau to turn raw data into practical insights that supported team decisions. A major part of my internship was a weather-forecasting project, where i combined historical weather data with basic predictive methods to estimate short-term weather trends. Prepared the dataset, handled missing values, explored key variables, and built a simple forecasting model to show how temperature and rainfall patterns could be predicted. Presented the results through visualizations and a written summary that explained the model’s behavior and its real-world relevance.

Projects3

  • EV Charging Demand Forecasting

    Analyzed EV charging, weather, and traffic datasets using Python and Excel. Built forecasts to identify peak demand periods and usage patterns. Visualized insights using Tableau dashboards.

    • Python
    • Excel
    • Tableau

  • Global CO2 Emission Tracker

    Developed a Global CO₂ Emissions Tracker dashboard using Tableau by analyzing and processing emissions data across energy, transport, and industry sectors with Python (Jupyter Notebook) and Excel.

    • Tableau
    • Python
    • Excel

  • Sales Data Analysis and Visualization

    Designed and implemented a sales data analysis pipeline using Python and SQLite by executing SQL queries to compute total quantity and revenue per product, and visualized insights through bar charts using pandas and matplotlib.

    • Python
    • SQLite
    • SQL
    • pandas
    • matplotlib

Education1

  • B.Tech – Electronics & Communication Engineering

    2026

    University of Engineering and Management Kolkata, WB

    General