My name is Rucheng Zhou. I hold a Master of Analytics from the University of South California, where I gained a deep understanding of data analysis, machine learning, and statistical modeling.
My Bachelor's degree in Applied Mathematics, with a minor in Statistics and Studio Arts, from the University of Rochester, has equipped me with a strong quantitative foundation. What excites me most about data science is its power to transform raw data into actionable insights.
I am passionate about leveraging data to solve complex problems, optimize processes, and predicate behaviors. I believe that data has the potential to revolutionize industries and create positive impacts, and I'm committed to being at the forefront of this transformation.
Led a team of 3 to develop “Hui Understand You”, a digital microloan platform, improving user engagement and loan approval rate by 26.7%. Responsible for an end-to-end automated user financial behavior analysis system, enhancing risk assessment efficiency.
During a four-month internship, gained practical experience in data analysis within the financial sector. Engaged in hands-on data work, contributing to projects that required precise data gathering, processing, and interpretation in a dynamic fund management setting.
A graduate program focused on developing expertise in data analysis, predictive analytics, and data science. Gained proficiency in advanced analytical tools and techniques, with a strong foundation in statistical analysis and machine learning.
Completed a rigorous undergraduate program with a minor in Statistics and Studio Arts. Developed strong quantitative and analytical skills, and engaged in various interdisciplinary projects that combined mathematical concepts with artistic design.
Coursistant is an innovative AI-driven platform designed to transform educational interactions. Tailored for specific academic fields, it offers customizable Q&A environments, making it perfect for students and experts alike. The platform ensures that all inquiries receive accurate and current responses by allowing educators to easily update and manage content. This tool not only enhances learning but also supports complex research needs, positioning it as an essential educational technology in both classroom settings and professional research environments.
View ProjectOptimized product recommendations through AB Testing and UI Enhancements. This project involved comprehensive data analysis, development of UI prototypes, and employment of rigorous statistical methodologies to deliver actionable results. It included the creation of a Logistic Regression model and an end-to-end UI change implementation process.
View ProjectLed the development of a news recommendation system by analyzing user history for click prediction. This project utilized Python for data mining of large user datasets and incorporated machine learning techniques like itemcf, YoutubeDNN, and advanced feature engineering for enhancing click predictions.
View ProjectParticipated in an online modeling competition to predict earthquake damage. This included feature selection and cleaning of a significant dataset, and modeling using ensemble methods such as random forest and Catboost to achieve high accuracy.
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