December 10, 2019 at 9:30 pm #6978Kristin SerwinParticipant
315: this class was the basis for most other classes for me. It also gave me a great background for my internships.
323: Though this was a super hard class, I absolutely loved the problem solving and optimization aspects.
515: This is a semester project focused on process improvement. This was great real world experience and taught me so much about actually working with companies.December 10, 2019 at 10:40 pm #6990Deepak Kumar JeyakrishnanParticipant
The course selection in college should be based on your career interests. I can share my experience with 3 subjects which I completed recently.
ISyE 412 – Introduction to data analytics – an introductory course that covers key topics related to data analytics, you also get to work using R and Tableau.
ISyE 510 – Facilities Planning – Covers all the factors like cell and assembly workstation design, material handling principles, facility optimization, space requirement considerations required while planning a facility.
ISyE 515 – Engg Management of Continuous Improvement – Through this course, You work on a Kaizen project in industry. This course gives you the practical experience/challenges faced while working in a real-world environment. It also introduces you to several Management tools like Affinity Diagram, Tree diagram.December 10, 2019 at 10:51 pm #6992Jackson LaniganParticipant
There are a few IE classes that stood out to me in terms of my academic development and going forward, my professional development. I think ISyE 323 is the best class in the department. The curriculum is challenging and rewarding, the course is well planned and taught, and the material seems applicable to learning skills that we can take with us moving forward. I also think ISyE 450 obviously gets you ready to acclimate to a work environment, challenging you to problem solve and work with others.January 25, 2020 at 9:24 pm #7099Jin-ri LeeParticipant
If you are interested in Machine Learning, I would recommend taking 601 course, Machine Learning in Action with the new professor, Justin Boutillier. His course work is worth learning and easy to follow. Also, a great way of teaching because it isn’t just memorization and uses case studies to teach different techniques.February 8, 2020 at 1:35 pm #7123Maxwell RoitsteinParticipant
I think the most valuable ISYE class I have taken at UW through my four years has been ISYE 323. I think it was a good balance of combining the theory of linear optimization as well as the real-world application of what we were learning. I think most other classes are lacking in one of the two categories, most often in the real-world application. While I may not be able to even set up an LP anymore, it still was invaluable in teaching me how to think about problems and improving my critical thinking skills.February 9, 2020 at 3:52 pm #7137Spencer NellisParticipant
I am currently taking ISYE 323 and I have really enjoyed it. It is very easy to see how linear programming applies to real world situations. I think it is a skill every Industrial Systems graduate should know well.February 9, 2020 at 9:37 pm #7141Ruixin ZhangParticipant
I would say ISYE 315 and 323 are by far the more interesting and useful lectures I’ve taken so far. 350 and 320 were also good in some aspects, but the practical knowledge from 315 and 323 could be more important for the future and probably chosing a graduate field to work with.February 9, 2020 at 10:12 pm #7144Sydney SeimParticipant
The most useful ISYE class that I have taken has by far been ISYE 315. I would consider this class to be best baseline for IE topics and information. Like someone said above, it was super helpful for my internship as well.February 21, 2020 at 7:14 pm #7175AndyParticipant
For me the most useful IE classes are 313, 320, and 412 in that order.
I probably will not go into a manufacturing role so for me learning about financial mathematics is easily the most important skill, followed by 320 and 412 because they provide students well rounded skills of simulations and basic data analytics.
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