About
Motivated and detail-oriented fresher with a strong foundation in SQL, Python, Pandas, Power BI, Tableau, and Excel, seeking a Data Analyst role where I can apply my analytical and data visualization skills to deliver actionable business insights. Eager to contribute to data-driven decision-making and grow within a dynamic organization.
Skills & Expertise (37)
Work Experience
Data Analyst
Retail Store
Present - Present
Analyzed 10,000+ retail sales records using SQL — wrote complex queries with CTEs and window functions to identify top-performing products, regional profit margins, and monthly sales trends, delivering structured business insights to support decision-making. Performed data cleaning and exploratory data analysis using Python and Pandas — handled missing values, standardized date formats, removed duplicates, and applied RFM customer segmentation to identify high-value customer groups. Built an interactive Power BI dashboard tracking KPIs including Total Revenue, Profit Margin, and Month-over-Month Growth — added Region, Category, and Date slicers enabling stakeholders to drill down into performance data and make faster business decisions.
Data Analyst
Employee Attrition
Present - Present
Analyzed employee attrition data using SQL — queried attrition rates by department, job role, and salary band using GROUP BY, HAVING, and window functions to identify high-risk employee segments and root causes of attrition. Cleaned and processed HR dataset using Python and Pandas — handled missing values, encoded categorical variables, and calculated department-wise attrition rates using groupby aggregations to prepare clean data for reporting. Built an HR Analytics dashboard in Power BI using DAX measures to track Attrition Rate, Average Salary, and Headcount by Department — enabled HR leadership to identify high-attrition departments and take targeted retention actions.
Data Analyst
Heart Disease Prediction
Mar 2022 - May 2022
Collected and cleaned a healthcare dataset using Python and Pandas — handled missing values, outliers, and standardized features for model readiness. Applied feature selection techniques including backward elimination and RFECV to identify the most significant predictors of heart disease. Built a Logistic Regression classification model achieving 85% prediction accuracy, evaluated using confusion matrix, precision, recall, and F1-score. Visualized results using Matplotlib and Seaborn to communicate findings clearly.
Education
Bachelor of Technology — Electronics and Computer Engineering - Sreenidhi Institute of Science and Technology
2018 - 2022 · Afghanistan
Certifications
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Skills (37)
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