Data Analyst

Xiaohuan Li

***W ***TH ST APT**, New York, NY ***25

Cell:917-***-**** Email: ******@********.***

SUMMARY AND OBJECTIVE

Have extensive knowledge of statistical models and machine learning algorithms, experienced with data

pre-processing, exploratory data analysis and model construction.

Seeking a data analyst position in a friendly and fast growing environment with quantitative problem-solving and

analytical skills to help the organization achieve its missions and goals.

EDUCATION

Columbia University, Graduate School of Arts and Sciences New York, NY

MA in Statistics, GPA: 3.6/4.0 December 2014

Huazhong University of Science and Technology, School of Economics Wuhan, China

BS in Financial Engineering, GPA: 3.8/4.0 June 2013

PROFESSIONAL EXPERIENCE

Rongzhi Investment Management Company Shanghai, China

Data Analyst Intern July-August 2014

Worked closely with various teams across the company, and provided technical assistance in support of

management and customer requests;

Enhanced market research efficiency by applying web scraping using Python in data collection and organizing

field trips to do detailed market investigation;

Implemented statistical models by R to do regression and correlation analysis.

China Minsheng Banking Corp.Ltd Hangzhou, Zhejiang, China

Financial Market Intern March-May 2013

Extracted financial index data from web sources to effectively facilitate daily inter-bank borrowing business;

Communicated with clients to understand their needs and made recommendations.

PROJECT EXPERIENCE

Face Recognition Fall 2014

Processed original data by transferring rectangle color images to square black & white ones;

Used three algorithms included Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and

Rectangular-Area Feature Extraction to do face recognition;

Compared the results of three methods, concluded that LDA outperformed PCA, and Rectangular-Area Feature

Extraction also has good performance if enough features are used.

Prediction of Default Probability Spring 2014

Explored application of various classification methods, models and algorithms to prediction default probability

based on clients’ basic and credit information;

Conducted data pre-processing and exploratory data analysis in R and used Github for version control;

Implemented Naïve Bayes, Logistic Regression, Support Vector Machines and Random Forest to do

classification and reached 90 percent accuracy.

Target Population Selection Spring 2014

Predicted whether a person makes over 50k a year based on 32561 observations and 15 variables by applying

Logistic Regression and Decision Trees;

Applied stepwise algorithm in Logistic Regression for feature selection and modified Decision Tree by using

Random Forest to improve the accuracy, which then reached 80 percent.

SKILLS AND CERTIFICATION

Skills: Microsoft Office Suite, R, SAS, MySQL, Python, Data Mining, Machine Learning, Time Series Analysis

Certification: Certified Advanced Programmer for SAS 9; Passed Level I of the CFA Program

Contact this candidate