Data Analyst Assistant

Zheyu (Joree) Liu

*** ****, ***** ******, *** York, NY 10025 646-***-**** ******@********.*** linkedin.com/in/zheyu-joree-liu Determined individual with strong analytical, communication, and leadership skills, solution-focused and ready to take initiative and work as a team player meeting new challenges with the ability to learn quickly and tackle every task with a positive attitude EDUCATION

Columbia University New York, NY

Master of Science – Urban Planning May. 2020

● Coursework: Quantitative Methods, Data Science, Exploring Urban Data with Machine Learning, Data Visualization for Architecture, Urbanism, Data Mining the City, Urbanism Nanjing University Nanjing, China

Bachelor of Science - Geographical Information Science May. 2018

● Coursework: C, C++, Applications of Database, GIS Technology and Application, Digital Image Processing University of California, Berkeley Berkeley, CA

Exchange Student – Data Science Sept. 2016

● Coursework: Data Science for Social Networks, Data Science for Smart Cities, GIS Principle and Applications EXPERIENCE

Gensler Research Institute New York, NY

Spatial Data Analyst Sept. 2019 – March. 2020

● Conducted data analysis and research for Gensler Cities Design Index

● Collected, analyzed and visualized thousands of urban data sources (census data, Oxford economic data, EIU, OSM street networks, public parks, real estate, POI, land use, etc) with 50000 survey responses for 50+ global cities utilizing ArcGIS and R for the compilation of Cities Design Index

● Delivered research to identify, quantify the relationship between neighborhood environment quality and spatial, economic, survey data in global cities with calculations of spatial metrics, map visualization of spatial patterns and spatial statistical analysis Savills New York, NY

Data Analyst Jun. 2019 – Aug. 2019

● Architected two web applications in JavaScript for Savills Occupier technology platform Knowledge Cubed, the applications provided labor analytics and real estate visualization for Savills Clients’ real estate portfolio optimization

● Created Social Demographic Explorer as an interactive map front end with embedded network analysis algorithm for the automatic generation of labor analysis report, with input as user selected draggable map area or user defined drive distance

● Built Real Estate 3D Viewer as an interactive map front end to highlight buildings on 3D maps based on longitude and latitude data with csv files drag and drop feature for user input, the application provides a quick way for client brokers to visualize bulk spatial data (JavaScript, ArcGIS Business Analyst, D3, Python) Columbia University in the City of New York New York, NY Teaching Assistant for GR5070 GIS and Spatial Analysis Sept. 2019 – Dec. 2019

● Assisted students with GIS spatial analytical techniques including social demography databases, social data visualization with GIS, spatially weighted regression models. Space-time models Research Assistant for Spatial Research Center Oct. 2018 – March. 2019

● Downloaded and batch converted New York State satellite map images at 19-zone level using web scrapping (Python, GDAL), stitched all images together, paired images with demographic data in 350000 census blocks (ArcGIS) to provide data source for spatial research tool at the center, visualized data in the front end (JavaScript)

● Created and visualized 2-step citation network analysis for 1954 Merton/Lazarsfeld paper by web scrapping google scholar data in Python with VPN service

PROJECTS

Explore Building Greenhouse Gas Emissions in New York City New York, NY

● Built, evaluated, and compared classification engines for identifying features affecting greenhouse gas emissions reduction, engines including decision tree, random forest, naïve bayes, SVM and multinomial logistic regression (Python, Scikit-learn)

● Identified features including energy star score, property floor area, assessed value, building age that affect emissions reduction MTA Subway Ridership Prediction New York, NY

● Implemented linear regression to predict MTA subway ridership with MTA performance, population, for hired vehicles data

● Performed time series cross section analysis, step-wise, all possible models regression, PCA for feature selection based on measures of AIC, Mallow’s CP, PRESS, MRes and R score, model reduced 20% of mean squared error from Baseline Model SKILLS

Technical: Python (Numpy, Pandas, Scikit-learn), JavaScript, Typescript, Angular, jQuery, html, CSS, C++, C, R, Scrapy, Selenium, Git, ArcGIS, QGIS, Tableau, Firebase, SQL Server, Excel, Power Point Languages: Mandarin (fluent), English (fluent)

Interests: Tennis, Swimming, Surfing

Contact this candidate