SQL(Postgres, MySQL), Python, R, AWS, Data Visualization

YICHAO ZHU

626-***-**** j ******@*****.*** j https://www.linkedin.com/in/yzhu0219

San Francisco, CA, 94112

EDUCATION

University of San Francisco expected June 2017

Master of Science in Analytics (MSAN)

Core courses: Machine Learning, Relational Databases, Statistics, Data Acquisition, Data Visualization Shanghai University of Finance and Economics June 2016 Information management and information system

Core courses: Database, Management Information System, Advanced Algebra, Object-Oriented Program Design Exchange Program: Kedge Business School, Bordeaux, France February 2015 EXPERIENCE

Data Analyst Intern at UCSF School of Medicine October 2016 present Department of Ophthalmology

Assisted with the project "Ophthalmic Seasonality and Outbreak detection from social media posts."

Worked on data acquisition using Python, twitter API, AWS and digitalocean.

Estimated missing location for regional outbreak of pinkeye

Predicted likelihood distribution of trachoma in 2020 Intern at P zer Inc.,Shanghai O ce July 2015 June 2016 Business Operation Department

Wrote queries to obtain sales and product information from databases using SQL

Maintained databases by updating data and removing outdated tables

Created monthly business reports for personal, group and regional sales data

Created comparative quarterly reports about sales data for di erent channel types

Ranked personal and group sales performance with SQL PROJECTS

Machine Learning

Wrote a collaborative ltering algorithm to predict movie reviews ratings using a dataset of Net

ix ratings.

Used Pearson coe cient to calculate user similarity

The mean squared error was 0.8899

Data Visualization

The dataset contains the

ights information from 20 unique carriers in the U.S. in year 2008.

Used techniques of line chart, map plot, bubble plot, bar chart and Sankey plot Nature Language Processing

Predicted the sentiment of certain tweets(negative, neutral, positive)

Created features and reduced the misclassi cation rate from 0.41 to 0.26.

Used Linear Discriminant Analysis as classi er

Application Development

Developed an App to decide the mode of travel between

ying and driving

Used data from Google direction API, weather API and gas price API

Create an algorithm which supported our recommendation SKILLS

SQL(Postgres,My SQL) Python(Numpy, Pandas, Scikit-learn), R,Spark, AWS(EC2,S3,EMR), digitalocean

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