Tien Tran

• Responsible for digitizing, compiling, and analyzing more than 1000 housing inspection reports for the city of San Francisco.
• Presented weekly to a panel of senior inspectors, providing critical data on the state of local housing conditions.
• Developed a keen eye for detail and a deep understanding of the importance of accurate data analysis in informing policy decisions.

• Gaining valuable experience in networking and technical skills in computers through 32 workshops and meetings.
• Demonstrated strong collaboration and project management abilities by working with fellows to plan and execute successful strategies to meet paper and grant deadlines.

• Performing a variety of tasks including answering phone calls, responding to emails, filing documents, managing schedules, and assisting with event planning.
• Demonstrated excellent communication and interpersonal skills while interacting with students, parents, and staff members.
• Gained valuable experience in office administration, time management, and problem-solving, which will be highly transferable to future roles in any industry.

Data Structures, Structure and Interpretation of Computer Programs, The Beauty and Joy of Computing, Principles and Techniques of Data Science, The Foundations of Data Science, Designing Information Devices and Systems, Linear Algebra, Discrete Mathematics and Probability Theory, Multivariable Calculus

Pre-Calculus, Trigonometry, Calculus 1, Calculus 2 & Women and Gender Studies

Ethnic Studies

A Crash Course in Causality: Inferring Causal Effects from Observational Data

Unsupervised Learning, Recommenders, Reinforcement Learning; Advanced Learning Algorithms; Supervised Machine Learning: Regression and Classification

• Conducted exploratory data analysis (EDA) on a housing dataset of 204,792 data points and 61S different features using Python and pandas, identifying key features and trends to inform modeling decisions and improve predictive accuracy for house price.
• Built and fit linear regression models using scikit-learn to predict housing prices based on relevant features, achieving a root mean squared error (RMSE) of 227K, and identified potential areas for model improvement through analysis of residuals and feature importance.

• Conduct an analysis of historical temperature data to identify trends over a prolonged period and utilize this information to make predictions about future weather patterns.
• Categorize and create visual representations of data from 210 U.S cities for the last hundred years.

• Developed and fine-tuned a spam email classifier from a 9,348 emails dataset using natural language processing techniques and machine learning models, achieving an accuracy rate of 96%.
• Leveraged data engineering strategies to extract pertinent features, such as filtering out common words and identifying distinct syntax patterns between spam and non-spam emails.
• Additionally, controlled for confounder factors like reply or forward status in emails to enhance model performance and ensure accurate prediction outcomes.
• Gained proficiency in utilizing sklearn libraries for data processing and model fitting, as well as validating model performance to minimize overfitting and analyzing ROC curves.

• Collected and analyzed 43,537 data points from gapminder.org to create visual representations of 12 statistical features pertaining to mortality, fertility, and poverty rates of 197 countries.
• Utilized graphing and tabling methods to effectively convey trends and changes over time.

• Design and implement a 2D tile-based world exploration engine for educational purposes, simulating product development cycles with minimal starter code.
• Focused on software engineering principles, code management, and problem-solving, while utilizing various data structures and algorithms for simplicity and efficiency.
• Successfully created a grid-based virtual world that allowed user interaction and exploration, demonstrating project management and creative problem-solving skills.

• In this project, I developed AI-based guessers and word choosers for the game "Awakening of Azathoth," gaining hands-on experience with Java's built-in List and Map data structures.
• I successfully created three distinct AI guessers that could play the game, along with two innovative word selection classes that elevated the complexity of the problem.
• This project showcased my ability to utilize data structures effectively, and allowed me to demonstrate my proficiency in Java programming.