Data Analyst Sales

YANLAN LIANG

Greater New York Area Email: *****@***********.*******.*** Cell:732-***-****

SUMMARY & OBJECTIVES

• 3-year experience in SQL, R, Python, Tableau, and machine learning. Expert in data-driven problem-solving.

• Expected graduated on Dec 2020. Seeking job position of data analyst or business analyst. EDUCATION

Rutgers Business School, Rutgers University Sep 2019 - Dec 2020

M.S. in Information Technology Analytic, GPA 4.00/4.00, Top 5% Newark, NJ Rutgers Business School, Rutgers University Sep 2015 - May 2019

B.S. in Business Analytics and Information Technology, GPA 3.60/4.00, Cum Laude New Brunswick, NJ PROFESSIONAL EXPERIENCES

Capstone Project: User’s Behavior Analysis for Vestiaire Collective Sep 2020 - Dec 2020

• Crawled 9 million users’ behavior and profile data, preprocessed imbalanced dataset, and conduced EDA via Python.

• Identified main factors that contribute to the success of second-hand market transactions by analyzed user behavior.

• Built Random Forest and Extreme Gradient Boosting models to detect and classify active users, leveraged bootstrap resampling technique to improve the model performance with skewed data (F1-score doubled).

• Drafted active-user persona analysis reports, guided around precision marketing strategies based on model forecasts. Credit Analyst Assistance, Bank of Guangzhou, China Jul 2018 - Aug 2018

Collected and analyzed over 20,000 financial data and researched industry outlooks to identify loan risks.

Designed risk evaluation metrics, quantified loan risk levels, and rated clients loan profiles using overdue classification models by R.

Authored and personalized risk analysis reports with rating reasons, conveyed reports to the marketing team to support the bank lending decision-makings and avoid potential loss.

Automated Corporates Integrity’ survey reporting process using Excel, reduced man-hour by 80% (from 5h to 1h). PROJECT EXPERIENCES

COVID-19 Impact on US Stock Market May 2020

Researched on the correlation between the stock market and coronavirus globally and its impact on the US stock market.

• Crawled 10,000+ COVID-19 related data and financial data using Excel Web Queries and visualized via Tableau.

• Built LSTM model in R to explore COVID-19 impact on different industries’ stock price trend.

• Educated the US stock investors on stock selections to reduce the risk of loss during the COVID-19 epidemic. China Automobile Market Financial Time Series Analysis Jan 2020 - May 2020 Led a team of 4, analyzed automobile time series datasets to support market strategies in China using R.

Formulated data cleaning and feature engineering, removed the non-seasonal fluctuations by applying Multiple Regression with dummy variables for over 96,000 sales data.

Developed ARIMA time series model to forecast vehicle demand, explore sales patterns, and evaluated model performance using NRMSE.

Advocated for 20+ regions' vehicle sellers on inventory management and marketing strategies based on the analysis.

Drafted 20-page report with model choosing and data visualizations, presented the findings to the class. Business Data Management: Simulated Hotel’s Database Project Oct 2019 - Dec 2019 Led a team of 4, designed and created a database structure to facilitate hotel data management process using MySQL.

Designed a database with 5 interrelated tables for a simulated hotel to store customers’ booking information.

Created hotel specifications based on business research, designed developed ER diagram of the relational database.

Optimized data management efficiency by creating integrity constraints to prevent the creation of duplicate records.

Built a fully functional web server using AWS EC2 and the LAMP architecture, allowing users to add, delete and search for records stored in a MySQL database from anywhere on the internet. Graduate School Admission Prediction Jan 2019 - May 2019 Led a team of 5, built classification models to predict next-year graduate school chance of admission using R.

Collected and analyzed 400+ rows of graduate student application data and classified chance of admission by applying feature scaling and linear and quadratic discriminant analysis.

Built logistic regression model and K Nearest Neighbors in R to forecast student chance of admission, discovered school admission preference, and raised recommendations for undergraduate applicants. TECHNICAL SKILLS

R / SQL / Tableau / Excel / Access / MySQL / Oracle / Python / C++ / AMPL / Octave / Amazon Web Service (AWS)

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