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Data Analyst with less than a year in data solutions and business insights.
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Analytical and detail-oriented Data Analyst with a strong foundation in mechanical engineering and hands-on expertise in Python (Pandas, NumPy, Matplotlib, Seaborn, SciPy), SQL (MySQL, Oracle), and Power BI, delivering end-to-end data solutions from raw ingestion to executive-ready dashboards. Proven ability to design scalable data pipelines, apply statistical hypothesis testing, and extract actionable business insights from complex datasets exceeding 5,000 records. Seeking an entry-level Data Analyst role to drive data-backed decision-making and operational improvements across business functions.
KGISL MicroCollege
Data Analytics and Data Science · Data Analytics and Data Science
January 1, 2025 – Present
Bannari Amman Institute of Technology
Bachelor of Engineering · Mechanical Engineering
January 1, 2020 – January 1, 2024
Books Data Analysis
June 21, 2026 – Present
Automated the collection of 1,000-plus book records by engineering a Python web scraping pipeline using BeautifulSoup and Requests, eliminating approximately 8 hours of manual data gathering per data refresh cycle. Improved dataset completeness from approximately 88 to 100 percent by executing a systematic data cleaning workflow using Pandas, including stripping currency symbols, converting star-rating text fields to numeric values, and resolving 8 to 12 percent missing values to produce a fully analysis-ready dataset. Disproved the assumption that higher-priced books receive better reader ratings by conducting Exploratory Data Analysis using Pandas and SciPy, revealing a near-zero correlation between price and rating (Pearson r of approximately 0.08 with a p-value greater than 0.05), providing a statistically supported insight for pricing strategy. Confirmed no statistically significant price difference between high-rated and low-rated book segments by executing an Independent T-test in SciPy, delivering a data-backed conclusion that validates reader-value-driven pricing over premium-tier assumptions. Enabled structured querying across price bands, genres, and stock availability by designing a normalized MySQL schema with clearly defined entity relationships and importing the cleaned 1,000-plus record dataset, reducing ad-hoc query complexity by standardizing data access patterns. Accelerated executive-level trend identification across 20-plus book categories by building a Power BI dashboard featuring pricing distribution charts, genre-level rating heatmaps, and stock availability indicators powered by DAX measures, reducing insight retrieval time from hours to under 5 minutes.
View ProjectCustomer Behavior and Business Intelligence Analytics Dashboard
June 21, 2026 – Present
Delivered a comprehensive end-to-end Business Intelligence solution for an e-commerce dataset of 5,000-plus transaction records by architecting a relational MySQL data model integrating purchase history, customer demographics, and subscription data to evaluate consumer purchasing behavior and revenue drivers. Reduced KPI extraction time by approximately 60 percent by developing optimized MySQL scripts leveraging Window Functions, Common Table Expressions (CTEs), and conditional aggregations to compute metrics including average order value, purchase frequency, churn rate, and subscription impact on revenue. Increased targeted retention campaign precision by engineering a dynamic behavioral customer segmentation model using Python (Pandas) and SQL, classifying 5,000-plus users into New, Returning, and Loyal tiers based on historical purchase frequency, enabling marketing teams to personalize outreach for each customer lifecycle stage. Empowered executive stakeholders to monitor real-time revenue contributions by building a Power BI dashboard with a relational data model and advanced DAX measures, visualizing revenue trends across complex age cohorts and gender demographics and reducing manual reporting effort by approximately 70 percent per review cycle.
View ProjectData Analytics and Data Science Professional Certificate
KGISL MicroCollege
June 1, 2026 – Present
Data Analytics Job Simulation
Deloitte
June 1, 2026 – Present
Complete Guide to Power BI for Data Analysts by Microsoft Press
LinkedIn Learning
June 1, 2026 – Present
Python 101 for Data Science
IBM SkillsBuild
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, covering book data analysis and e-commerce customer behavior, shows a broad interest in applying data analytics across different domains. The explicit mention of delivering insights for 'pricing strategy' and 'targeted retention campaigns' indicates a business-oriented approach, aligning well with roles that require direct impact on business decisions. The pursuit of a Data Analytics and Data Science degree and multiple certifications demonstrates a proactive learning attitude and commitment to the field.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical thinking, problem-solving skills, and attention to detail through their project descriptions. Their ability to automate tasks, improve data completeness, and reduce manual effort indicates an operational mindset focused on efficiency. The projects also highlight an ability to translate complex data into actionable business insights for stakeholders.