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.
Computer Systems Engineering
- Engineering Business
- Engineering Management
- Technology Systems Management
- Engineering Economic Decision Making
- Supply Chain Engineering Management
- Lean Six Sigma
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.
Complete all courses and requirements listed below unless otherwise indicated.
|EECE 5642||Data Visualization||4|
|IE 6200||Engineering Probability and Statistics||4|
|IE 7275||Data Mining in Engineering||4|
|IE 7280||Statistical Methods in Engineering||4|
|INFO 6210||Data Management and Database Design||4|
|OR 6205||Deterministic Operations Research||4|
Complete one of the following options:
Course Work Option
|Complete 8 semester hours from the course list below.||8|
|ME 7945||Master’s Project||4|
|Complete 4 semester hours from the course list below.||4|
|ME 7990||Thesis 1||8|
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|
|Bouvé College of Health Sciences Electives|
|HINF 5102||Data Management in Healthcare||3|
|HINF 6220||Database Design, Access, Modeling, and Security||3|
|College of Computer and Information Science Electives|
|CS 6140||Machine Learning||4|
|CS 6200||Information Retrieval||4|
|CS 6240||Parallel Data Processing in MapReduce||4|
|IA 5050||Data Mining in Cyberspace||4|
|College of Engineering Electives|
|CIVE 7100||Applied Time Series and Spatial Statistics||4|
|CSYE 7200||Big-Data System Engineering Using Scala||4|
|CSYE 7245||Big-Data Systems and Intelligence Analytics||4|
|CSYE 7270||Building Virtual Environments||4|
|EECE 5644||Introduction to Machine Learning and Pattern Recognition||4|
|EECE 7313||Pattern Recognition||4|
|EECE 7397||Advanced Machine Learning||4|
|EMGT 5220||Engineering Project Management||4|
|IE 5630||Biosensor and Human Behavior Measurement||4|
|IE 7270||Intelligent Manufacturing||4|
|IE 7615||Neural Networks in Engineering||4|
|INFO 7250||Engineering of Big-Data Systems||4|
|INFO 7290||Data Warehousing and Business Intelligence||4|
|OR 7245||Network Analysis and Advanced Optimization||4|
|OR 7250||Multi-Criteria Decision Making||4|
|College of Science Electives|
|MATH 7341||Probability 2||4|
|MATH 7342||Mathematical Statistics||4|
|MATH 7343||Applied Statistics||4|
|MATH 7344||Regression, ANOVA, and Design||4|
|PHYS 5116||Complex Networks and Applications||4|
|PHYS 7331||Network Science Data||4|
|College of Social Science and Humanities Electives|
|PPUA 5262||Big Data for Cities||3|
|PPUA 7237||Advanced Spatial Analysis of Urban Systems||3|
|D'Amore-McKim School of Business Electives|
|BUSN 6320||Business Analytics Fundamentals||1|
|BUSN 6324||Predictive Analytics for Managers||1|
|BUSN 6326||Introduction to Big Data and Digital Marketing Analytics||1|
Program Credit/GPA Requirements
32 total semester hours required
Minimum 3.000 GPA required
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.