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University of Pennsylvania
Introduction to Operations Management
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Introduction to Operations Management Professor Christian Terwiesch brilliantly and understandably explains the math behind "operations".... which explains Lean, Agile, DevOps and everything from running a restaurant to a doctor's office.
I really enjoied Introduction to Operations Management (https://www.coursera.org/learn/wharton-operations) which is not about CS/IT but more about organizing process workflow (i.e. how to make shops, plants or any kind of multi-operator job more efficient).
I also liked a Coursera one titled "Data Analysis" but the url now returns a 404 (https://www.coursera.org/course/dataanalysis) and it probably morphed in something slightly different.
⬐ anoncowI took the course last year and liked it.
My background: Double major in Mathematics and Statistics. I am currently studying for a masters in Industrial Engineering BECAUSE my math/stats degree could not secure me a job after looking for 1.5 years. People don't see the value in math and you can't make them. That's why I am going to trick them by getting a degree with "engineer" in the title and maybe I can finally make it past the HR robot resume scanners.
1.The course content looks ok. There is always some sort of crap class that the department "thinks" you should take. For you it is Mat 380 Error-correction Codes. Out of the classes you get to pick, you should absolutely consider "QSO 320-Intro to Management Science". Mathematics departments (I have been through about 4 different ones) do a terrible job of application. None of your classes except QSO 320 will have day to day usefulness.
2.If you have not paid for this degree yet I would encourage you to look up programs for the following departments. Management Science, Operations Research, Operations Management, Business Intelligence, Data Science. They usually have just enough theory to carry you but focus on problem solving methods and decision making.
3.If your future goal is to go on to a masters then to some sort of research based work, then a math degree is a great jumping point. When ever I have come into a practical application course, I crush the theory and have an advantage in the ease of absorbing the knowledge. If you are not really sure about research/MS/PHd I strongly advise against this plan. You will be disappointed in how little people understand the how to leverage math skills.
In general, school is where you pay money for the privilege of doing homework and writing tests subject to the human faults of a non-perfect professor. I would advise you to look at the course syllabi for the following Coursera classes. So much good content coming from there. At least you will be more knowledgeable of the breadth in these subjects. - https://www.coursera.org/category/cs-ai - https://www.coursera.org/category/stats - https://www.coursera.org/category/cs-theory - https://www.coursera.org/course/operations
The funny thing about that list is none of them are from the mathematics category. Math is pervasive but struggles to compete without context. Take courses with context, unless you really want to pursue an academic career.
⬐ darkxanthosI'm actually very excited about the intro to management science class so I'm glad to hear you resonated with it too. Yeah I plan to go onto my Masters. Part of this degree I see as improving my mathematical vocabulary if you will.⬐ medusa666It is not the responsibility of your degree to "secure you a job"! Please read _Ask The Headhunter_.⬐ walrus> 1. [...] There is always some sort of crap class that the department "thinks" you should take. For you it is Mat 380 Error-correction Codes.
Error correcting codes are fun! Taking a class on coding theory gave me some insight on compression, cryptography, and reliable data storage/transmission.