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Data Analyst with 1+ years in Python, SQL & Power BI
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Results-driven Data Analyst with a B.Tech in Computer Science and hands-on experience in data analysis, data visualization, ETL pipelines, and predictive modeling. Proficient in Python, SQL, and Power BI for data cleaning, feature engineering, KPI tracking, and dashboard development. Skilled in machine learning (classification, regression, clustering) and deep learning techniques. Experienced in translating complex datasets into actionable business insights to drive data-driven decision-making.
Sri Venkateswara College of Engineering (JNTUA), Tirupati
B.Tech · Computer Science
August 1, 2021 – June 30, 2025
Govt Junior College (BIEAP), Chippagiri
Intermediate
June 1, 2018 – May 31, 2020
Z P High School, Chippagiri
10th
June 1, 2018 – May 31, 2018
FOARGE
Gen AI-Powered Analytics Intern
June 1, 2025 – July 1, 2025
India
Blinkit Sales Data Analysis - Python & Power BI
June 1, 2025 – June 1, 2026
Analyzed large-scale Blinkit sales dataset using Python (Pandas, NumPy) to uncover trends in customer behavior, product demand, and revenue patterns. Built interactive dashboards using Power BI to visualize KPIs such as sales performance, top-selling categories, and regional insights. Applied data cleaning and preprocessing techniques to handle missing values, outliers, and inconsistencies, improving data quality for accurate analysis. Generated actionable insights identifying high-performing products and optimizing inventory and pricing strategies.
IPL Analysis Dashboard (2008–2025) — Power BI
June 1, 2025 – July 1, 2025
Developed an interactive Power BI dashboard analyzing team and player performance KPIs across 17 IPL seasons. Visualized season winners, Orange/Purple Cap holders, top sixes/fours, and overall statistics using Power BI. Performed ETL, data wrangling, and data cleaning on Kaggle datasets using SQL, Python, Excel, and Power Query. Delivered AI-driven insights on scoring trends, dominant teams, and player consistency using statistical analysis.
RFM Analysis on Retail - Predictive Customer Segmentation
January 1, 2025 – April 1, 2025
Achieved 96% prediction accuracy forecasting customer segments using RFM analysis and machine learning models. Applied Python (Pandas, scikit-learn) for feature engineering, clustering, and classification to segment high-value customers. Developed predictive models to forecast future customer behavior and improve business targeting accuracy. Built visual dashboards and data visualizations to enable data-driven marketing decisions for business stakeholders.
Microsoft Excel
Simplilearn
July 1, 2025 – Present
Data Analytics in Power BI
Coursera
June 1, 2025 – Present
Introduction to SQL
edX
April 1, 2024 – Present
Python for Data Science
edX
March 1, 2024 – Present
Cultural Fit Analysis
The candidate's project portfolio shows a strong interest in diverse data analysis applications, from retail customer segmentation to sports analytics. This breadth of interest, combined with continuous learning through certifications, suggests a proactive and curious individual who could adapt well to various data challenges. The focus on delivering 'actionable insights' and 'data-backed business recommendations' aligns with a results-oriented culture. The candidate's academic background in Computer Science further strengthens their foundational understanding.
Soft Skills & Operational Fit
The candidate demonstrates a results-driven approach, as highlighted in the professional summary and project descriptions (e.g., 'Achieved 96% prediction accuracy'). The ability to translate complex datasets into actionable business insights suggests good analytical and problem-solving skills. The project diversity indicates a proactive learning attitude and self-motivation. However, with only one short internship, direct operational experience in a team setting is limited.