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Data Python

Dallas, TX
May 21, 2020

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Yu (Nicole) Zhang

+1-814-***-****;;; EDUCATION

The University of Texas at Dallas M.S., Business Analytics GPA 3.81 May. 2020 Dean’s Excellence Scholarship

The Pennsylvania State University, University Park M.A., Teaching English as a Second Language GPA 3.97 May. 2016 Central South University, Changsha, China B.A., Journalism GPA 3.32 July. 2013 Excellent Academic Performance Scholarship


Programming Language: SQL, R, Python, SAS, Hadoop, Spark Analysis Tools: Power BI, STATA, SAS, Tableau, SQL Server Data Tools, Adobe Analytics, Alteryx, Google Analytics WORKING EXPERIENCE

Invesco (Dallas, TX) Global Performance Data Analyst June 2019 – Now

• Developed complex SQL queries to extract data from enterprise data warehouse; build a front-end interface with Power Query to enable team members acquiring information in the data warehouse through Excel without writing SQL queries

• Applied data visualization to compare company's performance with the industry benchmark across varied dimensions; build dashboards in Power BI to demonstrate performance to senior executives and clients

• Provided insights on operational decisions by implementing machine learning algorithms on financial and operational data

• Supported IT team and ETL developers by providing routine checking, troubleshooting and alternative development solutions; Participated in ETL team’s meeting and optimized data warehouse functionality for our business needs Academies of Math and Science (Phoenix, AZ) Teacher July 2016 – July 2017

• Led the Mandarin programs in two schools for over 1100 students from Kindergarten to 8th Grade; middle school students grasped around 200 Mandarin vocabulary by the end of the school year Northeast Asia Spot Commodity Exchange (Dalian, China) Advertising Manager Mar. 2013 – July 2014

• Directed the company’s advertising for 6 months and boosted the company’s client base 19% ACADEMIC PROJECT

Google Play Store Apps (Tool: Python) Oct. 2018 – Dec.2018 Web scraped data of 10k Play Store apps for analyzing the Android market

• Data cleaning& Imputing: decomposed the JSON columns and assigning values to the dummy variables and null values

• Data Modeling: applied supervised machine learning models, including both regression and classification to predict the total transaction revenue and evaluated the models

• Performance improvement: Implemented ensemble learning methods to improve the model performance, including bagging, pasting and random forest, resulting in increasing the both training and testing score 5% Relational Database Design and Analysis (Tool: MS SQL Server) Nov. 2018 – Dec. 2018

• Designed and build an entity-relationship database based on over 10,000 records from one text file, made it functionally capable of querying information across different dimensions from Microsoft SQL Server Credit Card Approval Prediction (Tool: Python) Sep. 2018 – Dec.2018

• Led a team of 3 to conduct data cleaning, EDA, visualization and feature engineering in Python to explore insights of credit card clients’ data, provided recommendations for financial institutions for their credit card approving process

• Deployed machine learning classification models including KNN classification, logistic regression, SVC (with and without kernel) and decision tree; applied grid search to find the best parameters in each model

• Applied bagging, pasting and boosting on each model and improve cross validation score around 9%

• Created a pipeline to feed data after PCA into the same models listed above and compared the cross validation score with the original models: model performance stayed same while reduced running time CERTIFICATIONS & ORGANIZATIONS

Data Science with Python (Certified by Department of Computer Science at UTD) Nov. 2018 – Present Google Analytics Certification Nov. 2019 – Present Intelligence Analytics Society Feb. 2018 – Present DATA Science Club, University of Texas at Dallas Jan. 2018 – Present

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