Headline

Data Analyst | SQL • Python • Power BI • Excel | Dashboard & Business Insights

About

I’m an entry-level Data Analyst with hands-on experience analyzing 16,000+ records across retail, e-commerce, telecom, and HR datasets to drive data-backed insights. I specialize in SQL-driven analysis, Python-based exploratory data analysis, and Power BI dashboards that help stakeholders understand trends, risks, and opportunities. My work focuses on transforming raw data into clear, actionable insights using statistics, visualization, and machine learning. Through academic and project work, I have: Identified key churn drivers contributing to 65% of customer churn Built Power BI dashboards to monitor KPIs and high-risk segments Improved reporting accuracy by 15% using optimized SQL queries Reduced reporting time by 30% through automated dashboards Developed machine learning models achieving up to 85% accuracy I’m comfortable working with SQL, Python (Pandas, NumPy), Power BI, Excel, and applying statistical techniques such as hypothesis testing and feature engineering to solve business problems. I’m currently seeking entry-level Data Analyst opportunities where I can contribute analytical skills, continue learning, and support data-driven decision-making.

Location

Dharwad

Work experience

Customer Churn Analysis & Prediction at NA, Dec 2025 - Jan 2026 · 2 mos, Bengaluru, Karnataka, India, Tools: Excel, SQL, Python, Power BI, Machine Learning Analyzed 4,000+ customer records to identify churn drivers responsible for 65% of churn cases Built an interactive Power BI dashboard to track churn KPIs and flag high-risk customers Developed a Logistic Regression model (82% accuracy) to support retention prioritization

Data Science Trainee – Projects at Self-Project | Personal, Nov 2025 - Jan 2026 · 3 mos, Bengaluru, Karnataka, India, • Built an end-to-end machine learning solution to predict employee attrition using HR analytics data • Performed data cleaning, exploratory data analysis (EDA), and feature engineering to uncover key attrition drivers • Trained and evaluated models including Logistic Regression, Decision Tree, and Random Forest • Improved prediction accuracy and identified high-risk employees to support data-driven HR decisions • Visualized insights using charts and dashboards for better stakeholder understanding • Tools & Technologies: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn

Education

Data science and Gen AI, Data science, Boston institute of analytics

Bachelor of computer applications, Karnataka University, Dharwad