Joseph worked in equity research for Stifel Nicolaus, a mid-sized investment bank, for close to two years before joining NYC Data Science Academy. In his role, he wrote reports on publicly traded companies and worked extensively with financial models to project a company’s earnings, which sparked his interest in data science methods. He holds a BA in Economics from Yeshiva University and aims to apply analytics and data visualization to solving everyday problems. Some of his projects at NYC Data Science Academy include analyzing which characteristics determine a public school’s average SAT score using R, scraping the WSJ to identify article topics and potential bias, and using non-supervised machine learning methods on Expedia hotel bookings to segment customers and identify different customer behaviors.
NYC Data Science Academy’s mission is to provide accelerated data science training programs that prepare people for employment as data science professionals and to offer continuing education courses for professional development.
