Welcome to my GitHub! As a passionate Data Science enthusiast and a Master of Science in Data Science candidate at the University of Arizona, I'm deeply engrossed in machine learning, AI, and transforming complex datasets into actionable insights. My projects, ranging from predictive analytics to advanced deep learning applications, mirror my academic and professional journey in data science.

• 🎓 Master of Science in Data Science, University of Arizona (2023-2024) - Department Topper with a GPA of 4.0/4.0.
• 🎓 Bachelor of Aeronautical Engineering, Anna University (2016) - University Gold Medalist among 1154 candidates.

• Curricular Analytics and AI Solutions: Spearheaded the use of Julia for data analysis, significantly enhancing the educational pathways for 50,000 students through in-depth curricular analytics. This work contributed to a marked increase in first-year retention rates from 68% to 78.3% at CECS, Washington State University, and boosted 6-year graduation rates from 15% to 52%.
• Google Earth Engine Project Management: Managed a Google Earth Engine account, facilitating efficient data handling and version control across 1,680 datasets. Developed standardized templates and implemented the STAC API, showcasing my technical prowess in managing large-scale data projects.

Jan 2022 - July 2023

• Environmental Data Science: Engineered an ARIMA-based forecasting model for CO2 emissions, achieving a 96% accuracy rate. This project was pivotal in aligning with UN Sustainable Development Goals and showcased my ability to apply time series analysis in environmental science.
• Healthcare Analytics: Developed a machine learning model to predict COVID-19 clinical trial outcomes, utilizing advanced NLP and text mining techniques. This not only demonstrated my interdisciplinary application of data science but also contributed to the pharmaceutical client’s strategic planning.
• Manufacturing Process Optimization: Applied neural networks to predict the energy yield of aircraft gas turbines, reducing mean absolute error by 15% compared to baseline models. This project highlighted my skills in deep learning and optimization for industrial applications.

Apr 2021 - Dec 2021

• Recommendation Systems: Designed and implemented a genre-based movie recommendation engine using Python, showcasing my abilities in web scraping, data management, and machine learning to enhance user experience.
• Automated Detection System Using Deep Learning: Developed an automated system for detecting holes and threads in manufacturing designs using YOLO, improving model generalization and precision in identifying specific components.

• Programming Languages: Proficient in Python, R, Julia, NVIDIA-CUDA.
• Libraries/Frameworks: Experienced with TensorFlow, Keras, PyTorch, scikit-learn.
• Tools & Platforms: Skilled in using Git, GitHub, VS Code, Jupyter Notebook, Tableau, PowerBI.
• Specializations: Machine Learning, Deep Learning, NLP, Time Series Analysis.

• Forecasting CO2 Emissions: Demonstrated proficiency in ARIMA models for environmental impact prediction.
• Clinical Trial Success Rate Prediction: Showcased my expertise in ML, NLP, and text mining to forecast clinical trial outcomes.
• Automated Detection System: Implemented YOLO for precision in manufacturing design detection.
• Awesome GEE Community Datasets: Curated a list of Google Earth Engine datasets to foster community sharing.
• Publications: Contributed to "samapriya/awesome-gee-community-datasets: Community Catalog (2.4.0)."

• LinkedIn: dhanyapriya-somasundaram
• Email: dhanyapriyas@arizona.edu
• GitHub: SaiDhanyaa
• ORCID: 0009-0000-7606-4623
• X: @dhanyapriyas

Beyond the realms of data science, I'm passionate about the intersection of technology and environmental sustainability. I aim to leverage AI in medical diagnostics and contribute to open-source projects to drive innovation and impact.

Thank you for exploring my profile! If you have questions or are interested in collaboration, don't hesitate to reach out.