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.
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.
Complete all courses and requirements listed below unless otherwise indicated.
|IE 5374||Special Topics in Industrial Engineering (Data Visualization Engineering)||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|
|BUSN 6320||Business Analytics Fundamentals||1|
|BUSN 6324||Predictive Analytics for Managers||1|
|BUSN 6336||Data Mining for Managers||1|
|BUSN 6340||Modeling for Business Analytics for Managers||1|
|Civil Engineering and Environmental Engineering|
|CIVE 7100||Time Series and Geospatial Data Sciences||4|
|CIVE 7342||System Identification||4|
|CS 5002||Discrete and Data Structures||4|
|CS 5004||Object-Oriented Design||4|
|CS 5100||Foundations of Artificial Intelligence||4|
|CS 5150||Game Artificial Intelligence||4|
|CS 5200||Database Management Systems||4|
|CS 5310||Computer Graphics||4|
|CS 5335||Robotic Science and Systems||4|
|CS 5330||Pattern Recognition and Computer Vision||4|
|CS 6120||Natural Language Processing||4|
|CS 6140||Machine Learning||4|
|CS 6200||Information Retrieval||4|
|CS 6220||Data Mining Techniques||4|
|Computer Systems Engineering|
|CSYE 7250||Big Data Architecture and Governance||4|
|CRIM 7718||Advanced Data Analysis||4|
|DS 5010||Introduction to Programming for Data Science||4|
|DS 5020||Introduction to Linear Algebra and Probability for Data Science||4|
|DS 5110||Introduction to Data Management and Processing||4|
|DS 5220||Supervised Machine Learning and Learning Theory||4|
|DS 5230||Unsupervised Machine Learning and Data Mining||4|
|Electrical and Computer Engineering|
|EECE 5155||Wireless Sensor Networks and the Internet of Things||4|
|EECE 5639||Computer Vision||4|
|EECE 5644||Introduction to Machine Learning and Pattern Recognition||4|
|EECE 7204||Applied Probability and Stochastic Processes||4|
|EECE 7312||Statistical and Adaptive Signal Processing||4|
|EECE 7397||Advanced Machine Learning||4|
|EMGT 5220||Engineering Project Management||4|
|EMGT 6225||Economic Decision Making||4|
|EMGT 6305||Financial Management for Engineers||4|
|HINF 5101||Introduction to Health Informatics and Health Information Systems||3|
|HINF 5102||Data Management in Healthcare||3|
|HINF 5200||Theoretical Foundations in Personal Health Informatics||4|
|HINF 5301||Personal Health Technologies: Field Deployment and System Evaluation||4|
|HINF 6202||Business of Healthcare Informatics||3|
|HINF 6240||Improving the Patient Experience through Informatics||3|
|HINF 6335||Management Issues in Healthcare Information Technology||3|
|HINF 6400||Introduction to Health Data Analytics||3|
|IE 5374||Special Topics in Industrial Engineering (Spreadsheet Modeling for industrial Engineering)||4|
|IE 5400||Healthcare Systems Modeling and Analysis||4|
|IE 5630||Biosensor and Human Behavior Measurement||4|
|IE 6300||Manufacturing Methods and Processes||4|
|IE 7200||Supply Chain Engineering||4|
|IE 7215||Simulation Analysis||4|
|IE 7285||Statistical Quality Control||4|
|IE 7290||Reliability Analysis and Risk Assessment||4|
|INFO 6101||Data Science Engineering with Python||4|
|INFO 6205||Program Structure and Algorithms||4|
|INFO 6215||Business Analysis and Information Engineering||4|
|INFO 7275||Advanced Database Management Systems||4|
|INFO 7290||Data Warehousing and Business Intelligence||4|
|INFO 7330||Information Systems for Healthcare-Services Delivery||4|
|INFO 7390||Advances in Data Sciences and Architecture||4|
|INFO 7610||Special Topics in Natural Language Engineering Methods and Tools||4|
|MATH 5131||Introduction to Mathematical Methods and Modeling||4|
|MATH 7234||Optimization and Complexity||4|
|MATH 7241||Probability 1||4|
|MATH 7340||Statistics for Bioinformatics||4|
|MATH 7341||Probability 2||4|
|MATH 7342||Mathematical Statistics||4|
|MATH 7343||Applied Statistics||4|
|MATH 7344||Regression, ANOVA, and Design||4|
|MATH 7345||Nonparametric Methods in Statistics||4|
|MATH 7346||Time Series||4|
|ME 6201||Mathematical Methods for Mechanical Engineers 2||4|
|ME 7205||Advanced Mathematical Methods for Mechanical Engineers||4|
|OR 6205||Deterministic Operations Research||4|
|OR 7230||Probabilistic Operation Research||4|
|OR 7235||Inventory Theory||4|
|OR 7240||Integer and Nonlinear Optimization||4|
|OR 7245||Network Analysis and Advanced Optimization||4|
|OR 7310||Logistics, Warehousing, and Scheduling||4|
|OR 7440||Operations Research Engineering Leadership Challenge Project 1||4|
|PHYS 5116||Complex Networks and Applications||4|
|PHYS 7331||Network Science Data||4|
|PHYS 7332||Network Science Data 2||4|
|Public Policy and Urban Affairs|
|PPUA 5261||Dynamic Modeling for Environmental Decision Making||4|
|PPUA 5262||Big Data for Cities||4|
|PPUA 5263||Geographic Information Systems for Urban and Regional Policy||4|
|PPUA 5301||Introduction to Computational Statistics||4|
|PPUA 5302||Information Design and Visual Analytics||4|
|PPUA 7237||Advanced Spatial Analysis of Urban Systems||4|
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.