Udayasri Urimiti

Data Analyst with a Master’s in Data Science and proven experience in data wrangling, statistical analysis, and KPI tracking. Skilled at transforming raw data into actionable insights through SQL, Python, R, Excel, Tableau, and Power BI. Adept at developing interactive dashboards, automating reporting processes, and delivering data-driven recommendations that improve decision-making efficiency.

• Benchmarking NoSQL Databases in Kubernetes (2025) - Benchmarked Redis, Cassandra, and RocksDB using YCSB on an 8-node Kubernetes cluster
• Deployed Prometheus and Grafana for real-time monitoring; visualized latency and throughput in
• Python
• Containerized RocksDB with FastAPI for benchmarking and observability
• Uber Data Analysis Project (2023) - Predicted trip distances with 98% accuracy using LightGBM, KNN, and Random Forest
• Conducted data preprocessing, feature engineering, and pattern analysis in Python
• Fast Food Nutritional Data Analysis (2024) - Classified fast food items with 95% accuracy using R, caret, k-means, and decision trees
• Cleaned and visualized data to identify nutritional trends across chains
• Predictive Insights into Hotel Reservations (2024) - Built classification models (KNN, Logistic Regression, LDA, Naive Bayes) to predict guest behavior
• Achieved high accuracy using hotel booking datasets
• Database Systems Project: MySQL vs
• Neo4j (2024) - Designed schemas for user authentication and product recommendation
• Compared MySQL's reliability with Neo4j's relationship management for e-commerce
• Customer Segmentation Using RFM and Clustering (2024) - Performed RFM analysis using KMeans, DBSCAN, and GMM
• Developed an interactive GUI on Hugging Face Spaces for segment-wise insights
• Certifications - Azure Data Fundamentals (DP-900) - Google Data Analytics - Coursera - Tableau & Power BI - LinkedIn Learning - Data Science Specialization - Coursera (Completed 2024)