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Data Project

Rosemead, California, United States
February 14, 2019

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**** ******* ***, **, ****** States, 91755


LinkedIn Profile: EDUCATION

Pepperdine University, Graziadio School of Business and Management Malibu, CA Master of Applied Analytics (MSAA) Aug 2018

• Asian Business Club

• Analytics and Digital Innovation Club

University of Iowa Iowa City, IA

B.S. in Statistics, Statistics in Business June 2017

• Chinese Students and Scholars Association (CSSA) Member EXPERIENCE

Python Machine Learning Project 05/2018-11/2018

Machine learning Projects & Kaggle Data Projects by Using SikitLearn

• Boston data: Completed the Linear Regression machine learning model to predict the median value of owner-occupied homes

• Iris data: Built the classification model by using Logistic Regression, KNN cluster, SVMs and Gaussian Naive Bayes machine models to classify the type of Iris flower

• Kaggle Titanic data: Accomplished the titanic data competition by using Random Forest and Decision Tree machine learning model to classify the survivors from Titanic

• UCI spam message data: Built a Natural Language Processing model to classify the message is ‘ham’ or


Alpha Capital Holdings, Inc. New York, United States 05/2018-08/2018 Investment Banking Analyst Intern

• Accomplished Financial modeling and deal support for LBO transaction with multiple buyers and financial scenarios

• Completed a training program focusing on different valuation methodologies, industry analysis, and soft skills

Pepperdine University, Malibu, CA 08/2017-08/2018

Research Program – “Explaining the Gender Gap Among Self-employed Veterinarians”

• Assisted a research on “Explaining the Gender Gap Among Self-employed Veterinarians” to explore the most significant independent variables to predict earnings by using multiple regression and helped Prof. Samuel Seaman posting the research on Atiner

• Viewed data from annual wage surveys conducted in 1994 and 1995 by Medical Economics Research Group, at the direction of Veterinary Economics and guessed what variables would be highly significant on affecting the outcome variable - earnings

• Coded the dummy variables for non-numerical variables, constructed the potential interaction variables, Explored the potential higher order term variables and remove the variables that has high collinearity

• Run the multiple regression by using step-wise method to find out the best model in SPSS. The most significant variable is “1994 personal compensation”. And the adjusted R square shows there are 89% of variation of earning income explained by the model The University of Iowa, Iowa City, IA 08/2015-12/2015 R Applied Time Series Project

• Collected and cleaned Facebook stock price data from Yahoo Finance in last 3-4 months by using python. Transformed the data by taking log and difference to make data stationary

• Constructed possible ARIMA models by using ACF, PACF and EACF diagram. Choose the best model by comparing AIC and BIC. Diagnosed the models by checking residuals, ACF of residuals, p-values and normality of residuals

• Built the model with seasonality and diagnosed model again. Predict the stock price for the next quarter. ADDITIONAL

• Languages: Bilingual in Mandarin and English

• Experienced Software: SPSS, R, SQL, Python, Tableau, Microsoft Excel

• Interests: statistical research, programing language, economics, physics and sports

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