May 2025 monthly summary for HWTeng-Teaching/202502-Financial-Econometrics: Substantial content updates and documentation enhancements across Chapters 10, 11, and 15, plus a full repository bootstrap. Focused on delivering accurate, well-formatted Markdown materials and preparing the material for scale and ongoing maintenance.

April 2025 delivered substantial documentation and content enhancements for the Financial Econometrics course repository. Key features include the initial creation of essential Markdown Question files across Chapters 5, 8, and 10 (Ch05_Q06.md, Ch05_Q31.md, Ch05_Q33.md, Ch08_Q06.md, Ch08_Q16.md, Ch08_Q18.md, Ch10_Q02.md, Ch10_Q03.md and related Q files), plus extensive content updates and documentation updates to improve clarity, alignment with chapter goals, and learner guidance (Ch05_Q06/31/33 docs; Ch08_Q06/16/18 docs; Ch10_Q02/03). Chapter 8 and Chapter 10 materials were expanded with uploads and batch updates, broadening the repository's question coverage (Q06, Q16, Q18; Q02, Q03; Q18, Q20, Q24). A notable bug fix involved cleaning up obsolete material by deleting the Apple HW0414_Q3 file, enhancing repo hygiene. These efforts deliver tangible business value: richer, easier-to-navigate learning materials; improved consistency across chapters; and a scalable foundation for ongoing curriculum updates. Technologies and skills demonstrated include Markdown authoring at scale, disciplined Git usage with traceable commits, batch/documentation updates, and effective repository resource management.

Month: 2025-03. Focused on building a scalable, high-quality documentation and repository hygiene baseline for HWTeng-Teaching/202502-Financial-Econometrics. Key features delivered include the creation and iterative refinement of Chapter 2 Question 1 documentation (Ch02_Q1.md) with an exemplar Ch02_Q1_e.py, plus numerous updates to the Q1 documentation. Established standardized Chapter 2 questions: created and updated Ch02_Q14.md (renamed from Ch02_14.md to Ch02_Q14.md), created Ch02_Q16.md, and expanded Ch02_Q17.md and Ch02_Q25.md, Ch02_Q28.md with extensive content improvements. Built Chapter 3/Chapter 4/Chapter 5 documentation scaffolding and content updates (Ch03_Q01.md, Ch03_Q07.md, Ch03_Q17.md, Ch03_Q19.md; Ch04_Q04.md, Ch04_Q28.md, Ch04_Q29.md; Ch05_Q03.md, Ch05_Q23.md) including file renames and formatting enhancements. Major bugs fixed and cleanup included removal of obsolete placeholders and directories (e.g., 313707029_YuChi Chang; 313707027_Apple HW directories) and multiple content corrections (Ch04_Q04/Q28/Q29; Q14/Q16/Q17 docs). Overall impact: improved documentation quality, consistency, and maintainability across chapters 2–5, enabling faster onboarding and future updates. Technologies/skills demonstrated: Git-based batch updates, Markdown documentation standardization, cross-document referencing, Python exemplar maintenance (Ch02_Q1_e.py), and proactive repository hygiene.

February 2025 — HWTeng-Teaching/202502-Financial-Econometrics: Established a solid documentation baseline and fixed README metadata to improve onboarding, transparency, and maintenance. Delivered documentation scaffolding with an initial README and visuals, and corrected hyperlinks and metadata across the README to ensure accuracy and traceability. These efforts reduce support overhead, accelerate new contributor onboarding, and set a repeatable docs pattern for future releases.

December 2024: Key features delivered include NVIDIA stock data analysis notebooks with a structured workflow, and NVIDIA stock prediction project documentation and presentation materials. Major bugs fixed: cleaned project organization by removing deprecated notebooks and updating links and README to prevent drift. Overall impact: enhanced maintainability, reproducibility, and readiness for stakeholder review; improved onboarding for new analysts. Technologies and skills demonstrated: Python, Jupyter notebooks, data loading/cleaning, EDA, technical indicators, Git-based project hygiene, README and PPTX management.

November 2024 monthly summary for HWTeng-Teaching/202409-ML-FinTech: Delivered a reproducible ML experimentation suite and onboarding materials. Key features include notebooks for regression analyses across weekly stock data, Auto MPG, and Boston housing, with model evaluations and feature explorations. Documentation skeletons and educational assets were created to support teaching and learning. No major bugs reported this month. Business value: enables faster experimentation, better learning resources, and improved educational outcomes; technical achievements include end-to-end notebook work, dataset coverage, and documentation assets.

In Oct 2024, delivered a focused ML analytics package for the Weekly dataset and strengthened project maintainability. The work enabled experimental evaluation of stock-direction predictions using multiple ML models, removed legacy artifacts to reduce technical debt, and established documentation scaffolding with reference assets to support onboarding and future work.