Amy Chu

Fascinated by data science, I am eager to leverage my skills in data analytics and statistics to contribute to a dynamic team. With a solid academic background in data science and engineering, I have honed my programming and analytic skills in SQL, Python, R, SAS, and VBA, and I am excited to take on new challenges.

Dedicated and driven, I am adaptable and thrive in fast-paced environments. I am confident that my dedication and enthusiasm will enable me to make a valuable contribution to my future employer.

Project: Library Management System using SSMS (Grade: 96%)             Platform: SQL Server Management Studio

• Designed and implemented a backend database to efficiently manage library operations, including book availability, borrower details, loan tracking, and membership information.
• Created multiple tables to store essential data, such as Books, Book Authors, Publisher, Book Copies, Book Loans, Library Branch, and Borrowers.
• Utilized primary keys and reference keys to establish relationships between tables, allowing for effective cross-referencing of information.
• Implemented views to facilitate easy retrieval of necessary information from the database.
• Key features of the system include:
  • Displaying all available books with their respective quantities.
  • Tracking books borrowed by individuals, including the loan date, expected return date, and late due fees.

Project: Customer Response Prediction for Promotion Campaign           Platform: Python, Databricks

• Led a project focused on predicting customer response to a promotion campaign utilizing a retail transaction dataset.
• Created an annual aggregate dataframe to capture annual spending patterns, incorporating descriptive statistics.
• Developed a separate dataframe to capture monthly transaction summaries per client, leveraging rolling window features and date-related attributes like the day of the week and days since the last transaction.
• Utilized the feature-engineered datasets to train various supervised machine learning models, including Logistic Regression (L1 regularization), Decision Tree, and Random Forests.
• Performed data preprocessing and optimized model hyperparameters to enhance model performance.
• Evaluated model performance through diverse evaluation methods, confusion matrix, ROC, and precision/recall.

Project: Customer Analysis and Deactivation Trend for Wireless Company           Platform: SAS

• Analyzed a two-year CRM dataset to gain insights into customer distribution and business behaviors.
• Conducted data exploration to examine the dataset, investigated the age and province distributions of active and deactivated customers to understand customer demographics.
• Segmented customers based on age, province, and sales amount into specific categories.
• Performed statistical analysis to derive valuable insights, including calculating the tenure in days for each account, determining the number of accounts deactivated per month, and segmenting accounts by status and tenure to report the percentage of accounts in each segment.
• Conducted chi-squared tests to explore relationships between tenure segments and attributes such as "Good Credit," "RatePlan," and "DealerType."
• Examined associations between account status and tenure segments, seeking to identify a more effective tenure segmentation strategy that aligns with the account status.
• Investigated whether sales amount varies across different account statuses, "Good Credit," and customer age segments using One-way ANOVA tests.

Advanced Diploma, Data Science and Application

Metro College of Technology, Toronto, ON                                              Expect to graduate in August 2023

Relevant coursework: Data Mining (Advanced Statistical Modeling), Machine Learning and Deep Learning, Business Analytics, Tableau, Cloud Computing and Big Data Analytics

Master of Engineering, Chemical Engineering & Applied Chemistry      Emphases in Analytics

University of Toronto, Toronto, ON                                                                      September 2021 – June 2022

Relevant coursework: Data Analytics and Machine Learning, Data Mining, Process Data Analytics

Bachelor of Applied Science, Materials Science Engineering       Minor in Engineering Business

University of Toronto, Toronto, ON           Cumulative GPA 3.62/4.0.                September 2017 – June 2021