Hi, This is Jane. I am currently a first-year master's student at Carnegie Mellon University studying Business Intelligence and Data Analytics.
Prior to coming to CMU, I graduated from National University of Singapore with a bachelor degree in Business Analytics. After graduation, I joined the R&D department of Sea as a data analyst. The two-year working experience as data analyst in e-commerce equipped me with practical experience on big-data ecosystem such as Hadoop, Spark, and Oozie for data storage, processing, and scheduling and enhanced capability of transferring data into business insights. I also worked closely with management team on identifying KPIs to support strategic decisions and interacted with multiple business units to produce high-quality analysis.
Perceiving myself as a wanderer, I am thrilled to explore multiple parts of the world. I was born and raised in Nanjing, Jiangsu, China. When I was 15, I made my first life decision to sign the scholarship contract with Singapore government and embark my education journey on a foreign land alone. Influenced by the multiracial environment in Singapore, I grew up as an open-minded individual who always love to explore new opportunities. During my undergraduate study in Singapore, I also had an exchange semester in Denmark when I was fortunate enough to travel around Europe a lot. I have been to 26 different countries (mostly in Asia and Europe) and counting! In the meanwhile, I am also an apprentice photographer trying to capture the world in my own way.
I am now actively seeking data science/analytics related internship in summer 2020, and please contact me if there is a match. You may check my resume HERE.
Main Courses: Intro to AI, Object Oriented Programming in JAVA
Main Courses: Data Structure and Algorithms, Data Mining, Regression Analysis, Stochastic Models, Data Visualization, Capstone Project
Main Courses: Optimization and Data Fitting, Computational Tools for Big Data, Intro to Financial Engineering
- Incorporated classification models, including logistic regression, Naïve Bayes, random forest, and SVM to analyze the influence of demographics, app installed, app activation rate, and device specifications on user attrition.
- Reached the accuracy rate of 71.2%, suggesting app activation rate and number of other apps installed as key factors
- Built a mobile web application for restaurants that allows customers to order the dishes and split the bill from one table, using Meteor framework (html, css, JavaScript) and MongoDB
- Crawled over 50,000 tweets using R through twitter API
- Developed a classification framework that categorizes Tweets into nine categories, using machine learning techniques such as Natural Language Processing, Naïve Bayes, and SVM with accuracy rate of 80%
- Designed and conducted an A/B testing to analyze the effect of image optimization on search result
- Crawled image-related website features, including image number, size and relevance of alt text, from more than 5,000 webpages for difference-in-difference statistical analysis, suggesting image as a significant factor for SEO