Data Analytics Science

Yogendra Sai Pavan Nalam

Dearborn, MI ********@*****.*** 313-***-**** https://www.linkedin.com/in/yogendra-sai-pavan-nalam/ EDUCATION

University of Michigan - Dearborn Dearborn, MI

Master of Science in Data Science, GPA: 3.56/4.0 August 2023 - April 2025 Relevant Coursework: Database Systems, Intelligent Systems, Big Data Analytics and Visualization, Artificial Intelligence, Models of Oper Research, Data Mining, Pat Rec & Neural Networks, Applied Regression Analysis, Applied Data Analytics and Modeling for Enterprise Systems, Intro to Big Data. Vellore Institute of Technology- Vellore, India

B.Tech in Computer Science and Engineering, SPL in Bioinformatics, GPA: 3.40/4.0 July 2019 – May 2023 EXPERIENCE

University of Michigan-Dearborn Michigan, USA

Graduate Teaching Assistant September 2024 - April 2025

• Implemented data-driven feedback loops for 131 students across senior design, using performance metrics to personalize guidance on architectural patterns and software design, leading to tangible improvements in project quality.

• Led weekly peer reviews, enhancing code quality and SOLID adherence, boosting scores by 25 points.

• Engineered data validation protocols incorporating statistical anomaly detection and data integrity checks, improving data quality scores by 40% and reducing data corruption incidents by 16 weekly. Quantum Pulse Consulting Michigan, USA

Data Science Intern June 2024 - September 2024

• Integrated a Power BI dashboard with the company’s REST API, providing seamless data access for client website users and reducing data retrieval bottlenecks by 20%.

• Integrated the dashboard with a REST API, enabling seamless access to analytics via the client’s website.

• Designed and implemented a multi-dashboard website for 100+ users, boosting workflow efficiency by 40%.

• Implemented a machine learning model with 90% accuracy for financial market analysis, enhancing strategic decision-making.

• Built a Retrieval-Augmented Generation (RAG) pipeline using LLMs to enhance document comprehension. University of Michigan-Dearborn Michigan, USA

Teaching Assistant June 2024 - July 2024

• Conducted weekly lab sessions for 33 students, teaching coding in HTML, CSS, JavaScript, Node.js, Express, and Vue.js.

• Led 16 lab activities to enhance coding skills and guide students in building interactive, user-friendly web pages. SKILLS

Data Analysis Techniques: Regression Analysis, Big Data Analytics, Data Mining, Predictive Modeling, Data Cleaning and Wrangling (Pandas, Numpy), Data Visualization, Statistical Analysis, Machine Learning, Generative AI, Prompt Engineering, n8n, Model Context Protocol (MCP)

Programming Languages:Python (Proficient), R (Proficient), SQL (Proficient), C, C++ Java, Shell Scripting Software and Tools: AWS, GCP, SAP, Advanced Excel, GitHub, Tableau, Power BI, Databricks, MS Excel, Caffe, PySpark CERTIFICATIONS

Microsoft Power BI Data Analyst Professional Certificate(PL 300) Microsoft AI & ML Engineering Professional Certificate ACADEMIC PROJECTS

Real-Time Traffic Sign Recognition Using YOLO v11 and CNN September 2024 - December 2024

• Achieved 98.7% top-1 accuracy using YOLO v11 on the GTSDB dataset of 39,209 images with optimized data augmentation.

• Designed and trained a custom CNN with TensorFlow for classifying 43 traffic sign classes.

• Conducted comparative analysis with confusion matrices to evaluate model accuracy and misclassification rates.

• Demonstrated YOLO v11’s suitability for real-time traffic sign recognition applications. Classification of Distributed Denial of Service Attacks Using ML Models August 2023 - December 2023

• Identified critical indicators, including packet count, protocol usage, duration, and source IP, enhancing prediction accuracy of malicious DDoS attacks by 45% and reducing false positives by 30%.

• Fortified network security by engineering Random Forest models, reaching 99.8% accuracy in distinguishing malicious attacks from benign traffic, and diminished potential security breaches by 60%.

• Automated a data pipeline using Apache Airflow, which integrated and preprocessed network traffic data, leading to a 25% reduction in data preparation time for security analysts on the team. Heart Disease Prediction Using Machine Learning January 2023 - April 2023

• Created a real-time heart disease prediction system with 85.8% accuracy using Logistic Regression, Random Forest, and KNN on patient data from Sri Ramachandra Hospital.

• Analyzed a dataset with 12 attributes to identify critical risk factors for early heart disease diagnosis.

• Enhanced model efficiency via advanced attribute selection, improving heart disease diagnosis and patient care.

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