Xigang Zhang

617-***-**** adkkv4@r.postjobfree.com

EDUCATION

The Pennsylvania State University University Park, PA Bachelor of Science in Statistics May 2021

Bachelor of Science in Economics GPA: 3.78 / 4.00

Minor in Mathematics Dean’s List: 2020Spring/2020 Fall semesters SKILLS

• Relational Databases: SQL Server, MySQL, MS Excel

• Programming Languages: MATLAB, Java, JavaScript

• Statistical Language: R, SAS, Python, Stata, Minitab

• Data visualization Tools: Tableau, Power BI, R Shiny

• Microsoft: Excel, PowerPoint, Word, Outlook

• Frontend Languages: CSS, HTML

RELEVENT EXPERIENCE

Penn State Department of Statistics University Park, PA Teaching Assistant January 2021 – Present

• Grade assignments for 107 students including daily problems, quizzes, midterm, and final exams.

• Facilitate coursework for professors with discussion sessions and offices hours every week

• Establish Time series models in R to find a time-varying approach to the stock return-inflation puzzle Educational Testing Service (ETS), Royal National Hospital for Rheumatic Disease in Bath University Park, PA Capstone Project January 2020 – December 2020

• Built multivariate regression models by using R and Python to find correlation between Sesame St. TV show and 4-year-old children ‘s cognitive skill test scores

• Built mix-effect regression models by using R and SQL to find significant treatment on healing Ankylosing Spondylitis

• Managed historical data from 5 different data sources, and used SQL to maintain a semi-relational structure between multiple csv files. Developed SQL query to pull data from multiple data sources

• Used time series analysis and generalized additive model, based on R, to test Lipstick effect in Brazil from 1989 to 2016

• Visualized data by using ggplot, SAS, Tableau generated plots, pies, histograms and dashboards for presentation Human-Computer Interaction (iHCI Club), South China University of Technology Guangzhou, China Data Analyst (Co-op with Debelhome) May 2017 – September 2017

• Built simulation models in R using Time series to predict future market size of home automation furniture and its distribution across longer timespans based on multiple key Macroeconomics factors

• Troubleshooted performance issues of SQL by analyzing executions plans, index usage patterns, and join strategies

• Drew data flow diagrams by using Power BI, analyzed business requirements, and compiled data mapping documentation and data transformation specifications for data pipelines using A/B tests with Excel League of Legends, Riot Games Mall Tencent Holdings, Ltd. Shenzhen, China Operations Analyst March 2015 – August 2015

• Fulfilled daily profit data and report requests from business units, and built data set and report results by using Excel

• Created day-by-day data analysis through assessing market shares, consumer ratings, daily growth rates and data fluctuations. Analyzed products trends, customer behaviors and clients’ value by using Excel

• Built sophisticated automations in Excel to compile and collect data from hundreds of data feeds on a daily basis.

• Provided detailed historical reports using A/B tests for customer’s feedback datasets and linear models to predict future prod- ucts’ consumption patterns

RESEARCH EXPERIENCE

Penn State Book of Apps for Statistics Teaching (BOAST) Program University Park, PA Undergraduate Research Assistant May 2020 – December 2020

• Revised 2 existing Shiny Apps CSS/HTML design, leading to a 23% increase in click rates

• Reprogramed 2 existing Shiny App game-based quiz modules by using JavaScript, increasing its mobile friendly scores 200%

• Fixed 50+ bugs on the Shiny App and implemented numerous enhancements to improve the overall user experience

• Used HTML, CSS, JavaScript, R, R to create 1 new Shiny App about collinearity problem and design escape room game.

• Utilized R shiny, ggplot to generate interactive data visualization plots Penn State Sports Analytics Club University Park, PA Undergraduate Research Assistant August 2019 – February 2020

• Assembled more than 100 sports games’ data from NFL, NCAAFB, MLB by using Python web crawler

• Used Trackman software to collect athletes’ raw data from more than 20 live game

• Developed SQL query to pull data from collecting raw data, conducted trends and patterns analysis to help football coaches identify athletes’ performance and predict the sports game results

• Set up dashboards, statistics reports and visualizations in Tableau, Power BI and ggplot

• Utilized R to build up regression models finding correlation between ticket price and teams, weather, location