Data Analyst
Resume:
San Jose, CA
*************@*****.***
ZHIJIAO (FREENA) WANG
(Permanent U.S. Resident)
GitHub
Portfolio Website
TECHNICAL SKILLS
● Programming Languages: Python, SQL, Shell, Git, HTML, CSS, JavaScript
● Data Analysis and Reporting: Python (numpy, pandas, matplotlib, scikit-learn), R, Tableau, Excel, Stata, SPSS
● Databases: SQL& NoSQL, MySQL, PostgreSQL, BigQuery, Teradata, MongoDB, Hive, Spark EDUCATION
● Udacity – Data Scientist & Data Engineer Nanodegrees Expected 06/2019
● Udacity – Data Analyst Nanodegree 06/2018
● Northeastern University, Boston, MA – Sociology (Concentration in Quantitative Research), M.A. 12/2016
● Sichuan Normal University, CHINA – Economics, B.A. 05/2006 PROFESSIONAL EXPERIENCE
WeRide.ai (a self-driving car startup), Sunnyvale, CA Data Analyst, Contractor 09/2017 – 08/2018
● Responsible for performing data analytical tasks, such as data manipulation, quantitative analysis, statistical modeling, data visualization, and reporting. Partnered with engineer team and product team on delivering actionable insights.
● Built data ETL pipeline using Postgres and Python to automatically process the raw sensor data and convert into structured databases. This automated process had resulted in significant time and cost saving.
● Extracted daily collected labeled data from NoSQL database, performed data preprocessing by Python to ensure data quality, which included dealing with missing values, duplicates, inaccurate information, and outlier detection.
● Wrote MySQL and HiveQL queries against large datasets and performed data analysis to identify problems and actions required, which helping engineer team was improving the efficiency of their developing algorithms.
● Identified the proper metrics needed and monitored KPIs to measure the performance of our product; performed statistical analysis by Python to identify the root cause behind trends or other abnormal scenarios.
● Built/modified/maintained dashboards using Tableau, SQL, Jupyter Notebooks, and regularly delivered ad-hoc analysis reports to unlock opportunities for growth. PROJECT EXPERIENCE
Audit and Wrangle WeRateDogs Twitter Data 06/2018
● Extracted Data: parsed tweet text data by using beautifulsoup and stored txt data in JSON format.
● Cleaned Data: audited and used Python to perform data wrangling to optimize data quality like missing, duplicate, incorrect data, and lack of tidiness such as specific structural issues and merged various tables.
● Analyzed Data: applied the exploratory data analysis approach to summarize and visualize the critical characteristics and trends of the data as well as visualized the insights by using numpy, pandas, matplotlib, seaborn, notebook. Predict Default Risk of Lending Club Loan 03/2019
● Analyzed the Kaggle Lending Club data which contains 60 fields for each loan originated and built predictive models
● Used Python to perform data wrangling and preprocessing to optimize data quality and applied statistical analysis approach to visualize the important characteristics by using numpy, pandas, matplotlib, seaborn, notebook.
● Built and trained predict model in scikit-learn using logistic regression algorithm to predict the risk of loans and improved the model accuracy to 90% by feature engineering and parameters tuning. CERTIFICATES
Google – “Google Analytics Individual Qualification Certificate” 08/2018
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