Master’s of Science in Data Science

Program Format
On-Ground|Hybrid|Online|
Curriculum
Application Deadlines
Please note this program will begin at the Silicon Valley campus starting Winter 2022.

Domestic admissions deadline: December 15, 2021
International admissions deadline: October 26, 2021

The Master of Science in Data Science curriculum is tailored toward technically or mathematically trained students. To ensure that all students have the foundation necessary to be successful in this program, each incoming student must either complete two introductory courses at Northeastern or complete two placement exams administered one week prior to the beginning of the semester. The two exams cover fundamentals of computer science and programming skills and basic statistics, probability, and linear algebra. This admission requirement can also be fulfilled by successful completion of Introduction to Programming for Data Science (DS 5010) and Introduction to Linear Algebra and Probability for Data Science (DS 5020). The introductory courses are not counted as credit toward the degree but are included in the student’s cumulative grade-point average.
Credits Required for Graduation
32 Semester Hours

Overview

An extensive core curriculum—designed jointly by Khoury College of Computer Sciences and the College of Engineering faculty—enables you to develop depth in computational modeling, data collection and integration, data storage and retrieval, data processing, modeling and analytics, and visualization. Plus, electives from Khoury College of Computer Sciences, the College of Engineering, or a Northeastern partner college provide an opportunity to explore key contextual areas or more complex technical applications.

For the fourth year in a row, Glassdoor named data scientist as the best job in America. Additionally, Daniel Gutierrez, managing editor of insideBIGDATA, told Forbes, “The word on the street is there’s definitely a shortage of people who can do data science.” Given the job prospects and shortage of talent, now is the time to advance your career in the data science field.

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 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 fail
  • 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 principle
  • 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

Experiential Learning

Co-op makes the Northeastern graduate education richer and more meaningful. It provides master’s students with up to 12 months of professional experience that helps them develop the knowledge, awareness, perspective, and confidence to develop rich careers. In addition to the esteemed faculty, many students enroll in the master’s programs largely because of the successful co-op program.

Graduate students typically have an experiential work opportunity following their second semester. This could be a six- to eight-month co-op or a three- to four-month summer internship. Those who initially experience co-op may have the opportunity to seek an internship for the following summer, or vice versa.

Student participation in experiential education provides enhanced:

  • Learning, technical expertise, and occupational knowledge
  • Confidence, maturity, and self-knowledge
  • Job-seeking and job-success skills
  • Networking opportunities within your desired career path

Northeastern’s co-op program is based on a unique educational strategy that recognizes that classroom learning only provides some of the skills students will need to succeed in their professional lives. Our administration, faculty, and staff are dedicated to the university’s mission to “educate students for a life of fulfillment and accomplishment.” Co-op is closely integrated with our course curriculum and our advising system. The team of graduate co-op faculty within the Khoury College of Computer Sciences provides support for students in preparing for and succeeding in their co-ops.

These multiple connections make co-op at Northeastern an avenue to intellectual and personal growth: adding depth to classroom studies, providing exposure to career paths and opportunities, and developing in students a deeper understanding that leads to success in today’s world.


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