Data Analyst

YU-CHENG (VINCENT) WENG

765-***-**** **********@*****.***

West Lafayette, IN linkedin.com/in/yu-cheng-weng EDUCATION

Purdue University, West Lafayette, IN 08/2019 - 05/2021 M.S., Joint Statistics & Computer Science GPA: 3.5/4.0

Relevant Coursework: Statistical Machine Learning, Generalized Linear Model Design of Experiment, Data Mining, Database Systems, Algorithm Design National Chengchi University, Taipei, Taiwan 09/2014 - 01/2019 B.A., Economics, Double Minor in Applied Mathematics & Statistics GPA: 3.63/4.0

Program of Mathematical Finance

Relevant Coursework: Econometrics, Regression Analysis WORK EXPERIENCE

Data Analyst Intern, Retailing Data, Taipei, Taiwan 07/2020 - 08/2020

Implemented generalized linear model to identify optimal expanding locations for client’s restaurant

Constructed interactive dashboard based on client requirement, shortened 50% of time for producing reports by Python and Power BI

Deployed web crawler library for machine learning models (Selenium, BeautifulSoup) Advisor, Statistics in the Community, West Lafayette, IN 09/2019 - 01/2020

Employed Tippecanoe County real estate price data set to develop predictive models using generalized linear models

Conducted data cleaning and variable selection on over 10K data points and 1K variables by R Research Assistant, National Chengchi University, Taipei, Taiwan 09/2018 - 01/2019

Designed and implemented algorithms for feature engineering process (lasso, ridge)

Researched nowcasting economic growth and conducted regression analysis on labor economic data Machine Learning Intern, Yuanzi Information Technology, Shanghai, China 06/2018 - 09/2018

Researched and applied YOLO algorithm for object detection model for related factories, which achieved 94% accuracy, automated work and saved 80% of time for the worker.

Integrated and deployed the algorithm as a web app service with Docker and Python Flask.

Built user interface for photo upload platform (HTML, CSS, JavaScript) ACADEMIC PROJECT

Mechanism of Action, Purdue University 08/2020 - 12/2020

Built predictive models in Python on biological reaction datasets with Random Forest, Xgboost, LightGBM, Deep Neural Network and tuned hyperparameters by temporal cross-validation with 92% precision-recall AUC

Performed descriptive data analysis by Python visualization libraries (Matplotlib, Plotly) Violence Crime Rates, Purdue University 01/2020 - 05/2020

Conducted data cleaning and transformation for County Health Rankings data from 2013-2019 by R

Applied Poisson Regression, Generalized Linear Mixed Model on selected features, achieved a highest accuracy of 96.3%

SKILLS

Software & Tools pro cient in Python, R, SQL, Power BI familiar with MATLAB, STATA, git, Tableau, HTML, CSS, JavaScript

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