Align Master of Science in Data Science

Program Format
On-Ground|
Curriculum
Application Deadlines
Please note: This program will begin at the Silicon Valley campus starting Fall 2021.


Domestic students: August 15, 2021
International students: April 15, 2021
Credits Required for Graduation
48 Semester Hours

Overview

The Align Master of Science in Data Science (Align MSDS) program’s first two semesters comprises four bridge courses that will prepare students from any undergraduate discipline for graduate-level coursework. The Align MS in Data Science delivers a comprehensive foundation for processing, modeling, analyzing, and working with data. This program provides research and coursework in artificial intelligence, machine learning, databases, statistics (hypothesis testing), and the theory of language programming. You will learn to collect data from numerous sources, explore and hypothesize, utilize machine learning models, and effectively analyze and communicate your models and predictions. These important skills, coupled with opportunities for experiential learning, will allow you to chart your own unique career course in this highly competitive and rewarding field.

Graduates from this program go on to a range of careers including, data scientist, data analyst, quantitative researcher, machine learning engineer, natural language processing/OCR engineer. There is demand for data science specialists in every industry. The U.S. Bureau of Labor Statistics reports that the demand for data science skills will drive a 28% rise in employment in the field through 2026.

Learning Outcomes

  • 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.

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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