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The mission of our program is to produce graduates who possess a theoretical and practical understanding of many classical and modern statistical modeling and machine learning techniques who use contemporary programming languages to scrape, clean, organize, query, summarize, visualize, and model large volumes and varieties of data and who use their knowledge and skills to successfully solve real-world data-driven business problems and to communicate those solutions effectively. Applications for this program end in Spring 2026. Possess a theoretical understanding of classical statistical models (e.g., generalized linear models, linear time series models, etc.), as well as the ability to apply those models effectively Possess a theoretical understanding of machine learning techniques (e.g., random forests, neutral networks, naive Bayes, k-means, etc.), as well as the ability to apply those techniques effectively Effectively use modern programming languages (e.g., R, Python, SQL, etc.) and technologies (AWS, Hive, Spark, Hadoop, etc.) to scrape, clean, organize, query, summarize, visualize, and model large volumes and varieties of data
