Hi! I am Julia

I am a

A passionate data analysis enthusiastic from Bay Area, San Francisco, USA. Recently graduated from UC Berkeley Extension Bootcamp. I believe in the power of data and its ability to transform business and society. I am a quick learner and a team player who is always eager to learn new technologies and tools. My background in hospitality and customer service has taught me the importance of communication and teamwork. I am looking for opportunities to apply my skills and knowledge to solve real-world problems.

HIRE ME
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Projects

NBA Draft Study

For "NBA Draft Study" project, I worked with classmates at UC Berkeley Extension Bootcamp to analyze NBA draft data to see if we could find trends that predict a player’s success. We collected historical draft data, player earnings, and stats, then cleaned and processed this information using Python and pandas. Our analysis included examining the correlation between draft positions and career earnings, identifying top-producing universities, and exploring the geographic origins of NBA players. We used libraries like Plotly and D3.js to create interactive graphs that made our findings accessible.

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Binging Netflix Data

The second project at the UC Berkeley Extension Bootcamp was another group project which we took on the exciting challenge of diving into Netflix's recently shared viewing data. Netflix gave us a peek into what shows and movies people are watching globally. We mixed this data with details from IMDb to give everyone a deeper look into popular viewing trends. Our websit is all about helping users find cool insights about Netflix's shows and what might be their next favorite watch. We built this site using tools that help us make sense of big data with interactive charts, and graphs built with libraries such as D3.js and Plotly. The recommendations are generated through data processing with Python, showcasing the power of backend technologies such as Flask and Pandas in data manipulation, all while keeping things straightforward and respectful of privacy.

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Jammin' with Spotify

In "Jammin' With Spotify," our team at UC Berkeley Extension Bootcamp developed a machine learning model for Spotify's playlist continuation challenge. We processed and cleaned data from 1 million Spotify playlists, extracting track features like danceability and energy using Spotify's API. Our project involved detailed data handling, from initial extraction to cleaning, utilizing Python and pandas. We explored machine learning models—cosine similarity, track clustering, and playlist clustering—to predict song recommendations based purely on musical characteristics. Although we created a prototype website to showcase our findings and allow user interaction, we didn't host it publicly due to Spotify's terms and conditions. This project highlighted our approach to data analysis, machine learning, and respecting data usage policies.

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Portfolio

Utilized Excel to organize and analyze a database of 1,000 sample crowdfunding projects to uncover any hidden trends.

Created and manipulated Pandas DataFrames to analyze school and standardized test data with Python and Jupyter Notebook.

Used Matplotlib to produce all necessary tables and figures summarizing the data for the technical report on a clinical study conducted by a new pharmaceutical company, Pymaceuticals, Inc.

Utilized Python, OpenWeatherMap API, and Matplotlib to analyze and visualize how latitude affects weather variables. Implimented API integration, data manipulation, and graphical presentation.

Worked on SQL challenge by using Python, SQLAlchemy, Pandas, and Flask to analyze climate data for planning a holiday in Honolulu, Hawaii. Developed a web application to visualize precipitation levels and station activity, providing insights into weather trends.

Developed a binary classification model using Python, TensorFlow, and Keras to help Alphabet Soup predict which funding applicants might succeed. This involved analyzing data from over 34,000 organizations, focusing on key features and employing preprocessing techniques such as categorical variable encoding and data scaling.

Preprocessed data for Alphabet Soup's neural network model, dropping specific columns and organizing unique values. Successfully split data into feature/target arrays and standardized values with StandardScaler. Compiled, trained, and evaluated the model, noting some output errors but effectively saved model weights for optimization.

Education & Work Experience

RESUME
1999-2009

Highschool

Practicing Highschool Yangon Institute of Education

Yangon, Myanmar

2010-2013

Bachelor of Arts (BA)

Yangon University of Foreign Languages (YUFL)

Language and Linguistics

2015-2016

Postgraduate Diploma

Hotel and Tourism Management Institute of Switzerland

Hospitality and Tourism Business Management

2016

Internship

St Regis (Park City, Utah)

2017-2019

Gap Year & Part-Time Jobs

Went back to Hometown, Traveled in Europe, Became a Permenant Resident in USA and started part-time jobs

2019-2021

The Olympic Club

Food and Beverage Assistant & Supervisor

2021-present

The Olympic Club

Golf Operations Administrative Assistant

2023-2024

UC Berkeley Extension Bootcamp

Data Analytics Bootcamp

Contact Me