Data Analytics Engineering, MS

Master of Science (MS) Degree in Data Analytics Engineering

The Department of Mechanical and Industrial Engineering (MIE) offers Master of Science (MS) 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 MS 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 GPA of 3.000. Students may pursue any master's program either on a full-time or part-time basis; however, certain restrictions may apply.

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. Other students may choose to complete their master's degree by pursuing a thesis, project, or course work option. Students who complete the thesis option must make a presentation of their thesis before approval by the department.

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, diagnostics, 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 Course Requirements

All MIE MS students doing thesis or project options (excluding MS students in engineering management and Gordon Engineering Leadership programs) must complete Technical Writing (MEIE 6800) and Research Seminar in Mechanical and Industrial Engineering (MEIE 6850) , preferably during the first year of their full-time study. If appropriate, part-time students may petition the graduate affairs committee to waive these requirements. Students in combined BS/MS programs must take Research Seminar in Mechanical and Industrial Engineering (MEIE 6850) as part of their course work requirement, while Research Seminar in Mechanical and Industrial Engineering (MEIE 6850) is optional for these students.

All MIE graduate students are also required to complete a brief online session on Responsible Conduct of Research and Plagiarism in one of these courses. 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 co-advisor. Thesis option students are advised by the academic advisor of 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. Plan of Study may be modified at any time as students progress in their degree programs. However, requests for changes in 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 Project Option by taking Master’s Project (ME 7945).  An MS project must be petitioned to the MIE graduate affairs committee and approved by both faculty member (instructor) and the academic (concentration) 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). 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.

Graduate Certificate Options

Students who are officially accepted into a graduate degree program in the College of Engineering may apply to pursue one of the following graduate engineering certificates in addition to the MS or PhD. Please visit the links below for additional information about each graduate engineering certificate program, related requirements, and how to apply.

Chemical Engineering

Computer Systems Engineering

Energy Systems

Engineering Management

Industrial Engineering

Telecommunication Systems Management

Gordon Institute of Engineering Leadership

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

Students may complete a master's degree 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 16 hours of advisor-approved Data Analytics technical courses.

Engineering Leadership

Complete all courses and requirements listed below unless otherwise indicated. 

General Requirements

EECE 5642Data Visualization4
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

Certificate Option

Students completing this option receive a Graduate Certificate in addition to the master’s degree. Students should consult their faculty advisor regarding the certificate options.

Complete 16 semester hours of graduate certificate course work.16

Course List  

Bouvé College of Health Sciences Electives
HINF 5102Data Management in Healthcare3
HINF 6220Database Design, Access, Modeling, and Security3
College of Computer and Information Science Electives
CS 6140Machine Learning4
CS 6200Information Retrieval4
CS 6240Parallel Data Processing in MapReduce4
DS 60204
DS 60304
IA 5050Data Mining in Cyberspace4
College of Engineering Electives
CIVE 7100Applied Time Series and Spatial Statistics4
CSYE 7200Big-Data System Engineering Using Scala4
CSYE 7245Big-Data Systems and Intelligence Analytics4
CSYE 7270Building Virtual Environments4
EECE 5644Introduction to Machine Learning and Pattern Recognition4
EECE 7313Pattern Recognition4
EECE 7397Advanced Machine Learning4
EMGT 5220Engineering Project Management4
IE 5630Biosensor and Human Behavior Measurement4
IE 7270Intelligent Manufacturing4
IE 7615Neural Networks in Engineering4
INFO 7250Engineering of Big-Data Systems4
INFO 7290Data Warehousing and Business Intelligence4
OR 7245Network Analysis and Advanced Optimization4
OR 7250Multi-Criteria Decision Making4
College of Science Electives
MATH 7341Probability 24
MATH 7342Mathematical Statistics4
MATH 7343Applied Statistics4
MATH 7344Regression, ANOVA, and Design4
PHYS 5116Complex Networks and Applications4
PHYS 7331Network Science Data4
College of Social Science and Humanities Electives
PPUA 5262Big Data for Cities3
PPUA 7237Advanced Spatial Analysis of Urban Systems3
D'Amore-McKim School of Business Electives
BUSN 6320Business Analytics Fundamentals1
BUSN 6324Predictive Analytics for Managers1
BUSN 6326Introduction to Big Data and Digital Marketing Analytics1

Program Credit/GPA Requirements

32 total semester hours required
Minimum 3.000 GPA required

1

A thesis is required for all students who receive financial support from the university in the form of a research, teaching, or tuition assistantship.  The thesis topic should cover one or more of the areas from statistics, mathematics, optimization, data mining, machine learning, database design, big data, visualization tools, or forecasting methods. The thesis should train students for research in data and operations analytics and/or prepare them for a doctoral program.