Uma Mahesh
Aspiring Data Analyst • u************@gmail.com • 939****494 • linkedin.com/•••••
Career Objective
B.Tech Chemical Engineering graduate (CGPA 8.16) with hands-on project experience in SQL database design, Python-based EDA, web scraping, and Power BI dashboarding. Demonstrated ability to design normalized relational schemas, clean and analyze datasets (6K+ records), build interactive BI dashboards (8.5K+ items, ₹1.2M sales), and extract actionable business insights. Seeking an entry-level Data Analyst role to apply strong SQL, Python, Power BI and visualization skills to drive data-driven decisions.
Education
Chaitanya Bharathi Institute of Technology
Bachelor of Technology in Chemical Engineering • 2019 – 2023
Narayana College
TSBIE • 2019
TVR Model High School
SSC • 2017
Technical Skills
Programming Languages: Python
Frameworks and Libraries: Pandas,NumPy,Matplotlib,Seaborn
Databases: SQL,MySQL,Relational database design,Normalization,Referential integrity,Star schema design
Data and Analytics: Data cleaning,Plotly,Power BI,Excel
Business Intelligence & Reporting: Power BI Desktop,DAX,Dashboarding,Interactive reports
Web Scraping & ETL: BeautifulSoup,Requests,Regex
Statistical Analysis & EDA: Exploratory data analysis,Correlation analysis
Projects
Employee Management System – SQL Project
Tools Used: MySQL, SQL, Relational database design, Normalization
- Designed a relational database schema for employee, department, qualification and payroll modules enforcing 100% referential integrity to prevent orphaned records and ensure data accuracy.
- Implemented a fully normalized schema to support scalable storage and efficient querying for 10,000+ personnel, reducing data redundancy and improving update performance.
- Authored advanced SQL queries and stored procedures to compute payroll aggregates, employee tenure metrics and qualification-based filters for HR analytics.
- Prepared data views and exportable result sets to enable integration with BI dashboards and downstream reporting tools for HR and payroll stakeholders.
Foodpanda Exploratory Data Analysis (EDA) using Python
Tools Used: Python, Pandas, NumPy, Matplotlib, Seaborn
- Performed EDA on 6,000 Foodpanda order records across 20 features covering customer, restaurant and delivery variables to identify demand patterns and operational bottlenecks.
- Executed data cleaning and preprocessing using Pandas and NumPy (missing value handling, type conversions, outlier detection) to ensure analysis reliability.
- Created visualizations to reveal top food categories, peak order timings and city-level trends, supporting data-driven recommendations for marketing and logistics.
- Delivered actionable insights on restaurant performance and user behavior that can inform menu promotions and delivery allocation strategies.
Power BI Project – Blinkit Sales Dashboard
Tools Used: Power BI, DAX, Star schema, Dashboarding, Excel
- Designed and deployed an interactive Power BI dashboard analyzing 8.5K+ grocery items and ₹1.2M+ in sales to surface category and outlet performance.
- Built a star schema (1 fact, 4 dimension tables) to optimize reporting performance and enable slice-and-dice analysis across time, category and store tiers.
- Developed 10+ DAX measures and calculated columns (e.g., Sales per Kg, Years of Operation) to support custom KPIs and comparative analysis.
- Delivered insights on top-performing categories, tier-wise outlet efficiency and health-conscious product trends to support merchandising and assortment decisions.
Electric Vehicle Data Analysis: From Web Scraping to Visualization
Tools Used: BeautifulSoup, Requests, Python, Pandas, Plotly, Matplotlib, Seaborn, Regex
- Scraped 1,146 Electric Vehicle records from automotive websites using Requests and BeautifulSoup, transforming unstructured HTML into a structured dataset for analysis.
- Performed extensive data cleaning with Pandas and Regex to standardize specifications and remove inconsistencies prior to analysis.
- Conducted EDA to identify relationships between battery capacity, driving range, price efficiency and charging speed, observing a strong correlation (r ≈ 0.96) between battery capacity and range.
- Built interactive visualizations with Plotly and static plots with Matplotlib/Seaborn to communicate insights on EV efficiency, cost per km and market trends.
Certifications
Introduction to MS Excel — Simplilearn
Python Programming Certification (Gold) — Innomatics Research Labs
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