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Data Analyst with less than a year in Python, SQL, and Power BI.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Detail-oriented Data Analyst with hands-on experience in Excel, SQL, Python, and Power BI. Skilled at collecting, cleaning, and organising data from multiple sources, preparing daily/weekly/monthly reports, and building interactive dashboards that surface actionable insights. Passionate about ensuring data accuracy, identifying trends and anomalies, and supporting business teams with ad-hoc data requests.
Nagesh Karajgi Orchid College of Engineering and Technology
B.Tech · Artificial Intelligence and Data Science
N/A – June 30, 2026
Sales Performance Dashboard
June 27, 2026 – Present
Collected and organised sales data from multiple CSV sources (Excel/SQL exports), performed data cleaning and transformation to ensure accuracy and consistency. Built and maintained an interactive Power BI dashboard tracking revenue, profit margin, and product performance with real-time visibility into sales trends. Applied DAX to create calculated KPIs and implemented filters/slicers for dynamic data exploration by management teams. Prepared clear, concise reports and summaries that improved reporting efficiency by 40%, enabling data-driven decision-making.
Employee Attrition Dashboard
June 27, 2026 – Present
Collected and cleaned an HR dataset of 1,400+ employee records from Excel sources, identifying and resolving data inconsistencies to ensure data accuracy. Built and maintained an interactive Power BI dashboard with visuals covering attrition by job role, salary, tenure, department, gender, age group, and education. Created DAX-calculated measures to compute attrition rates and identify trends, patterns, and anomalies across departments. Supported HR team with ad-hoc data requests and dynamic slicers for self-service data exploration; maintained clear documentation of metrics and report definitions. Findings enabled HR to pinpoint high-risk departments, contributing to a 12% reduction in voluntary attrition through targeted retention initiatives.
Customer Churn Prediction (Machine Learning)
June 27, 2026 – Present
Collected and cleaned a telecom customer dataset of 7,000+ records, handling missing values, encoding categorical variables, and performing feature engineering to prepare data for modelling. Performed exploratory data analysis (EDA) to identify key trends and patterns — such as correlations between contract type, tenure, and churn rate - using matplotlib and seaborn visualizations. Built and evaluated multiple classification models (Logistic Regression, Decision Tree, Random Forest) using scikit-learn, comparing Accuracy, Precision, Recall, and F1-Score across models. Achieved 87% accuracy with the Random Forest model; identified top churn-driving features (contract type, monthly charges, tenure) to provide actionable business insights for retention strategy. Documented model results, feature importance findings, and recommendations in a structured report to communicate insights clearly to non-technical stakeholders.
Data Science
Udemy
June 1, 2026 – Present
Data Science Professional Certificate
Devtown
January 1, 2024 – Present
Python
Great Learning
January 1, 2023 – Present
SQL
Great Learning
January 1, 2023 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest in data analysis and machine learning, aligning well with a Data Analyst role. The diversity of projects (sales, HR, customer churn) shows adaptability and a broad application of data skills. However, the lack of professional experience and the academic nature of all projects suggest a need for mentorship and exposure to real-world business environments and team collaboration dynamics. The ongoing education and certifications indicate a proactive learning attitude.
Soft Skills & Operational Fit
The candidate's project descriptions highlight attention to detail in data cleaning, problem-solving in identifying and resolving inconsistencies, and a results-oriented approach (e.g., 40% improved reporting efficiency, 12% reduction in attrition). The ability to support HR teams with ad-hoc requests indicates responsiveness and client-facing skills. The documentation of model results and recommendations suggests good communication and organizational skills.