Data Analytics Engineering, MS

The Department of Mechanical and Industrial Engineering (MIE) offers the Master of Science in Data Analytics Engineering in order to meet the current and projected demand for a workforce trained in analytics. This degree program offers students an opportunity to train for industry jobs or to acquire rigorous analytical skills and research experience to prepare for a doctoral program in health, security, and sustainability at Northeastern University. While the core courses for this program are offered by the College of Engineering, elective courses can be chosen from diverse disciplines spread across various colleges at Northeastern. The MS degree in data analytics engineering is designed to enable the graduating students to address the growing need for professionals who are trained in advanced data analytics and can transform large streams of data into understandable and actionable information for the purpose of making decisions. The key sectors that require analytics professionals include healthcare, smart manufacturing, supply chain and logistics, national security, defense, banking, finance, marketing, and human resources.

The Master of Science in Data Analytics Engineering is designed to help students acquire knowledge and skills to:

  • Discover opportunities to improve systems, processes, and enterprises through data analytics
  • Apply optimization, statistical, and machine-learning methods to solve complex problems involving large data from multiple sources
  • Collect and store data from a variety of sources, including Internet of Things (IoT), an integrated network of devices and sensors, customer touch points, processes, social media, and people
  • Work with technology teams to design and build large and complex SQL databases
  • Use tools and methods for data mining, big-data algorithms, and data visualization to generate reports for analysis and decision making
  • Create integrated views of data collected from multiple sources of an enterprise
  • Understand and explain results of data analytics to decision makers
  • Design and develop analytics projects

This degree program seeks to prepare students for a comprehensive list of tasks including collecting, storing, processing, and analyzing data; reporting statistics and patterns; drawing conclusions and insights; and making actionable recommendations.

General Degree Requirements

To be eligible for admission to any of the MS degree programs, a prospective student must hold a Bachelor of Science degree in engineering, science, mathematics, or an equivalent field. Students in all master’s degree programs must complete a minimum of 32 semester hours of approved course work (exclusive of any preparatory courses) with a minimum grade-point average (GPA) of 3.000. Students can complete a master's degree by pursuing one of the three tracks: course work option, project option, and thesis option. Specific degree requirements for each of these tracks can be found under the Program Requirements tab. Students may pursue any master's program either on a full-time or part-time basis; however, certain restrictions may apply.

Specific Degree Requirements

Core courses for the MS in data analytics engineering provide students with a foundation in operations research, statistics, data and knowledge engineering, and visualization. Students can select electives from a wide range of fields including business, engineering, healthcare, manufacturing, and urban communities/cities. These courses are designed to provide students with a strong understanding of probability and statistics, optimization methods, data mining, database design, and visualization. Elective courses provide students with the knowledge and understanding of descriptive, prescriptive, diagnostic, and predictive analytics as applied to a specific field of interest such as business, healthcare, manufacturing, and urban communities/cities. Alternatively, students can select their electives so that they can prepare for a doctoral program by taking advanced courses in mathematics, statistics, machine learning, and pattern recognition. 

Special Ethics Requirement

All MIE graduate students are required to complete a brief online session on Responsible Conduct of Research and Plagiarism during their first semester of full-time study. All enrolled students will be sent proper instructions on how to complete this assignment and satisfy this important requirement. The outcome of the online session will be filed with the student’s records.

Academic and Research Advisors

All nonthesis students are advised by the academic advisor designated for their respective concentration or program. Students doing thesis option must find a research advisor within their first year of study and may have thesis reader(s) at the discretion of their research advisor. The research advisor must be a full-time or jointly appointed faculty or affiliated member of the MIE department; otherwise, a petition must be filed and approved by the MIE graduate affairs committee. If the research advisor is outside the MIE department, a faculty member with 50 percent or more appointments in the MIE department must be chosen as the co-advisor. Thesis option students are advised by the academic advisor designated for their concentration before they select their research advisor(s).

Plan of Study and Course Selection

It is recommended that all new students attend orientation sessions held by the MIE department and the Graduate School of Engineering to acquaint themselves with the course work requirements and research activities of the department as well as with the general policies, procedures, and expectations.

In order to receive proper guidance with their course work needs, all MS students are strongly encouraged to complete and submit a fully signed Plan of Study (PS) to the department before enrolling in second-semester courses. This form helps the students manage their course work as well as helps the department to plan for requested course offerings. The PS may be modified at any time as students progress in their degree programs. However, requests for changes in the PS must be processed before the requested change actually takes place. A revised PS form must also be approved and signed.

Each student’s academic advisor must approve all courses prior to registration. Students may only use courses taken with the approval of the academic advisor toward the 32-semester-hour minimum requirement. However, students may petition the MIE graduate affairs committee to substitute graduate-level courses from outside the approved list of electives.

Students pursuing study or research under the guidance of a faculty member can choose the project option by taking Master’s Project (ME 7945) or Master’s Project (IE 7945). An MS project must be petitioned to the MIE graduate affairs committee and approved by both the faculty member (instructor for Master's Project) and the student's academic advisor. The petition must clearly state the reason for taking the course; a brief description of the goals; as well as the expected outcomes, deliverables, and grading scheme. 

Students doing the course work option may petition the MIE graduate affairs committee to substitute up to a 4-semester-hour Independent Study (ME 7978) or Independent Study (IE 7978). An independent study must be approved by the academic advisor. The petition must clearly state the reason for taking the course; a brief description of the goals; as well as the expected outcomes, deliverables, and grading scheme. Students in other options (i.e., thesis or project) are not eligible to take independent study. When taking thesis or project options, the independent study course cannot be taken.

Options for MS Students (course work only, project, or thesis)

Students accepted into any of the MS programs in the MIE department can choose one of the three options: course work only, project, research project or MS thesis. Moreover, students who receive financial support from the university in the form of a research, teaching, or tuition assistantship must complete an 8-semester-hour thesis.

Students who complete the thesis option must make a presentation of their thesis before approval by the department. The MS thesis presentation shall be publicly advertised at least one week in advance, and all faculty members and students may attend and participate. If deemed appropriate by the research advisor, other faculty members may be invited to serve as "thesis readers" to provide technical opinions and judge the quality of the thesis and presentation.  

Change of Program/Concentration

Students enrolled in any of the MIE department programs or concentrations may change their current program or concentration no sooner than the beginning of their second full-time semester of study. In order for the program or concentration change request to be considered by the MIE graduate affairs committee, the student must be in good academic standing and have completed at least 8 semester hours of required course work in their sought program at Northeastern. See here for instructions on how to request a program or concentration change.

Graduate Certificate Options

Students enrolled in a graduate degree program in the College of Engineering have the opportunity to pursue an engineering graduate certificate in addition to or in combination with the MS degree. For more information please refer to Graduate Certificate Programs. Please note that students pursuing the Master of Science in Data Analytics Engineering are not eligible for the Graduate Certificate in Data Mining.

Gordon Institute of Engineering Leadership

Master's Degree in Data Analytics Engineering with Graduate Certificate in Engineering Leadership

Students may complete a Master of Science in Data Analytics Engineering in addition to earning a Graduate Certificate in Engineering Leadership. Students must apply and be admitted to the Gordon Engineering Leadership Program in order to pursue this option. The program requires fulfillment of the 16-semester-hour curriculum required to earn the Graduate Certificate in Engineering Leadership, which includes an industry-based challenge project with multiple mentors. The integrated 40-semester-hour degree and certificate will require 24 hours of advisor-approved data analytics technical courses.

Engineering Leadership

Engineering Business

Master's Degree in Data Analytics Engineering with Graduate Certificate in Engineering Business

Students may complete a Master of Science in Data Analytics Engineering in addition to earning a Graduate Certificate in Engineering Business. Students must apply and be admitted to the Galante Engineering Business Program in order to pursue this option. The program requires the applicant to have earned or be in a program to earn a Bachelor of Science in Engineering from Northeastern University. The integrated 32-semester-hour degree and certificate will require 16 semester hours of the data analytics engineering core courses and 16 semester hours from the outlined business-skill curriculum. The course work, along with participation in cocurricular professional development elements, earn the Graduate Certificate in Engineering Business.

Engineering Business

Complete all courses and requirements listed below unless otherwise indicated. 

Core Requirements

IE 5374Special Topics in Industrial Engineering (Data Visualization Engineering)4
IE 6200Engineering Probability and Statistics4
IE 7275Data Mining in Engineering4
IE 7280Statistical Methods in Engineering4
INFO 6210Data Management and Database Design4
OR 6205Deterministic Operations Research4

Options

Complete one of the following options:

Course Work Option

Complete 8 semester hours from the course list below.8

Project Option

ME 7945Master’s Project4
Complete 4 semester hours from the course list below.4

Thesis Option 

ME 7990Thesis 18

Course List  

Business Administration
BUSN 6320Business Analytics Fundamentals1
BUSN 6324Predictive Analytics for Managers1
BUSN 6336Data Mining for Managers1
BUSN 6340Modeling for Business Analytics for Managers1
Civil Engineering and Environmental Engineering
CIVE 7100Time Series and Geospatial Data Sciences4
CIVE 7342System Identification4
Computer Science
CS 5002Discrete and Data Structures4
CS 5004Object-Oriented Design4
CS 5006Algorithms2
CS 5100Foundations of Artificial Intelligence4
CS 5150Game Artificial Intelligence4
CS 5200Database Management Systems4
CS 5310Computer Graphics4
CS 5335Robotic Science and Systems4
CS 5330Pattern Recognition and Computer Vision4
CS 5800Algorithms4
CS 6120Natural Language Processing4
CS 6140Machine Learning4
CS 6200Information Retrieval4
CS 6220Data Mining Techniques4
Computer Systems Engineering
CSYE 7250Big Data Architecture and Governance4
Criminal Justice
CRIM 7718Advanced Data Analysis4
Data Science
DS 5010Introduction to Programming for Data Science4
DS 5020Introduction to Linear Algebra and Probability for Data Science4
DS 5110Introduction to Data Management and Processing4
DS 5220Supervised Machine Learning and Learning Theory4
DS 5230Unsupervised Machine Learning and Data Mining4
Electrical and Computer Engineering
EECE 5155Wireless Sensor Networks and the Internet of Things4
EECE 5639Computer Vision4
EECE 5644Introduction to Machine Learning and Pattern Recognition4
EECE 7204Applied Probability and Stochastic Processes4
EECE 7312Statistical and Adaptive Signal Processing4
EECE 7397Advanced Machine Learning4
Engineering Management
EMGT 5220Engineering Project Management4
EMGT 6225Economic Decision Making4
EMGT 6305Financial Management for Engineers4
Health Informatics
HINF 5101Introduction to Health Informatics and Health Information Systems3
HINF 5102Data Management in Healthcare3
HINF 5200Theoretical Foundations in Personal Health Informatics4
HINF 5301Personal Health Technologies: Field Deployment and System Evaluation4
HINF 6202Business of Healthcare Informatics3
HINF 6240Improving the Patient Experience through Informatics3
HINF 6335Management Issues in Healthcare Information Technology3
HINF 6400Introduction to Health Data Analytics3
Industrial Engineering
IE 5374Special Topics in Industrial Engineering (Spreadsheet Modeling for industrial Engineering)4
IE 5400Healthcare Systems Modeling and Analysis4
IE 5630Biosensor and Human Behavior Measurement4
IE 6300Manufacturing Methods and Processes4
IE 7200Supply Chain Engineering4
IE 7215Simulation Analysis4
IE 7285Statistical Quality Control4
IE 7290Reliability Analysis and Risk Assessment4
Information Systems
INFO 6101Data Science Engineering with Python4
INFO 6205Program Structure and Algorithms4
INFO 6215Business Analysis and Information Engineering4
INFO 7275Advanced Database Management Systems4
INFO 7290Data Warehousing and Business Intelligence4
INFO 7330Information Systems for Healthcare-Services Delivery4
INFO 7390Advances in Data Sciences and Architecture4
INFO 7610Special Topics in Natural Language Engineering Methods and Tools4
Mathematics
MATH 5131Introduction to Mathematical Methods and Modeling4
MATH 7234Optimization and Complexity4
MATH 7241Probability 14
MATH 7340Statistics for Bioinformatics4
MATH 7341Probability 24
MATH 7342Mathematical Statistics4
MATH 7343Applied Statistics4
MATH 7344Regression, ANOVA, and Design4
MATH 7345Nonparametric Methods in Statistics4
MATH 7346Time Series4
Mechanical Engineering
ME 6201Mathematical Methods for Mechanical Engineers 24
ME 7205Advanced Mathematical Methods for Mechanical Engineers4
Operations Research
OR 6205Deterministic Operations Research4
OR 7230Probabilistic Operation Research4
OR 7235Inventory Theory4
OR 7240Integer and Nonlinear Optimization4
OR 7245Network Analysis and Advanced Optimization4
OR 7310Logistics, Warehousing, and Scheduling4
OR 7440Operations Research Engineering Leadership Challenge Project 14
Physics
PHYS 5116Complex Networks and Applications4
PHYS 7331Network Science Data4
PHYS 7332Network Science Data 24
Public Policy and Urban Affairs
PPUA 5261Dynamic Modeling for Environmental Decision Making4
PPUA 5262Big Data for Cities4
PPUA 5263Geographic Information Systems for Urban and Regional Policy4
PPUA 5301Introduction to Computational Statistics4
PPUA 5302Information Design and Visual Analytics4
PPUA 7237Advanced Spatial Analysis of Urban Systems4

Program Credit/GPA Requirements

32 total semester hours required
Minimum 3.000 GPA required