Khoury College of Computer Sciences 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 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.
Prerequisite Courses
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. Students are required to get a passing grade in each section of the placement exams in order to progress into the core courses in the degree program. If the student does not get a passing grade in a part of the placement exam, then the student must take the corresponding introductory course. Students that do not achieve a B or better in the introductory courses will be required to retake the courses.
Complete all courses and requirements listed below unless otherwise indicated.
Students should refer to the course numbering table for graduate course leveling.
Core Requirements
A cumulative GPA of 3.000 or higher is required in the following core courses.
| Code | Title | Hours |
|---|---|---|
| Complete 20 semester hours from the following: | ||
| Data Management and Processing | ||
| DS 5110 | Introduction to Data Management and Processing | 4 |
| Algorithms | ||
| Complete 4 semester hours from the following: | 4 | |
| Algorithms | ||
| Fundamentals of Computer Engineering | ||
| Machine Learning and Data Mining | ||
| DS 5220 | Supervised Machine Learning and Learning Theory | 4 |
| DS 5230 | Unsupervised Machine Learning and Data Mining | 4 |
| Presentation and Visualization | ||
| DS 5500 | Capstone: Applications in Data Science | 4 |
Electives
| Code | Title | Hours |
|---|---|---|
| Complete 12 semester hours from the following: 1 | 12 | |
| Khoury College of Computer Sciences | ||
| Foundations of Artificial Intelligence | ||
| Reinforcement Learning and Sequential Decision Making | ||
| Database Management Systems | ||
| Robotic Science and Systems | ||
| Computer/Human Interaction | ||
| Web Development | ||
| Natural Language Processing | ||
| Information Retrieval | ||
| Large-Scale Parallel Data Processing | ||
| Empirical Research Methods | ||
| Fundamentals of Cloud Computing | ||
| Building Scalable Distributed Systems | ||
| Advanced Machine Learning | ||
| Deep Learning | ||
| Special Topics in Artificial Intelligence | ||
| Statistical Methods for Computer Science | ||
| Special Topics in Database Management | ||
| Special Topics in Data Science | ||
| Thesis | ||
| Project | ||
| College of Engineering | ||
| Time Series and Geospatial Data Sciences | ||
| Special Topics in Civil Engineering | ||
| Computer Vision | ||
| High-Performance Computing | ||
| Introduction to Machine Learning and Pattern Recognition | ||
| Information Theory | ||
| Advanced Computer Vision | ||
| Advanced Machine Learning | ||
| Data Mining in Engineering | ||
| Statistical Methods in Engineering | ||
| College of Social Sciences and Humanities | ||
| Applied Econometrics | ||
| 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 | ||
| College of Science | ||
| Advanced Spatial Analysis | ||
| Complex Networks and Applications | ||
| Statistical Physics | ||
| Computational Physics | ||
| Network Science Data | ||
| Bouvé College of Health Sciences | ||
| Introduction to Epidemiology | ||
| Biostatistics in Public Health | ||
| Social Epidemiology | ||
| College of Arts, Media and Design | ||
| Game Design and Analysis | ||
| Data-Driven Player Modeling | ||
Program Credit/GPA Requirements
32 total semester hours required
Minimum 3.000 GPA required
| 1 | Students taking electives worth less than 4 credits (i.e., Bouvé courses) should enroll 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 are 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. |