Data science graduate student

Christine Vu

San Jose, CA ● **************@*****.*** ● 510-***-**** ● github.com/christinevu510

EDUCATION

University of San Diego Mar 2022 – Expected Dec 2023 Master of Science, Applied Data Science

Relevant Coursework: Applied Data Mining, Applied Data Science for Business, Applied Time Series Analysis, Practical Data Engineering

University of California, Riverside Sep 2018 – Sep 2021 Bachelor of Science, General Applied Mathematics

WORK EXPERIENCE

Elite Pain Management Aug 2021 – Present

Project Developer

Develop, test, and refine office automation processes for HMO patient authorizations.

Perform analysis to identify areas for improving test approaches through batch processing.

Evaluate the automation system designs, optimize test plans, and test the reliability and efficiency of the software.

PROJECTS

Supermarket Orders, Invoices, and Sales

Built an ELT pipeline to analyze the ordering, invoicing, and sales processes spanning 4 years at a supermarket.

Stored the processed data in a SQL database, creating 10 views to facilitate data analysis and visualization in Tableau.

Constructed a Tableau dashboard of 14 charts and graphs to visualize consumer behavior trends and key performance indicators central to business success such as revenue, sales conversion rates, and 4,725 companies’ performance.

Grocery Store Forecast

Developed predictive models aimed at forecasting weekly grocery sales for a supermarket chain, resulting in a 38% improvement in data forecast accuracy.

Observed time-series sales data patterns over 5 years to describe sales behavior, forecast future sales, and identify high-volume sales dates, which can be leveraged to optimize inventory management.

Analyzed short-term and long-term sales patterns to identify the highest-volume products and top three highest-selling product categories that account for 80% of store sales and $52,000 in daily sales. Predicting Airline Customer Satisfaction Ratings

Employed data mining techniques to predict customers’ level of satisfaction with their airline experience, yielding the top performing models with a 95.37% accuracy rate and 0.9886 AUC score.

Identified attributes that are correlated with a positive flight experience with the purpose of enhancing customer retention and acquisition, which has the potential to recover up to $1.2 million in lost profits due to dissatisfied customers not returning.

SKILLS

Programming Languages: Python, R

Technologies: MySQL

pandas, numpy, scikit-learn, machine learning, classification, regression, modeling, prediction, forecasting, time series analysis, clustering, cleaning, manipulation, visualization, algorithm, data pipeline, optimization, problem solving, data science, jupyter notebook, vscode, visual studio code, professional, analytical, behavorial, pipeline, data collection, requirements specification, processing, analysis, ongoing deliverables, presentations, business recommendations, cost-benefit, forecasting, experiment analysis

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