• Implemented data dimension reduction techniques, such as PCA, Autoencoder, NMF, and UMAP, to extract relevant features from large datasets • Developed a robust patient segmentation algorithm by incorporating consistent features in clustering • Utilized data clustering techniques such as K-Means Clustering to segment diabetic patients’ data and identify key demographic groups

• Discussed experimental and survey designs with three clients per semester• Completed preliminary EDA reports from wrangled and cleaned data by using R/Python and PostgreSQL• Selected statistical methods and translated statistical analyses into understandable recommendations to clients

• Forecasted and valuated for the stock prices of ferrous / non-ferrous companies by using DCF model• Researched targeted companies by using fundamental analysis and technical analysis• Published stock research report of five companies to identify their worthy of being invested

• Collected 5-year data from American Community Survey to identify factors for economic development• Designed interactive data dashboards using R Shiny to provide clients with easy access to data insights

• Organized ESG data by grouping, filtering, and using advanced functions in Excel to analyze data through pivot table and track the performance of investments• Created interactive dashboard in Tableau to forecasted future trend and present insights to stakeholders