Data Science, MS

The College of Computer and Information Science (CCIS) and the Department of Electrical and Computer Engineering (ECE) jointly offer a new interdisciplinary Master of Science program in data science. This program is designed to give students a comprehensive framework for processing, analyzing, modeling, and reasoning about data. Students will engage in an extensive course work intended to develop depth in data collection, storage, retrieval, processing, modeling, and visualization. Students will also be able to choose elective courses from a variety of offerings in CCIS, 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.

Course Requirements

The Master of Science in Data Science curriculum requires five core courses that jointly represent the essential technical skills in data science. Two courses in algorithms and data processing examine foundational concepts and languages, focusing on data representation, storage, manipulation, and query, as well as large-scale computing and optimization. Two core courses in machine learning and data mining introduce concepts on data modeling, representation, uncovering associations, and making predictions. The capstone course presents a holistic view of data science. Through experiential learning, students are exposed to the real-world challenges of implementing data science techniques to solve meaningful problems and effectively communicate with data. The courses are tailored toward technically or mathematically trained students.

The five core courses include:

  •            Two core courses in algorithms and data processing
  •            Two core courses in machine learning and data mining
  •           One core course in information visualization

Three elective courses are drawn from a selection of courses across Northeastern.

Learning Outcomes

Students who 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
  • 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
  • Manage, process, analyze, and visualize data at scale. This outcome allows students to handle data where the conventional information technology fail.

Placement Exams

Each incoming masters student, regardless of his or her background, takes 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.  If the student does not get a B or above in a part of the placement exam, then the student must take the corresponding introductory course.

  • Introduction to Programming for Data Science (DS 5010) The introductory course on fundamentals of programming and data structures covers data structures (lists, arrays, trees, hash tables, etc.), program design, programming practices, testing, debugging, maintainability, data collection techniques, and data cleaning and preprocessing. This course will have a class project where the students will use the concepts they learn to collect data from the web, clean, and preprocess and ready for analysis.
  • Introduction to Linear Algebra and Probability for Data Science (DS 5020) The introductory course on basics of statistics, probability, and linear algebra covers random variables, frequency distributions, measures of central tendency, measures of dispersion, moments of a distribution, discrete and continuous probability distributions, chain rule, Bayes' rule, correlation theory, basic sampling, matrix operations, trace of a matrix, norms, linear independence and ranks, inverse of a matrix, orthogonal matrices, range and null space of a matrix, the determinant of a matrix, positive semidefinite matrices, eigenvalues and eigenvectors.

Complete all courses and requirements listed below unless otherwise indicated.

Core Requirements

A cumulative GPA of 3.000 or higher is required in the following core courses:

Complete 4 semester hours from the following:4
Fundamentals of Computer Engineering
Data Management and Processing
DS 5110Introduction to Data Management and Processing4
Machine Learning and Data Mining
DS 5220Supervised Machine Learning and Learning Theory4
DS 5230Unsupervised Machine Learning and Data Mining4
Presentation and Visualization
DS 5500Information Visualization: Applications in Data Science4


Complete 12 semester hours from the following:12
College of Computer and Information Science
Foundations of Artificial Intelligence
Natural Language Processing
Information Retrieval
Empirical Research Methods
Special Topics in Artificial Intelligence
Special Topics in Database Management
College of Engineering
Special Topics in Civil Engineering
Computer Vision
High-Performance Computing
Information Theory
Combinatorial Optimization
Advanced Computer Vision
Advanced Machine Learning
Data Mining for Engineering Applications
Data Mining in Engineering
Statistical Methods in Engineering
College of Social Sciences and Humanities
Dynamic Modeling for Environmental Decision Making
Big Data for Cities
Geographic Information Systems for Urban and Regional Policy
Urban Theory and Science
Advanced Spatial Analysis of Urban Systems
Perspectives on Social Science Inquiry
Research Design
Quantitative Techniques
D'Amore-McKim School of Business
Business Analytics Fundamentals
Predictive Analytics for Managers
College of Science
Statistics for Bioinformatics
Complex Networks and Applications
Statistical Physics
Computational Physics
Network Science Data
Bouvé College of Health Sciences
Epidemiology and Population Health
Introduction to Epidemiology
Biostatistics in Public Health
Social Epidemiology
College of Arts, Media and Design
Game Design and Analysis
Data-Driven Player Modeling

Note: Students that take electives worth less than 4 credits (i.e., Bouvé, CSSH courses) will register for an accompanying data science project course in the same semester to bring the cumulative credits to 4. In order to earn this additional credit, students will be expected to work with faculty to design an additional project in line with the curricular aims of their chosen elective and the data science core learning outcomes.

Program Credit/GPA Requirements

32 total semester hours required
Minimum 3.000 GPA required