Data Analyst

Sylvia Yang

**** ****** ***, *** *, Berkeley, CA, 94709

+1-510-***-**** **************@*****.***

EDUCATION:

Peking University, Beijing Sept. 2015 – Jul. 2019

Bachelor of Science in Psychology GPA: 3.56/4.0

Double Major in Mathematics and Applied Mathematics Coursework: Data Analysis and Statistical Software (A) Data Analytical Thinking: From Data Analysis to Business Value (A) Statistic Package for Social Science (A) Cognitive Science & Economics (A) Psychological Research Method - Using Matlab (A) University of California, Berkeley, Berkeley Aug. 2019 – Present MA in Statistics GPA: 3.83/4.0

Related Course: Introduction to Probability at an Advanced Level (A) Introduction to Statistics at an Advanced Level (A) Principles and Techniques of Data Science (A)

SKILLS: R Python SQL SPSS MATLAB SAS Excel PowerPoint Word CERTIFICATES: Machine Learning by Andrew Ng (Coursera) INTERNSHIP:

Kantar Millward Brown Jul. 2018 – Oct. 2018

Data Analyst, Client Service Team Beijing, China

• Helped client find their best strategy for choosing a city to promote their peer-to peer lending product

• Collected data about residents’ basic information, p2p preference, and competitors' distribution according to different cities

• Converted data from questionnaire survey and conducted Natural Language Processing technique to analyze customer sentiment

• Built a regression model to predict the number of latent customers in each city and summarized pros and cons with data analysis Zhuanzhuan Spirit Technology Co. Ltd., Apr. 2018 – Jun. 2018 Data Analyst, Strategy Research Beijing, China

• Conducted research on second-hand books and second-hand smartphone business markets

• Collected data about second-hand books and second-hand smartphone users’ information

• Analyzed their basic information and behavior data on apps and labeled potential customers with the same pattern

• Applied Machine Learning to build a Lookalike model to find users with a second-hand product preference TEAM PROJECTS:

R package of the genetic algorithm for variable selection Dec. 2019 Advisor: Christopher Paciorek Department of Statistics, University of California - Berkeley

• Made an R package to implement the genetic algorithm for variable selection in linear and generalized linear regression models

• The R package allows users to specify a dataset, the type of regression, the fitness function, number of generations and popular size and selects features for the model which optimize its performance

• Specifically, I wrote the main function, additional functions as well as some formal tests Image classification Dec. 2019

Advisor: Josh Hug Electrical Engineering and Computer Sciences, University of California - Berkeley

• Built classifiers using a learning set of “real-world” images which could classify images into 20 types

• Implemented a complete workflow, including data manipulation, visualization, exploratory data analysis, feature selection, class prediction, and predictor performance assessment

• Tried several classifiers, including Logistic Regression, K-Nearest Neighbors, Random Forests and Support Vector Machines. Among them, the Support Vector Machines had the highest accuracy. EXTRACURRICULAR EXPERIENCE:

Volunteer Activities of Peking University Kindergarten, Organizer Sep. 2015 – Sep. 2016

• Led a group of volunteers to host a variety of weekly activities for physically or mentally challenged kids

• Communicated with teachers and parents and improved the content of activities according to their feedback Young Volunteers Association of Peking University, Member Sep. 2015 – Sep. 2016

• Coordinated with multiple departments and held more than 10 activities, such as voluntary recitation meeting and cultural festival

• Standardized the process of recruiting volunteers by promoting the official website of the Beijing Volunteer Service Federation

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