BS degree in industrial engineering or related area or equivalent
Mathematical Statistics Course (for example, Stat 312)
Introduction to Programming Course (for example, CS 301)
Non-native English speakers must have a Test of English as a Foreign Language
(TOEFL) score of 580 (written), 243 (computer-based test), or 92 (Internet
version).
The Graduate Record Examination (GRE) is *required for all masters programs
in ISyE. Information on taking the GRE exam can be found here:
https://www.ets.org/gre. Please note: Applicants should plan to take their exam
by Dec. 1st to allow scores to be sent and processed.
*ISyE undergrads and applicants with prior institutional approval are waived
from the GRE requirement.
申请材料清单
立即申请
Fill out an online application through the Graduate School website and pay
the application fee.
List three recommenders and their contact information as part of the online
application. An email will be sent to the recommender, asking that they submit
their letter online using the Graduate School’s recommendation form. Applicants
can log back into their online application to re-send the email request if the
recommender loses the email. Letters of recommendation must be submitted
electronically.
Submit a Statement of Purpose with your online application.
GRE EXAM INFORMATION (STARTING FALL 2018): The course-only option does
require the GRE exam be taken by prospective students as part of the application
but note there are no specific scoring guidelines for the exam as the GRE is
only one part of consideration for admission into the program. Please note:
Applicants should plan to take their exam by Dec. 1st to allow scores to be sent
and processed.
TOEFL EXAM INFORMATION: Ask ETS to submit your TOEFL scores to the UW-Madison
Graduate School (Institution Number 1846). If you have your scores sent to
UW-Madison, they will be available online to all departments to which you have
applied. The institution code, therefore, is the only number needed. For more
information please visit the Graduate School Requirements page. (Please note:
Exam information must be valid at start date of the semester that you are
applying for (non-expired)).
Electronically submit one copy of your official transcript with your
application. Unofficial copies of transcripts will be accepted for review but
official copies are required for admitted students.
Analytics, and the ability to effectively utilize data, is quickly becoming
an important component in engineering decision making. There is a strong need in
the marketplace for people who use analytical tools to transform data into
insights for making better decisions. The Systems Engineering and Analytics
option within the UW-Madison graduate program in Industrial and Systems
Engineering offers students the opportunity to pursue graduate training in this
important and emerging area, under the auspices of the foremost experts in their
field, in one of the world’s top-ranked departments of industrial and systems
engineering. (We were ranked 8th in the latest US News and World Report
Rankings). The flexible curricula in Systems Engineering and Analytics enable
students to tailor their degree program to suit their particular needs and
career objectives.
After completing your degree, you will be able to analyze, process, and build
conclusions based on the data you collect in the design, testing, and operations
phases of engineering and design processes.
The program includes training in optimization models and methods, applied
industrial analytics, simulation modeling and analysis, and courses wherein
these analytical and computational tools are applied in an engineering systems
setting. These learned skills are now highly sought after in manufacturing,
transportation, finance, healthcare, and other industrial sectors.
What You Learn
Acquire mathematical, scientific, and engineering principles in
analytics.
Utilize data-driven methodologies to formulate, analyze, and solve advanced
engineering problems.
Evaluate relevant analytical, computational, engineering tools to address
advanced systems engineering problems.
Solve real-world problems using computer-assisted, data-driven decision
making technologies.