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Academic Catalog 2021-2022

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  • Data Science, MS

Data Science, MS

2021-2022 Edition

  • Undergraduate
  • College of Professional Studies Undergraduate
  • Graduate
    • General Admission and Transfer Credit
    • Information for Entering Students
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    • PhD Programs
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    • D'Amore-​McKim School of Business
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      • Academic Policies and Procedures
      • Computer Science
        • Computer Science, PhD
        • Artificial Intelligence, MS
        • Data Science, MS
        • Data Science, MS—Align
        • Robotics, MS
        • Computer Science, MSCS
        • Computer Science, MSCS—Align
        • Cloud Software Development, Graduate Certificate
        • Computer Science, Graduate Certificate
        • Data Analytics, Graduate Certificate
      • Cybersecurity
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      • Interdisciplinary
    • College of Engineering
    • Bouvé College of Health Sciences
    • School of Law
    • College of Professional Studies
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    • Faculty
    • Appendix
  • Course Descriptions
  • Catalog Archives
  • Overview
  • Program Requirements

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.

Course List
Code Title Hours
Complete 20 semester hours from the following:
Data Management and Processing
DS 5110Introduction to Data Management and Processing4
Algorithms
Complete 4 semester hours from the following:4
CS 5800
Algorithms
EECE 7205
Fundamentals of Computer Engineering
Machine Learning and Data Mining
DS 5220Supervised Machine Learning and Learning Theory4
DS 5230Unsupervised Machine Learning and Data Mining4
Presentation and Visualization
DS 5500Capstone: Applications in Data Science4

Electives

Course List
Code Title Hours
Complete 12 semester hours from the following: 112
Khoury College of Computer Sciences
CS 5100
Foundations of Artificial Intelligence
CS 5180
Reinforcement Learning and Sequential Decision Making
CS 5200
Database Management Systems
CS 5335
Robotic Science and Systems
CS 5340
Computer/Human Interaction
CS 5610
Web Development
CS 6120
Natural Language Processing
CS 6200
Information Retrieval
CS 6240
Large-Scale Parallel Data Processing
CS 6350
Empirical Research Methods
CS 6620
Fundamentals of Cloud Computing
CS 6650
Building Scalable Distributed Systems
CS 7140
Advanced Machine Learning
CS 7150
Deep Learning
CS 7180
Special Topics in Artificial Intelligence
CS 7200
Statistical Methods for Computer Science
CS 7280
Special Topics in Database Management
CS 7290
Special Topics in Data Science
DS 7990
Thesis
DS 7995
Project
College of Engineering
CIVE 7100
Time Series and Geospatial Data Sciences
CIVE 7388
Special Topics in Civil Engineering
EECE 5639
Computer Vision
EECE 5640
High-Performance Computing
EECE 5644
Introduction to Machine Learning and Pattern Recognition
EECE 7337
Information Theory
EECE 7370
Advanced Computer Vision
EECE 7397
Advanced Machine Learning
IE 7275
Data Mining in Engineering
IE 7280
Statistical Methods in Engineering
College of Social Sciences and Humanities
ECON 5140
Applied Econometrics
PPUA 5261
Dynamic Modeling for Environmental Decision Making
PPUA 5262
Big Data for Cities
PPUA 5263
Geographic Information Systems for Urban and Regional Policy
PPUA 5266
Urban Theory and Science
PPUA 7237
Advanced Spatial Analysis of Urban Systems
College of Science
ENVR 5563
Advanced Spatial Analysis
PHYS 5116
Complex Networks and Applications
PHYS 7305
Statistical Physics
PHYS 7321
Computational Physics
PHYS 7331
Network Science Data
Bouvé College of Health Sciences
PHTH 5202
Introduction to Epidemiology
PHTH 5210
Biostatistics in Public Health
PHTH 6224
Social Epidemiology
College of Arts, Media and Design
GSND 5110
Game Design and Analysis
GSND 6350
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.

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