Data Science Machine Learning

Weiqian Peng

Mobile: 341-***-**** Email: ***********@*****.***

Linkedin Website: https://www.linkedin.com/in/weiqian-peng-46b249246/ Personal Website: https://qianzzzzz.github.io/Mypage/ EDUCATION

University of California, Berkeley

Bachelor of Science in Data Science Expected June 2024 Domain Emphasis: Economics

Relevant Coursework: Data Engineering, Data Mining & Analytics, Data Inference and Decisions, Data Science of Economists SKILLS

Programming language: Python, SQL, Scheme, Java, R, Html, CSS Technical skills: VSCode, IntelliJ IDEA, Jupyter Notebook, RStudio, Microsoft Office Suite, Data visualization, MySQL, SQLite, MongoDB, Tableau, Database Management, Machine Learning Algorithms, Data Mining Techniques, Power BI, Pytorch Other Spoken Languages: Mandarin (Fluent), Cantonese (Fluent), Korean (Basic) WORK EXPERIENCE

UC Berkeley Haas School of Business Berkeley, CA

Student Assistant; Research and Data Analysis September 2023 – Now

• Conducted comprehensive data analyses, uncovering business insights that improved research project efficiency by 25%.

• Cultivated a comprehensive data processing framework using Python and R, significantly enhancing project turnaround time. Demonstrated effective teamwork, autonomy, and responsiveness, contributing to successful project completion. Guangdong Jingxin Data Technology Co., LTD Guangdong, China Data Science Intern May 2023 – July 2023

• Improved database efficiency across multiple sectors using Python, SQL, and Tableau. Established data governance and standardization protocols to ensure consistency and accuracy in client data management.

• Secured database integrity by innovating backend management practices, which reduced data breaches by 30%. Music Land school of music Fremont, CA

Data Analyst Assistant November 2020 – May 2023

• Developed and maintained databases, ensuring data integrity and consistency. Presented data-driven reports and visualizations to stakeholders, improving operational efficiency and strategic planning for school events.

• Optimized data system performance, enhancing data accuracy by 50% through data validation processes and performance tuning. PROJECTS

Project: Replication Study of Civil War Exposure and Violence April 2024

• Conducted a replication study of Miguel et al. (2011), analyzing the relationship between civil conflict exposure and aggressive behavior in professional soccer players. Reproduced the original Stata code using Python, Employed a Negative Binomial Regression Model to analyze count data.

Project: NBA team performance and player salaries prediction December 2023

• Developed a Python-based predictive model to analyze NBA player salaries and team performance using machine learning, uncovering key correlations between player acquisition and team success. Achieved 90% prediction accuracy by applying model evaluation techniques to identify critical features. Project: AI-Generated Text Detection and Analysis December 2023

• Employed text processing techniques on a dataset of AI-influenced articles, focusing on artificial modified AI-generated sentences. Developed and trained a RandomForestClassifier model to differentiate humans from AI-generated texts.

• Analyzed lexical diversity, grammar errors, and word frequencies to identify distinctive characteristics of AI-generated text. Project: Titanic - Machine Learning from Disaster - Kaggle October 2023

• Developed a GradientBoostingClassifier model for Titanic survival prediction, achieving 85% accuracy and a top-five finish among 500 participants, demonstrating strong data science skills.

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