MPS in Information Science student with experience in data analysis, database design, ETL, and business intelligence projects. Skilled in Python, R, SQL, Tableau, Azure, and WhereScape, with project experience spanning d…
MPS in Information Science student with experience in data analysis, database design, ETL, and business intelligence projects. Skilled in Python, R, SQL, Tableau, Azure, and WhereScape, with project experience spanning data manipulation, statistical analysis, visualization, and relational database implementation.
Collaborated with a multidisciplinary team to define user operations and meticulously designed the relational database for efficient streaming the data about details of courses the university has.
Implemented the database using Azure Data Studio and Azure SQL Database, including creating and maintaining the ERD, writing Data Definition Language (DDL) and Data Manipulation Language (DML) queries.
Integrated the frontend and database through Azure SWA and built-in APIs to ensure seamless data flow.
Led the dimensional database design and ETL using WhereScape, focusing on revenue and customer behavior analysis.
Implemented dataset structure, data filtering and type conversion techniques to maintain data integrity and relevance.
Developed and calculated new columns within the data model, enhancing the granularity and utility of the datasets.
Navigated challenges during the Q2 2020 pandemic, experienced a rapid 80% decrease in service units and indicated which department and staff gamed the reservation system with cancellation rate approaching 50%, visualized in Tableau.
Manipulated raw data using Python to remove duplicates and outliers, modify zip code data format and impute missing value.
Applied EDA and statistical analysis to conclude users prefer to book entire home at Manhattan midtown.
Created interactive map dashboard in Tableau to visualize city criminal risk, social event distribution, building years that affected the number of Airbnb housing options and customer decision in different locations.
Increased R2 score of linear regression between price and other potential factors by reducing price data variance.
Performed data preprocess and cleaning using NumPy and Pandas such as missing data imputation, data extraction, duplicates reduction and outlier detection.
Applied exploratory analysis using Matplotlib and Seaborn to visualize and present the findings.
Conducted the dashboards and findings with the company’s actual situations to come up with possible solutions for improving profits and increasing customer loyalty under such circumstances.
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