A portfolio of exploratory data analysis (EDA) projects spanning Excel, Python, and SQL, covering a range of real-world and synthetic datasets. Each project includes data cleaning, analysis, and visual communication through infographics or notebooks.
Folder: Excel EDA Libby Borrowing History Between 2020 and 2025
An EDA assignment for INFO 101 at Victoria University of Wellington (worth 35% of overall grade). Analyses augmented data from a reader's Libby borrowing history between 2020 and 2025, sourced from Wellington City Library and the Lower North Island library.
Core question: What story does this Libby audiobook borrowing history tell, and how can it be communicated through data visualisation?
Tools: Excel, Canva
Folder: Python EDA Netflix Movies and TV Shows
Explores Netflix's content catalog to uncover patterns in genre distribution, vote engagement, and language representation.
Core questions:
- Which genres dominate Netflix's catalog?
- How does vote engagement vary across content types?
- What languages are most represented on the platform?
Dataset: Netflix Movies and TV Shows — Kaggle
Tools: Python (Pandas, NumPy, Matplotlib, Seaborn), Jupyter Notebook, Canva
Folder: Python EDA Starbucks Customer Ordering Patterns
Analyses 100,000 Starbucks transactions from 2024–2025 to uncover ordering patterns across digital and physical channels by product, time, and customer behaviour.
Core question: How do Starbucks customers order across digital and physical channels, and what patterns emerge by product, time, and behaviour?
Dataset: Starbucks Customer Ordering Patterns — Kaggle
Tools: Python (Pandas, NumPy, Matplotlib, Seaborn), VSCode, Canva
Folder: Python Excel EDA Bike Sales
An end-to-end analysis of a bike sales dataset, from raw data cleaning in Excel through to visual insights in Python.
Dataset: Sourced from Microsoft's Harnessing the Power of Data with Power BI course on Coursera.
Tools: Excel, Python (Pandas, Matplotlib, Seaborn), Jupyter Notebook, VSCode
Folder: Python Excel EDA Work-Life Balance and Longevity
Analyses how daily lifestyle choices — work, rest, sleep, and exercise — affect longevity across a synthetic dataset of 10,000 individuals.
Core question: How do daily lifestyle choices, including work, rest, sleep, and exercise, affect how long a person lives?
Dataset: Quality of Life Data — Kaggle
Tools: Excel, Python (Pandas, Matplotlib, Seaborn), Jupyter Notebook, Canva
Folder: SQL Chicago (US) Analysis
A SQL analysis of Chicago's crime reports, public school performance, and socioeconomic indicators, exploring crime patterns, school safety, and the relationship between neighbourhood income and community outcomes.
Core questions:
- What are the most common crime types and which lead to the most arrests?
- Which community areas and districts are most crime-concentrated?
- How does school safety and college enrollment vary across community areas?
- Is there a relationship between school safety scores and per capita income?
Dataset: Provided by IBM's SQL: A Practical Introduction for Querying Databases course on Coursera.
Tools: SQL, MySQL, phpMyAdmin
Folder: SQL Python D2C Skincare E-Commerce Analysis
A combined EDA and business performance analysis of a Direct-to-Consumer skincare e-commerce business, examining customers, products, orders, reviews, and returns to identify performance trends and improvement opportunities.
Core question: How is this D2C skincare business performing across customers, products, sales, and returns, and where are the opportunities to improve?
Dataset: D2C Skincare E-Commerce Analytics Dataset — Kaggle
Tools: SQL, MySQL, Python (Pandas, Matplotlib, Seaborn), Canva
| Tool / Language | Purpose |
|---|---|
| Excel | Data cleaning, analysis, and pivot chart visualisation |
| Python (Pandas, NumPy, Matplotlib, Seaborn) | Data manipulation, analysis, and visualisation |
| SQL / MySQL | Relational database querying |
| Jupyter Notebook | Interactive Python environment |
| VSCode | Code editor |
| Canva | Chart and infographic design |
| Kaggle / Coursera | Dataset and project sources |
laingangiang2006 - GitHub Profile
This project is for educational purposes. Dataset credits are listed in each individual project's README.