Immersive Data Science program involving R, Python, SQL, and Sparks, as well as advanced statistics, visualization, and hands-on machine learning.- Scraped hedging positions for oil/gas companies from SEC (Securities and Exchange Commission) to investigate how company strategies varied against volatility in the market over time. Heavy emphasis on data cleaning. Performed time-series analysis to predict stock return based on engineered features and other macro-economic variables.- Predicted housing prices with Elastic Net, kernel ridge, SGD and XGBoost using ensemble and stacking regression techniques. The project scope also encompassed EDA, data cleaning and feature engineering.- Scraped movie data from RottenTomatoes and created a Tableau dashboard to visualize basic business performances and strategies for Pixar using Scrapy in Python. Investigated the reliability of reviews through various bias analysis. Visualized review contexts through word-clouds. - Created an R Shiny application to visualize suicide trends in major countries over the past thirty years. Produced interactive visualizations using Ggplot2 and Plotly, involving an animated times series to explore the relationship between suicide rate and GDP over time.

- Assisted in monitoring an online database of patient data. - Set up auto-monitoring reports of the most recent patient conditions using statistical software and Python.

- Facilitated the implementation of cloud-based ERP to clients through site visits.- Identified pain points in legacy systems and synthesized solutions to increase workflow efficiency.- Illustrated deliverables to end-users and channeled feedbacks to engineers.

- Prepared reagents and required chemicals for various experiments.- Cataloged, prepared and analyzed processed soil samples, reported results thereafter.