Sheryl L. Kosakowski, MS, MBA
Senior Director of Graduate Admissions
Following a comprehensive, in-depth and well-paced curriculum, Bay Path's Applied Data Science program leads students through statistical topics from maximum likelihood, hypothesis testing, and survey sampling, to re-sampling methods, time series analysis, and Bayesian analysis, to various regression methods, and eventually to a detailed treatment of machine learning methods such as support vector machines, tree-based methods, neural networks, graphical models, EM algorithms and ensemble learning. Both applied aspects and theoretical principles will be emphasized throughout this advanced program. Important programming tools such as SQL, R, Python, and SAS will be utilized in our SAS joint certificate program.
Our outstanding faculty team boasts many academic achievements, years of industry experience, and includes leaders of cutting-edge data teams. For recent graduates, our program will provide you with a solid foundation in both theory and applications to launch your career in a desirable area. For seasoned business analysts, this will serve as a "bridge program" for effective collaboration with data scientists and for better business insights in your industry.
Dr. Ning Jia, program director of the MS in Applied Data Science program at Bay Path University, has been invited to speak on October 18 at TDWI ACCELERATE Seattle, the leading conference for analytics and data science training, brings together the brightest minds in data to share their expertise and help you realize your analytics goals, faster. In her 90-minute presentation, Dr. Jia will discuss a wide array of methods and algorithms that have been developed for feature selection. She will give important guidelines for their business applications, with a special focus on ensemble modeling approach, or how to combine multiple methods as well as domain knowledge effectively in one feature selection process, and how to assess and select different approaches in practice. Click Here to learn more.