Sowjanya Hanumanla
Professional Summary
Data Analyst with 0 years of professional experience (6-month internship in data engineering and analytics). Experienced in Python, SQL and MySQL for data cleaning, transformation and ad-hoc analysis across business datasets. Applied Pandas and NumPy to preprocess data and wrote complex SQL queries using joins, CTEs and window functions to generate business reports. Built interactive dashboards and KPI reporting in Power BI and delivered exploratory visualizations using Matplotlib and Seaborn. Implemented end-to-end data workflows to load, validate and model sales and transactional data for e-commerce and retail analysis. Built classification and regression models with Scikit-learn for loan-prediction experiments and evaluated performance with cross-validation. Comfortable documenting pipelines and version-controlling analysis scripts in Git; seeking an entry-level Data Analyst or BI Analyst role to operationalize analytics across small-to-midsize datasets.
Technical Skills
Work Experience
- Cleaned and normalized raw business datasets using Python and Pandas to remove duplicates, impute missing values and standardize schema for reporting.
- Authored complex SQL queries with joins, CTEs and window functions to aggregate transactional and sales data for stakeholder reports.
- Implemented data transformation steps and loaded curated datasets into MySQL to maintain a single source of truth for analysis.
- Developed interactive KPI dashboards in Power BI to visualize sales trends, customer segments and product performance for business users.
- Created reusable Excel-based reports and SQL templates to accelerate monthly reporting and reduce manual effort in ad-hoc analyses.
- Maintained analysis scripts and version history using Git and documented data preparation steps to improve reproducibility of results.
- Validated datasets and implemented row-level data quality checks using Python to detect anomalies prior to dashboard refreshes.
Projects
- Developed an automated web-scraping pipeline using Selenium and BeautifulSoup to collect product metadata and pricing at regular intervals.
- Stored time-series price snapshots in MySQL and used Pandas to transform and prepare historical price tables for analysis.
- Built a Streamlit dashboard to visualize price history and alert on price drops, enabling quick identification of competitive price movements.
- Analyzed sales datasets with SQL and Python to identify trends in revenue, seasonality and high-performing SKUs.
- Created interactive Streamlit dashboards exposing KPIs and drill-downs for product and customer segments.
- Generated Excel pivot reports and visual summaries to enable business stakeholders to consume monthly performance metrics.
- Performed detailed retail sales analysis in MySQL using joins, CTEs and window functions to compute running totals and retention metrics.
- Optimized complex reporting queries to reduce runtime on large transaction tables through refactoring and indexed joins.
- Produced segmented revenue and customer lifetime value reports to inform merchandising and pricing decisions.
- Built classification models using Scikit-learn (Logistic Regression, Decision Tree, Random Forest) to predict loan approval.
- Conducted feature engineering and data preprocessing with Pandas and NumPy to improve model input quality.
- Evaluated model performance with cross-validation and confusion-matrix metrics to compare algorithms and tune hyperparameters.
Education
Certifications
Achievements
- Top Performer — Data Engineering & Analytics Internship — Apr–Jun 2026: Recognized for consistent performance and significant project contributions during the internship.
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