Master of Science in Data Science

• 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, and 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 structure 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 the 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 the 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.

Unique Features

• This is an interdisciplinary program between Northeastern’s Khoury College of Computer Sciences and the College of Engineering.
• Courses are tailored toward technically or mathematically trained students.