data analysis

Nianchao Wang

Tel/Wechat:1-899-***-**** Email:*********@***.***

Education

2023.09-2026.04 Nanjing University of Aeronautics and Astronautics Master in Industrial Engineering and Management (Data Mining & Big Data Analytics) Core Courses:Modern Information Management & Big Data, Advanced Econometrics, Statistics, Forecasting & Decision Making●

2018.09-2022.06 Xi’an University of Technology

Bachelor in Industrial Engineering

Core Courses: Database Principles & Design, Statistics, Operations Research, Supply Chain Management, Technical Skills

●Programming Languages Proficient in Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn); skilled in MySQL

●Data Analysis A/B testing, attribution analysis, funnel analysis, and other core analytical methods

●Supply Chain Hands-on experience in procurement data analysis, supplier risk assessment, production plan monitoring, and ERP data maintenance

● AI Complete machine learning modeling pipeline with Scikit-learn (e.g., logistic regression); NLP project experience; understanding of Transformer/BERT applications; strong background in statistics and mathematics.AI, as the 4th Industrial Revolution – is fundamentally about productivity restructuring. I can rationally distinguish the capabilities of traditional ML vs. LLMs and choose the right technique for real-world business problems, rather than merely listing buzzwords.

●Certifications CDA Level 1 Data Analyst Certification

●Personal Profile Strong English proficiency (passed CET-6 with 576 as a freshman); MBTI: ESTJ – good at planning, strong execution and organizational skills

Internship Experience

● 2025.6-2025.12 ERIGIN - Product Operations (Data Analytics) a)Procurement Data Analysis: Extracted and integrated data using SQL, performed automated analysis with Python, generated supplier performance and cost alert reports to support procurement decisions. b)Supplier Risk Assessment: Designed an evaluation system based on historical delivery data (on-time rate, pass rate, etc.), quantified supplier risk using logistic regression, improving risk identification capability.

● 2024.8-2025.2 AVIC Suzhou Changfeng – Data Analyst a)Data Automation & Visualization: Used Python (Pandas) to automatically clean and integrate production data, queried and analyzed with SQL, and built dynamic monitoring dashboards in Tableau, improving data accessibility and decision efficiency (supporting production planning and material coordination). b)Production KPI Monitoring: Developed core metrics (e.g., production plan achievement rate) and set thresholds for anomaly detection.

● 2021.7-2021.8 Qinchuan Machine Tool & Tool Group Co., Ltd. – Production Data Analyst a)Analyzed production volume, labor hours, defect rate, etc., delivered a capacity analysis report, providing data-driven recommendations for production process optimization.

●2020.7-2020.8 Hutchinson Automotive Rubber Products Co., Ltd. – Supply Chain a)Maintained and validated ERP system data to ensure accurate linkage between demand and supply plans, supporting end-to- end supply chain processes.

Project Experience

● 2023.09-2026.3 Project Lead – Fake Review Detection Based on Positive and Unlabeled (PU) Overview Independently designed and implemented a weakly supervised detection system using Dianping (Chinese Yelp) data. Addressed the core challenge of scarce labeled data by adopting a PU learning framework, training the model with only a small set of high-confidence positive samples and a large amount of unlabeled data. Key Contributions

a. Data Construction & Preprocessing: Collected raw review data from public sources; filtered high-confidence positive samples (genuine reviews) using composite rules (user credibility, review pattern consistency), effectively mitigating the lack of labeled training data.

b. Feature Engineering & Modeling: Combined textual features (sentiment polarity, keywords, synactic complexity), user behavioral features (historical review count, rating distribution), and merchant features (average rating, category). Applied the classic two-step PU learning approach. Publication

● Research on Financing Decision of New Energy Vehicle Supply Chain with Manufacturer's Capital Constraint IEIM 2023 Rome, Italy January 9-11, 202

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