Applying Statistical Modeling & Machine Learning to Perform Time-Series Forecasting
Techniques: Time-series forecasting.
I used publicly available Airbnb data to perform time-series forecasting using statistical and machine learning models.

Adventures in big data wonderland: Going down the Pinterest Path
Techniques: Hadoop, MapReduce, big data, network science.
I productionized graphical network metrics in Python in a distributed manner at scale, using a Hadoop framework (Hive).

Mention of my poster at Neural-IPS Conference 2016
Techniques: Presentation.
I presented my publication at a poster session for the Women in Machine Learning (WiML) Workshop at the Neural-IPS Conference 2016.

Review of my roundtable session at Grace Hopper Conference 2017
Techniques: Presentation.
I hosted a workshop on transitioning into data science at the Grace Hopper Conference 2017.

YouTube video of my tutorial on time-series forecasting at PyData LA 2018
Techniques: Presentation.
I led a tutorial on time-series forecasting at PyData LA 2018.

Modeling Predictors of Health Outcomes Using R, Python, and STATA
Techniques: regression, generalized additive models.
I used publicly available data from the Framingham Heart Study to analyze the role of systolic blood pressure in predicting coronary heart disease using R, Python, and Stata, with a detailed written report of my findings using Microsoft Word.

Sentiment Analysis On Gilmore Girls' Subreddit Data from Reddit Crawler
Techniques: reddit crawling, sentiment analysis, natural language processing.
I used publicly available data from Reddit, an online community, using the Gilmore Girls' subreddit as training and testing data to predict sentiment of subreddit comments using Python.

Visualizing Data Using Tableau
Techniques: Data visualization.
I used U.S. Center for Disease Control (CDC) data to visualize flu vaccination rates during the 2013-2014 flu season using Tableau.