Entry-Level Data Analyst with Power BI, Python, and SQL skills
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Aspiring Data Analyst with a solid command of the modern data stack — Python (Pandas, NumPy), SQL, Power BI, and Excel. Capable of end-to-end data workflows: from data extraction and transformation to visualization and reporting. Passionate about uncovering patterns in data and translating them into clear, business-ready insights. Ready to contribute to data-driven teams as a dedicated entry-level analyst.
T.B.M.L. Arts and Science College
Bachelor of Computer Applications (BCA)
August 1, 2022 – June 30, 2025
Swift Route Logistics Analysis Dashboard
January 1, 2025 – June 1, 2026
Built a 4-page interactive Power BI dashboard for a logistics company tracking end-to-end delivery operations. Visualized key KPIs — Total Orders (27,979), On-Time Delivery Rate, CSAT %, and Average Delivery Time with Month-over-Month comparison. Designed Drivers Overview page analyzing delayed delivery rates across 55 drivers and experience vs. performance ratings. Created Hubs Overview with hub capacity vs. orders processed and a heat map showing processing hours across 6 hubs (Mon–Sun). Built Vehicles Overview tracking 45 vehicles — active vs. maintenance status, breakdown by vehicle model, and orders by vehicle type. Implemented dynamic filters (Year & Month slicers) and page navigation buttons for seamless user experience.
Super Store Sales & Profit Dashboard
January 1, 2025 – June 1, 2026
Built a 2-page interactive Power BI dashboard on 5,000+ retail orders spanning 2023–2026, analyzing $2.33M in sales, $292.3K profit, and 39K units across 3 categories, 4 regions, and 50 US states. Designed 8 DAX measures including RANKX for Top-10 state rankings, SAMEPERIODLASTYEAR for YoY growth, CALCULATE for segment-level filtering, and DIVIDE for profit margin (12.56%) and return rate calculations. Built a Star Schema data model in Power Query — cleaned nulls, cast data types, created a Date dimension table, and established 1:Many relationships between Orders, Returns, and Date tables for optimized DAX performance. Designed Page 1 with Sales Overview visuals: KPI cards, treemap (3 categories × sub-categories), segment donut, region bar chart, ship-mode distribution, and monthly sales line chart — with 3 interactive slicers (Category, Region, Returned). Built Page 2 as a Regional & Customer Drill-through: geographic map (US states), Top-10 states by Sales and Profit, monthly profit bar chart, and a granular order-level detail table with Order ID, Customer, Product, State, and Returned flag. Uncovered that Technology category contributes 43% of total revenue, the West region leads all 4 regions, California tops both sales (~$0.50M) and profit (~$50K), and 60.04% of orders ship via Standard Class.
AI Job Market Analysis Dashboard
January 1, 2025 – June 1, 2026
Built an AI Job Market Analysis Dashboard in Power BI analyzing 2,000 AI job records across 8 job roles, 2,000+ companies, and global locations to identify hiring trends and salary benchmarks. Preprocessed raw data using Python (Pandas & NumPy) — handled missing values, standardized categorical fields (experience_level encoding), performed feature engineering (salary bands, remote ratio flags), and exported clean CSV for Power BI ingestion. Developed DAX measures including RANKX for role ranking, CALCULATE for experience-level segmentation, and DIVIDE for percentage distributions; modeled data using a Star Schema for optimized query performance. Designed interactive visuals: KPI cards, horizontal bar charts, donut charts, geographic map, and column charts — with 4 slicers (Job Title, Location, Company, Experience Level) enabling multi-dimensional filtering. Uncovered that Data Analyst is the #1 demanded AI role (271 openings), 35.1% of jobs target Entry Level candidates, and the market average salary is $171K — enabling job seekers to prioritize high-opportunity roles.
View ProjectInternship Certificate
A1Technoskill
June 1, 2026 – Present
Self-learning in Data Analyst, Excel, and Visualization
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a proactive, self-driven learning attitude, which is a positive indicator for cultural fit in a data-driven environment. The diversity of projects (logistics, retail, job market analysis) shows adaptability and a broad interest in applying data analysis across different domains. The focus on practical, dashboard-driven outcomes aligns well with roles requiring tangible business impact. However, the lack of team-based projects or professional experience makes it challenging to assess collaboration and interpersonal skills.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a detail-oriented approach to data analysis and a focus on delivering clear, interactive dashboards. The ability to work on end-to-end data workflows suggests good problem-solving and project management skills for an entry-level role. However, without direct work experience or psychometric test results, it's difficult to fully assess stress handling, team collaboration, or broader operational fit.