Cornell Tech’s DSDA Masters allows you to develop a theoretical and practical understanding of the data-to-models-to-decisions pipeline in entrepreneurial environments by drawing from tools in machine learning, optimization and statistics. After going through the program, you will be able to develop and deploy algorithms to drive the decisions of online business such as e-retailers, ride-sharing systems and ad exchanges. Working with the faculty that are world leaders in optimization, statistics, causal analysis and machine learning, as well as being immersed in the tech start-up community of New York City, you will get comfortable with analyzing data, building decision models that turn data into decisions and deploying these models at large scale. In parallel with your academic technical courses, you will also complete Studio courses – an essential component of every Cornell Tech program that allow you to solve real business problems faced by tech companies and launch your own startups. During the course of Studio courses, you will work in cross-disciplinary teams with students in computers science, electrical engineering, business and law programs to study all facets of the problem you are focusing on. Who Should Apply? Cornell Tech’s DSDA Masters is ideal for students with a passion for data analysis that create insights to drive business decisions. The technical requirements are that you have completed one course on linear algebra, one course on intermediate-level probability and statistics, one course on calculus, and one course on programming before applying. Cornell Tech offers another Masters degree in Operations Research and Information Engineering (ORIE). As is the case with all Masters programs at Cornell Tech, both DSDA and ORIE programs have an entrepreneurial focus, but DSDA emphasizes the intersection of optimization, machine learning and probabilistic analysis with computing, whereas ORIE emphasizes the intersection of optimization, machine learning and probabilistic analysis with business. Technical Topics Covered Optimization for AI Statistics for Data Science Probabilistic Analysis Machine Learning Modeling Under Uncertainty Studio Topics Covered Entrepreneurship Intellectual Property Law for Non-Lawyers Leadership for Digital Transformation Product Management Startup Funding & Pitching View the curriculum