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Data with 1+ years in Power BI, SQL & Python for data analysis and visualization.
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Assessing your cultural and operational fit
Data Analyst fresher with hands-on experience in data cleaning, exploratory data analysis (EDA), dashboard design, and KPI tracking through internships and personal projects. Proficient in Power BI (DAX, Power Query), SQL, and Python for transforming raw data into business insights. Seeking an entry-level Data Analyst role to drive data-driven decision-making in a fast-paced organization.
SAL Engineering & Technical Institute
Bachelor of Engineering · Computer Engineering
August 1, 2022 – June 30, 2026
Nishan School
Higher Secondary Certificate (HSC)
June 1, 2021 – May 31, 2022
Nishan School
Secondary School Certificate (SSC)
June 1, 2019 – May 31, 2020
JHS & Associates LLP
Data Analyst Intern
December 1, 2025 – April 1, 2026
Ahmedabad, Gujarat, India
Grras IT Solutions
Data Analyst Intern
February 1, 2025 – July 1, 2025
Ahmedabad, Gujarat, India
E-commerce Data Analysis
July 1, 2025 – June 1, 2026
Analyzed 105,952 e-commerce orders across 6,987 SKUs using Python and Pandas, building an interactive Streamlit dashboard with Plotly to visualize revenue (₹709.9L), category performance, and geographic trends. Applied KMeans clustering to segment SKUs into Star, Regular, High Risk, and Low Stock categories (1,747 Star, 970 High Risk SKUs) to support inventory prioritization decisions. Built a Random Forest classification model and an XGBoost regression model to predict order cancellation risk and forecast sales. Developed a live ML prediction tool for real-time cancellation risk and SKU segment classification, and integrated a Power BI dashboard for business reporting.
Customer Churn & Retention Analysis
July 1, 2025 – June 1, 2026
Performed end-to-end churn and retention analysis by writing MySQL queries. Building MongoDB aggregation pipelines (session analytics, feature retention, onboarding funnels, engagement scoring). Using Python for data merging, cleaning, hypothesis testing, and customer segmentation. Designed a Power BI dashboard analyzing 1,204 customers with 22.09% churn rate, featuring interactive filters and actionable retention recommendations for stakeholders.
Customer Shopping Behavior Dashboard | Power BI
July 1, 2025 – June 1, 2026
Designed and developed an interactive Power BI dashboard analyzing 3,900+ customer records across multiple product categories to track revenue, customer behavior, and sales performance. Utilized DAX measures, KPIs, and drill-down visualizations to identify high-value customer segments and uncover purchasing trends. Analyzed revenue by location, age group, product category, and discount utilization, enabling data-driven marketing and sales recommendations. Created dynamic dashboards with filters, slicers, and interactive visualizations to improve reporting efficiency and support business decision-making.
Credit Delinquency Prediction & Risk Analysis
July 1, 2025 – June 1, 2026
Analyzed financial data for 500 customers and built a Power BI dashboard tracking credit usage, missed payments, loan amounts, and delinquency rates by age group, location, and card type. Created interactive visuals showing delinquency trends by age group, debt-to-income ratio, and credit usage over account tenure. Built a Random Forest classification model in Python to predict customer delinquency risk based on income, credit score, credit utilization, and payment history. Identified class imbalance in the dataset as a key limitation affecting model performance, suggesting techniques like SMOTE for future improvement.
Python + Artificial Intelligence Course
Centre for Development of Advanced Computing (C-DAC)
June 1, 2026 – Present
Data Analyst Certificate
Tata GenAI Powered Data Analytics / Tata Forage
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, covering e-commerce, customer churn, shopping behavior, and credit risk, indicates a broad interest in applying data analysis across different business domains. This adaptability, coupled with experience in both SQL and NoSQL databases, suggests a willingness to learn and integrate new technologies, which aligns well with dynamic team environments. However, the experience is primarily academic and internship-based, which might require more mentorship in a fast-paced professional setting.
Soft Skills & Operational Fit
The candidate demonstrates a proactive approach to learning and applying data analysis techniques through personal projects and internships. Their ability to identify and address data quality issues, automate reporting, and present actionable insights suggests good problem-solving and operational efficiency. The focus on creating interactive dashboards indicates an understanding of stakeholder needs and effective communication of data.