Hello! My name is

MIN CHANG

I am a BI Engineer and Data Analyst, currently pursuing my Master’s in Data Science Engineering at Northeastern University. Most recently, I worked as a Business Intelligence Engineer Intern at Amazon, where I built SQL pipelines, automated reporting workflows, and developed interactive dashboards in QuickSight to support large-scale supply chain execution.

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Get to Know Me

My background spans Marketing Analytics, Business Intelligence, and Data Engineering fundamentals, giving me the versatility to move seamlessly between data analysis, pipeline design, and visualization. I have strong experience with Python, SQL, ETL automation, and statistical modeling, and I’m passionate about applying these skills to uncover actionable insights and build scalable data solutions.

Driven by curiosity and a deep interest in machine learning and predictive analytics, my long-term goal is to grow into a Data Scientist role where I can apply advanced models to solve complex, real-world problems.

About Me

"No one can whistle a symphony. It takes a whole orchestra to play it"

- H.E. Luccock

My Latest Projects

NLP/Sentiment Analysis on IMDB Reviews

This project focuses on sentiment analysis using the IMDB movie review dataset, which contains 50,000 labeled reviews. Our goal was to compare different machine learning and deep learning approaches — including Naive Bayes, LSTM, and BERT — to see how well each model could classify reviews as positive or negative. By evaluating their F1 scores and error patterns, we identified the strengths and weaknesses of each method for sentiment classification.

Smart Contract Vulnerability Risk Analysis

• Conducted unsupervised machine learning & K-means for hierarchical clustering analysis in Python using Euclidean and Jaccard Distance metrics on binary datasets of compiled risk tags, leveraging these metrics for their precision in quantifying similarity in binary data structures
• Created and analyzed heatmaps, dendrograms, and histograms, to visualize and quantify complex relationships and shared security vulnerabilities in smart contracts, which supported strategic risk management decisions.
• Validated peers' data labels to ensure accuracy and reliability of data sets.
• Performed frequency & correlation analysis of risk tags using Python to identify the most prevalent vulnerabilities and their associations, gaining proficiency in data manipulation and statistical tools.

Project: Hyundai QBR 2023

In the Q3-Q4 review for 2023, I led a critical analysis project at Hyundai, tasked with organizing and reviewing multiple complex datasets from October to December. The primary challenge was to assess the applicability and organization of extensive data to ensure its relevance and accuracy for the project’s goals. I developed and executed numerous complex SQL queries for data joining, migration, aggregation, parsing, and cleaning. Leveraging Alteryx cloud technologies, I automated and scheduled data workflows, which significantly enhanced operational efficiency. I created detailed Tableau visualizations to present key insights and KPIs to upper management effectively. As the project lead, I collaborated closely with analysts, Quality Assurance, and Engineers, ensuring the integrity and utility of data in driving strategic decision-making

Neural Network Modeling using Python TensorFlow

In this project, I forecasted Hyundai's parts freight and supply chain costs by creating a simple recurrent neural network (RNN) for its effectiveness in handling sequential data. I developed a basic model using Python TensorFlow's Keras API to predict future costs based on the WTC historical data, consisting of monthly freight and supply chain costs.

Alteryx ETL Pipelines with Hyundai Big Data

During my tenure at Hyundai, I developed over 30 ETL automations using Alteryx and SQL, significantly enhancing data processing workflows for projects. This initiative not only streamlined the integration and cleansing of data but also deepened my expertise in managing complex data structures and optimizing data storage solutions. Details are kept confidential due to privacy and security considerations.

Osteoporosis Risk Analysis with Machine Learning

The dataset provides lifestyle details of individuals with and without osteoporosis, including demographic information, lifestyle factors like physical activity, smoking, and alcohol consumption, and medical history. It aims to aid research in osteoporosis risk prediction and analysis, enabling the development and evaluation of machine learning models to identify individuals at risk based on various health factors.

Google Data Analytics Specialization Certified
Issued on July 2022
ID Credentials: AEJDB6WEVLU5

Prepare

Ensuring ethical data analysis practices, addressing issues of bias and credibility, accessing databases and importing data, writing simple queries, organizing data.

Process

Connecting business objectives to data analysis, cleaning small and large datasets using Excel Spreadsheets, SQL, R, Python, documenting data-cleaning processes

Analyze

Converting, formatting, sorting, and filtering data using SQL queries, substantiating data analysis processes, understanding the purpose of different business communication tools,

Visualize

Creating visualizations and dashboards and filters in Tableau, telling a data-driven story, creating static infographics and interactive tools that can be consumed online or in print.

My Certificates

Get in Touch!

+1 (562) 390-3115

mchangprotocol@gmail.com

MIN CHANG

Get to Know Me

My Latest Projects

Google Data Analytics Specialization Certified Issued on July 2022 ID Credentials: AEJDB6WEVLU5

Get in Touch!

Google Data Analytics Specialization Certified
Issued on July 2022
ID Credentials: AEJDB6WEVLU5