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I just started in UC Berkeley's MIDS program.

My only two misgivings about the program thus far: It is 1) pretty expensive and 2) geared towards working professionals rather than academics, but my employer is helping pay for a good chunk of the degree and I'm more interested in acquiring the skills and tools to go solve problems in industry as opposed to doing research.

Otherwise it has been great thus far. The program was attractive to me because it is somewhat marketed towards those that may not have a software background, but have problems in their industry that could benefit from a "proper" data science treatment. I've been referring to my application of the principles as "Six Sigma for the 21st Century" with managers/directors. I think the vast majority of HN would groan at that term, but it helps communicate the potential value to someone who has no technical background with software whatsoever (think old school manufacturing/operations types): Process improvement for environments with many variables that have practically unknowable inter-dependencies (as is the case with the project described in my original comment).

Interesting perspective. I work in Manufacturing and have created similar models in the past and I was in the MIDS program but I dropped out. Like you it was too expensive and had other misgivings as well.

Care to elaborate at all on those additional misgivings? One thing I could see is that the material might not be very mind-blowing to someone who already has a software background.

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