YUXUAN GENG

MSc in Data Science | Aspiring Data Analyst
Monash University
📍 Expected Graduation: Dec 2026
✉️ ygen0018@gmail.com
GitHub: https://github.com/ygen0018-sketch

---

About Me

I am a Master’s student in Data Science at Monash University, commencing in March 2025 with an expected graduation in December 2026.

I hold a Bachelor’s degree in Financial Management, which provides me with strong business and financial foundations.
I am primarily targeting Data Analyst roles, where data-driven insights support decision-making across diverse industries.

My interests focus on data analysis, exploratory analysis, and insight communication, with an emphasis on building solutions that are practical, interpretable, and broadly applicable.

---

Education

MSc in Data Science — Monash University
Mar 2025 – Dec 2026

Relevant Coursework:

• Statistical Analysis & Probability
• Data Analysis & Visualization
• Machine Learning Fundamentals
• Financial & Business Data Analysis
• Programming for Data Science

BSc in Financial Management — Shandong Youth University of Political Science
Sep 2018 – Jun 2022

Relevant Coursework:

• Financial Accounting
• Corporate Finance
• Economics
• Business Analysis

---

Technical Skills

Programming & Querying

• Python (pandas, NumPy, matplotlib, seaborn)
• SQL
• R (basic)

Data Analysis

• Data Cleaning & Wrangling
• Exploratory Data Analysis (EDA)
• Data Visualization & Reporting
• Regression & Classification (basic)
• Time Series Analysis (introductory)

Tools

• Git & GitHub
• Jupyter Notebook
• Excel
• Power BI / Tableau (if applicable)

---

Projects

📊 Data Cleaning & Quality Assessment on Real-World Dataset

Description:
Performed end-to-end data cleaning and preprocessing on a real-world dataset with significant data quality issues to support reliable downstream analysis.

Key Contributions:

• Identified and handled missing values, outliers, and inconsistent formats
• Designed data validation rules to improve data reliability
• Evaluated how different cleaning strategies affected analytical outcomes

Innovation / Highlight:

• Treated data cleaning as an analytical decision-making process by assessing how data quality choices influenced final insights, rather than applying fixed rules mechanically.

---

📱 Social Media–Based Anxiety Index Prediction (Kaggle)

Description:
Developed a data-driven approach to predict anxiety levels using social media data, focusing on interpretable analytical insights.

Key Contributions:

• Cleaned and processed large-scale social media datasets
• Conducted exploratory data analysis to identify behavioral and textual patterns
• Built and evaluated predictive models for anxiety index estimation
• Interpreted key features contributing to anxiety-related signals

Innovation / Highlight:

• Framed the task as an interpretable anxiety index prediction problem, emphasizing feature relevance and real-world applicability rather than purely optimizing model accuracy.

---

🧠 Enhancing Adolescent Mental Health Insights Using Social Media Data

Description:
Analyzed social media information to explore population-level patterns related to adolescent mental health and well-being.

Key Contributions:

• Transformed unstructured social media data into structured analytical features
• Identified trends and risk-related patterns at the population level
• Applied data analysis techniques to support insight generation and awareness

Innovation / Highlight:

• Shifted the focus from individual-level prediction to population-level pattern analysis, enabling insights that support early intervention strategies rather than diagnosis.

---

Career Focus

I am primarily interested in Data Analyst roles, including:

• Data Analyst
• Business Data Analyst
• Financial Data Analyst
• Analytics / Insights Analyst

I value roles that emphasize practical data analysis, cross-domain applicability, and business impact.