Data Analyst

Yuping “Coco” Qi

628-***-**** ****@*******.*** San Francisco, California LinkedIn.com/in/yupingqi

E D U C A T I O N

University of California, Davis San Francisco, CA

Master of Science in Business Analytics (GPA 3.7/4.0) Expected June 2020 Highlighted Coursework: Big Data, Data Mining, Machine Learning, Data Visualization, Inferential Statistics Case Western Reserve University Cleveland, OH

Bachelor of Science in Accounting (GPA 3.8/4.0 cum laude) Dec. 2018 Highlighted Coursework: Writing, Business Law, Excel Application & Modeling, Economics, Finance Award: Dean’s List Scholarship: Weatherhead Scholarship Leadership: Treasurer of PERIOD @ CWRU Honors: Civic Engagement Scholar Licensure: CPA eligible by June 2020 T E C H N O L O G I E S

SQL, MongoDB, R, Python (PySpark, pandas, numpy, scikit-learn, scipy), Google Cloud Platform (AutoML), Tableau

(calculated field, Kepler.gl extention, etc.), Lucidchart (UML flowchart application), Excel (Pivot table, vlookup) P R O F E S S I O N A L E X P E R I E N C E

Air Safety Institution San Francisco, CA

Practicum Project Data Analyst & Project Manager Sept. 2019 – June 2020

• Established the objective to lower the fatality rate of General Aviation pilots using data science;

• Conducted exploratory data analysis using Tableau and Python and obtained data profile, distribution diagram, and correlation information; Selected important features out of 98; Concluded first-stage data cleaning

• Used kNN to divide the dataset into categories to help to label incidents; Constructed a classification model to label the aviation incidents into a binary category and created main topics using NLP

• Developed Tableau dashboards to visualize the above findings and added the feature of downloading data to our dashboard; Automated Tableau dashboard updates with packaged workbooks on Tableau server; Used Kepler to map geolocation data and made it animated using the time-series variable;

• Built a client-facing website using Github Pages and presented our aggregated products The Walt Disney Company y Shanghai, China

Operation Analyst Intern June 2018 – Aug. 2018

• Supported auditing in the sales department by using UML to design compliance checking procedures

• Conducted A/B testing on the quality of the procedures and documented the results; Used statistical tools such as random sampling and hypothesis testing to compare different procedures

• Documented results and restrictions on the procedures; Formulated a report to the direct manager Case Western Reserve University Cleveland, Ohio

Teaching Assistant (Statistics Application in Business and Science) Feb. 2015 – May. 2015

• Web-scraped 60,000 rows of data regarding whether companies are privately or publicly owned

• Organized the data into binary variables using Python

• Compiled a report on the data quality and dumped the data into MySQL P R O J E C T S

• Booking Agent database: Researched on the ER diagram; Used subqueries and Boolean operators to show information of certain entertainers; Counted subtotal and grand total using grouping function;

• Bowling League database: Listed all bowlers and calculated their average scores; Used case when to avoid a divide by zero error; used window function to show quartiles of bowling scores;

• Sales Order database: Used temporary table to show sales that is greater than the average

• Electric power company

- Conducted hypothesis test on temperature data to see if the cooling system is working properly and nonparametric test to see the difference in the quality of different sensors;

- Identified type I and type II error and evaluate their risks to adjust our conclusions accordingly

• Dell company

- Used of simple linear regression to estimate the relationship between the return on a stock and the market return; Tested to see if Dell’s beta coefficient is greater than 1;

- Researched data distribution, equal variance, and log transformation to improve the model performance Tableau Visualization (https://public.tableau.com/profile/yuping.qi newProfile=&activeTab=0)

• map (map layering, paths, custom geocoding, background image), manipulate data (add dimensions, sort, filter), calculated field (ratio, count distinct, if, string functions), level of detail (LOD), data connection (blend, union), create dashboard, trend lines (p-value, best fit, r squared), forecast prediction interval, default setting Other Projects

• Conjoint Analysis, Churn Prediction, CAPM regression modeling, PCA with Ridge/Lasso/Elastic Net Regression, Operational Optimization and Sensitivity Analysis, Sentiment Analysis Using Text Vectorization (word2vec and TFIDF), Deep Learning Techniques(CNN, Random Forest, KNN)

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