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Data Analytics including Python, R, SQL, Tableau

New York City, NY
March 18, 2020

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Tianqi Liang

*** ******** ***, *** ****, NY ***** 651-***-****


A client-focused and detail-oriented professional with strong analytical skills integral to solving business problems. Proven ability to effectively communicate with key stakeholders utilizing visualization techniques. RELATED SKILLS

l SQL & Database Management: ER/Studio

l Data Analysis & Modeling: R, Python(Pandas, Numpy, Scikit-learn), Advanced Exel & MS office l Statistics: Hypothesis Test, ARIMA, SES, VERMA

l Data Visualization: Tableau, Matplotlab


School of Professional Studies, Columbia University 09/18-12/19 Master of Science in Applied Analytics, GPA: 3.9/4.0 New York, NY Gabelli School of Business, Fordham University 09/14-05/18 Bachelor of Arts in Global Business (Global Finance and Business Economics), GPA: 3.7/4.0 New York, NY PROJECTS

Analytics of Data Related Jobs in H1B Dataset 10/19-12/19 Relational Database Management- Course: MySQL New York, NY l Used Python to web scrape the H1B dataset including 10 variables (employer, location, job title, salary, etc.) and 136,981 observations from the official website l Normalized the pre-processed data into 14 tables and completed the ETL steps by SQL in R studio l Tested more than 20 SQL queries and created a dashboard by Tableau to analyze and to present the results Book Publishing Company Database Model Assessment 06/19-07/19 Database Design- Course: Database Modeling New York, NY l Checked the correctness of a logical database model of a book publishing company l Identified camera settings (zoom, focus, timer, filter, etc.) of the database and relationships between tables l Ensured the correctness and completeness of the database by fixing its cardinality and adding missing entities l Assessed and scored the database from all 10 perspectives (readability, consistency, etc.) for a correct model Adoption Speed Prediction 03/19-05/19

Data Analytics- Course: Applied Analytics Frameworks & Methods New York, NY l Utilized a dataset covering 24 variables and 14,993 observations from PetFinder to predict adoption speed l Observed basic data patterns through clustering and selected features using stepwise and PCA l Built several predictive models: linear regression to get continuous adoption speed, simple tree and random forest to get adoption speed category results by R l Used text mining technics such as text frequency analysis and sentiment analysis for pet descriptions by R l Evaluated model performance with cross validation and obtained a final RMSE as 1.065 INTERNSHIP

Microsoft Azure Machine Learning Platform Assessment 09/19-12/19 Intern- Capstone Course Practical Internship Program New York, NY l Combined and processed a time series dataset regarding grocery sales with 17 variables using Python l Used empirical methods to select the most effective features and the optimal lag value l Built 4 time series models including SES, ARIMA, VERMA, and Automated Machine Learning models on both Azure Machine Learning Services and Machine Learning Studio l Selected the best model on the most suitable sub-dataset with the lowest MAPE score of 8.14% l Produced a report comparing and evaluating the performance of the platform (Services vs. Studio)

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