I am a Data Science professional with extensive experience in Data Engineering, Analytics and Data Modeling. Over the years, I have worked in diverse industries such as Healthcare, Finance, Mobile Analytics and Manufacturing. Currently, I am working as Vice President of Data Science at Cohen Veterans Network, Inc..
At Cohen Veterans Network, I lead the Data Science team. My team's primary objective is to extract information from the data to answer business questions. We are responsible for creating and maintaining Data Infrastructure, creating Reports and Analyses in Python, maintaining Tableau Server and creating Tableau Dashboards, and exploring Predictive Modeling use-cases.
The goal of this project was to build a system which predicts the next word based on the previous words. The prediction algorithm was built by computing n-grams from a corpus containing data from various blogs, tweets and news articles. Spark was used to process data and compute the n-grams, R was used to build the prediction alogrithm and Shiny was used to create a reactive web app.
The goal of this project was to build a system which recommends products to a bank customer, based on customer's historical purchases. The recommender system was based on historical data provided by Santander Bank and was built using Apache Spark and Scala. Zeppelin notebook was used for Exploratory Analysis, Data Cleaning and Model Fitting. The final model was implemented in an SBT project.
The goal of this project was to investigate correlation between controversy and view count for YouTube videos. For this project, I used YouTube Data API, Python and iPython Notebook.
This project was aimed at building a Prediction Model, to predict the exercise type based on various sensor measures. All the analysis and model fitting was done in R, using data provided by Jawbone.
The aim of this analysis was to answer the following questions -
Using a simple and intuitive Simulation to do Hypothesis Testing.
Running a Monte Carlo Simulation to estimate the value of Pi.
Verifying the solution to the famous Monty Hall Problem with a simulation.
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