東北大學(xué)

The innovative ALIGN bridge program to the Master of Science in Data Science is designed for students with a BS/BA degree from all backgrounds. During the first semester of year one, students are expected to take foundational courses in computer science fundamentals, as well as a course in data structures/discrete mathematics. During their second semester, students will take coursework in programming for data science, as well as linear algebra and probability. Upon successful completion of the second semester, students in good standing will matriculate into the Master of Science in Data Science program.

The Master of Science in Data Science program is designed to give students a comprehensive framework for reasoning about data. Students will engage in extensive coursework intended to develop depth in data collection, storage, retrieval, manipulation, visualization, modeling, and interpretation. Students will also be able to choose elective courses from a variety of offerings in Khoury, the College of Engineering (COE), and throughout the campus to explore areas that generate data or specialized data science applications. Successful program graduates will be well-positioned to attain data scientist and data engineer positions in a fast-growing field or to progress into doctoral degrees in related disciplines.

Learning Outcomes

Students who successfully complete the MS degree will be able to: Collect data from numerous sources (databases, files, XML, JSON, CSV, and web APIs) and integrate them into a form in which the data is fit for analysis; Use R and Python to explore data, produce summary statistics, perform statistical analyses; use standard data mining and machine-learning models for effective analysis; Select, plan, and implement storage, search, and retrieval components of large-scale structured and unstructured repositories; Retrieve data for analysis, which requires knowledge of standard retrieval mechanisms such as SQL and XPath, but also retrieval of unstructured information such as text, image, and a variety of alternate formats; Manage, process, analyze, and visualize data at scale. This outcome allows students to handle data where conventional information technology fails; Match the methodological principles and limitations of machine learning and data mining methods to specific applied problems and communicate the applicability and the advantages/disadvantages of the methods in the specific problem to nondata experts; Carry out the full data analysis workflow, including unsupervised class discovery, supervised class comparison, and supervised class prediction; summarize, interpret, and communicate the analysis of results; Organize visualization of data for analysis, understanding, and communication; choose appropriate visualization method for a given data type using effective design and human perception principles; Develop methods for modeling, analyzing, and reasoning about data arising in one or more application domains such as social science, health informatics, web and social media, climate informatics, urban informatics, geographical information systems, business analytics, bioinformatics, complex networks, public health, and game design.