Marketing Data

Ning Han

(970-***-**-** *****@********.*** Charlottesville, VA 22903

E D U C A T I ON

M a s t e r o f S c i e n c e i n D a t a S c i e n c e ( e x p e c t e d M a y 2 0 1 9 ) University of Virginia, Charlottesville, VA 2018 – Present

• GMAT:720, GPA 3.8/4.0

• Data Science Institute Student Council Career Liaison

• Relevant Coursework: Programming and System for Data Science (Python), Statistical Computing Data Science (R and SAS), Data Mining, Practice and Application of Data Science (AWS/Spark), Text Analytics B a c h e l o r o f S c i e n c e i n B u s i n e s s A dministration Major in Marketing & Computer Information Systems

Colorado State University, Fort Collins, CO 2014 – 2016

• Graduated Cum Laude; Dean’s list; GPA 3.8/4.0

• Relevant Coursework: Business Database Systems (SQL), Pricing and Financial Analysis in Marketing, Advanced Application Design and Development (Java), Marketing Analytics (SPSS) R E L E V A N T E X P E R I E N C E

U n i v e r s i t y o f V i r g i n i a C a r e e r C e n t e r, P r o g r a m a n d R e s o u r c e A s s i s t a n t Charlottesville, VA, United States 2018 – Present

• Extracting email campaign data with Python, cleaning and consolidating the data with available demographic information and creating a relational database using SQLite.

• Defining KPI metrics for the email campaigns and creating a newsletter performance dashboard with Flask to uncover audience insights and engagement trends to career counselors.

• Utilizing correlation analysis and regression models to identify the strong factors that affect audience engagement.

• Using topic models such as latent semantic indexing to analyze the campaign contents to identify the topics with high engagement.

• Delivering clear takeaways and making practical business recommendations based on the data analysis findings to increase email campaign performance.

H i v e e l T e c h n o l o g i e s I n c ., M a r k e t i n g D i r e c t o r Los Angeles, CA, United States 2016 – 2017

• Defined business performance measurement strategies, developed metrics dashboards to monitor marketing activities, and optimized marketing campaign budgets through observational analyses, decreasing the marketing cost by 40%.

• Identified technical and design issues through the user behavior trend analyses and communicated the results with the development team, consistently improving user expereince and increasing platform users by 30K.

• Utilized regression models to forecast marketing trends, conducted hypothesis testing to uncover customer preference and initiated online and offline marketing campaigns based on the findings, increasing user engagement by over 50%.

• Collaborated with the IT team and successfully deployed an internal business performance interface. P R O J E C T

P r e d i c t i n g D r i v e r S e a t b e l t U s a g e i n C r a s h e s Department of Motor Vehicles, Richmond August 2018 – Present

• Conducting exploratory data analysis using Python and discovering unrestrained crash trends through visualizations using Tableau.

• Preprocessing the dataset by data cleaning and feature transformation and applying oversampling methods to manage data imbalance.

• Applying a variety of hypothesis testing methods, such as two-proportion z-tests, Welch’s t tests and Chi-square tests, to discover the differences between unrestrained group and the restrained group.

• Developing classfication models with K-nearest neighbors, logistic regression, random forest and XGBoost algorithms to identify unrestrained behaviors and applying regularization with optimal parameters to overcome overfitting.

• Generating an ensemble learning model using the above algorithms to improve prediction performance and currently achieving an accuracy rate at 78%.

Contact this candidate