The Analytics track emphasizes fundamentals in the areas of optimization, statistics, computing, data analysis, and communication. Students in the Analytics track learn how to use data to generate practical business insights and make decisions.
The goal of learning from data is to make better decisions, and this objective lies at the heart of our M.S. Analytics Track curriculum. In addition to descriptive and predictive methods, our master's program emphasizes prescriptive methods that help decision-makers select appropriate courses of action.
The core curriculum is grounded in the methodology of Operations Research/Industrial Engineering and includes courses from Computer Science and Statistics, as well as a flexible choice of electives. Students receive training from engaged, energetic faculty in a full range of skills to implement, analyze, and improve data-oriented engineering and business processes.
Students enter the three-semester (16-month) program in the fall semester and typically finish the program in the subsequent fall semester. Through rigorous coursework, students build a strong foundation in data-driven methodologies for model building, decision making, and communication of results.
Each student in the Analytics track puts these skills to use in a real-world setting by completing an exciting semester-long Capstone project. Projects are completed in small teams of two to four students. Each project has an industry sponsor, and is supervised by both an industry advisor and a faculty advisor. Projects allow students to interact closely with their two advisors and the industry sponsor.
The Analytics track requires 30 credits of coursework. The required courses for the Analytics track are IE 5531, IE 5532, IE 5561, IE 5773, IE 5801, STAT 5302, ME 8001, and CSCI 5521 or CSCI 5523. Non-native English speakers must also take ESL 5008. The recommended course sequence is listed below.
1st Year Fall Semester
- IE 5531—Engineering Optimization I
- STAT 5302—Applied Regression Analysis
- IE 5773—Practice-focused Seminar
- ESL 5008—Speaking for Professional Settings (required only for non-native English speakers)
1st Year Spring Semester
- IE 5561—Analytics and Data-Driven Decision Making
- One of the following two courses:
- CSCI 5521—Introduction to Machine Learning
- CSCI 5523—Introduction to Data Mining
- One Elective from the list below
2nd Year Fall Semester
- IE 5532—Stochastic Models
- IE 5801—Capstone Project Course
- One Elective from the list below
Approved Electives for MS Analytics
|IE 5441—Financial Decision Making||STAT 5303—Designing Experiments|
|IE 5522—Quality Engineering and Reliability||STAT 5401—Applied Multivariate Methods|
|IE 5541—Project Management||STAT 5421—Analysis of Categorical Data|
|IE 5545—Decision Analysis||STAT 5511—Time Series Analysis|
|IE 5551—Production Planning and Inventory Control||STAT 5601—Nonparametric Methods|
|IE 5553—Simulation||CSCI 5521—Introduction to Machine Learning|
|PUBH 7461—Exploring and Visualizing Data in R||CSCI 5523—Introduction to Data Mining|
|PUBH 7475—Statistical Learning and Data Mining||CSCI 5751—Big Data Engineering and Architecture|
Note: Subject to departmental approval, other elective courses can be selected.
Hyoseok graduated from the Analytics track in 2018 and began working for LG Electronics in Seoul, South Korea. In his new role, Hyoseok sets up and manages software pricing policies. Moving forward, he intends to employ techniques he learned in his M.S. studies to address important problems for LG.
Adhithya Shanmugam completed his MS degree in the Analytics track in 2019 and is employed by Daikin Applied as a Senior Business Analyst. "The Analytics track at ISyE has greatly helped in refining my data analysis skills and I use them with confidence at work," he says.
Become Your Company's Most Valuable Asset
With corporations and organizations everywhere striving to operate more efficiently, our graduates are sought after to help employers make smarter, better-informed decisions driven by data. In the ISyE Analytics track, students go beyond what is typically taught in business degree programs, and gain hands-on experience working with data while also mastering the details behind sophisticated models and algorithms.
Potential Career Paths
- Data Scientist
- Operations Researcher
- Data Analyst
- Business Intelligence Analyst
- Financial Analyst
Take Advantage of the Twin Cities
The Department of Industrial and Systems Engineering is located on the University of Minnesota—Twin Cities campus in the heart of Minneapolis, on the banks of the scenic Mississippi River. A new, modern light rail system connects the campus with downtown Minneapolis, downtown St. Paul, and the MSP International Airport. With year-round outdoor activities, a metro population of over 3 million, and a diversified economy, the Twin Cities of Minneapolis and St. Paul offer a truly exceptional standard of living. The state of Minnesota is home to 17 Fortune 500 companies.