Data Analyst with less than a year in Power BI, Python & SQL
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Assessing your cultural and operational fit
Results-driven Data Analyst with hands-on experience in Power BI, Tableau, Advanced Excel, Python, and SQL. Proven ability to transform large datasets into actionable business insights through interactive dashboards, statistical analysis, and data visualization. I Completed B.Tech in Computer Science (AI & ML), with strong expertise in EDA, data cleaning, KPI reporting, and stakeholder-ready reporting. Seeking to leverage analytical and BI skills to drive data-driven decision-making in a fast-paced analytics environment.
Vaagdevi Engineering College, Warangal
B.Tech · Computer Science & Engineering (AI & ML)
January 1, 2022 – January 1, 2026
Sales and Profit Analysis Dashboard
June 19, 2026 – Present
Developed a multi-dimensional Power BI dashboard to analyze sales, profit, quantity sold, discounts, and order trends - enabling period-over-period performance comparisons (daily, monthly, quarterly, yearly). Identified top 5 and bottom 5 products by sales, profit, and quantity sold to guide product strategy and inventory decisions. Built dynamic filters and interactive visuals exploring KPIs by product, customer segment, promotion category, city, and date range. Delivered insights on discount impact, seasonal demand patterns, and city-wise revenue distribution to support executive decision-making.
Business Analytics & Executive Dashboard
June 19, 2026 – Present
Built an end-to-end Excel analytics solution using transactional data to analyze revenue, expenses, profit margins, and customer behavior across multiple business dimensions. Designed 8+ pivot tables evaluating performance by region, product line, department, payment method, and customer segment; built comparison reports to identify high-profit products and cost-efficient departments. Developed an interactive executive dashboard featuring KPI summary cards, revenue trend charts, and profitability insights, reducing manual reporting time significantly. Delivered actionable insights on top revenue-generating products, high-value payment methods, and regional demand patterns.
View ProjectCustomer Churn Prediction Model
June 19, 2026 – Present
Built an end-to-end machine learning pipeline to predict customer churn for a telecom dataset of 7,043 records using Python (Pandas, Scikit-Learn, Seaborn), achieving 82% model accuracy with a Random Forest classifier. Performed comprehensive EDA, feature engineering, and label encoding on 21 features; applied SMOTE to handle class imbalance and improve minority class recall by 18%. Evaluated models using confusion matrix, ROC-AUC (0.87), precision-recall curves; identified top churn drivers - contract type, tenure, and monthly charges - enabling targeted retention strategies. Visualized churn patterns using Matplotlib and Seaborn; delivered a feature importance report showing contract type as the strongest predictor with 34% contribution.
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest and foundational skill set in data analysis, business intelligence, and machine learning, aligning well with a Data Analyst role. The diversity of tools and techniques used (Excel, Power BI, Python, SQL) indicates adaptability and a willingness to learn new technologies. The focus on delivering business insights and reducing manual reporting time suggests a practical, value-driven mindset. However, without professional experience, the cultural fit is primarily based on academic project alignment and stated interests.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a results-driven approach and an ability to deliver actionable insights, which are valuable for operational fit. The focus on end-to-end solutions and clear reporting suggests good problem-solving and communication skills, though these are primarily inferred from project outcomes rather than direct evidence of soft skills.