Hello everyone! I'm a mechatronics / mechanical engineer preparing to start a MSCS degree, likely with CU Boulder.

This post would serve two purposes:
(1) Experience-sharing for other people with similar role switches.
(2) I would be glad if someone points out potential blind spots in the plans I made so far, towards earning the degree.

-----

🔘 Background

• I work 10+ years as a mechanical design engineer in Europe. Still my main job.
• Bachelor in mechatronics + Master degree in another engineering discipline.
• No CS degree, not working in CS field.
• I code to make automation tools for our engineering department (AI-assisted). Recently also started freelancing with automation / digitalization work, which is the wildcard in my time planning.
• Pursuing the degree parallel to full-time job.

Why the Degree

I want to move toward senior roles that combine mechanical engineering with software. I likely won't fully switch to the software engineering roles, as I don't see myself so competitive when it's purely CS / software / ML. I'm most competitive if I augment my mechanical engineering experiences with an additional CS / AI capability. Forward deployment engineers or product engineer lead could be interesting fit for me.

Also, I want to have good technical foundation to judge and propose real opportunities in my sector, apart from hype. To be technically competent of later leading companies / technical teams of whichever side.

I'm also into the degree / title for its "convenience." It gets me into HR and recruiter keyword searches, and it reduces the effort of explaining why me as a mechanical engineer can code. I know I need to win people's trust by true competence - I first want to increase chances for a ticket to even set foot in the right meeting rooms.

Preparation & Skill Level

This part is mainly for people coming from mechatronics or mechanical engineering who wonder whether their background is enough.

• Programming: I learned C++ in my bachelor. Python was not hard to pick up (except that coming from C++, I have a passionate hatred towards how messy Python feels). I write real automation tools using Python/JS at work, so I am not starting from zero.
• Linear algebra: High school level. So far matrix multiplication has been entirely sufficient for the basic ML courses I have touched. I genuinely don't know yet whether it gets significantly harder later. I'd like to hear from people ahead of me.
• Calculus: College 1st-year level. Similar to linear algebra above, so far just understanding very basic slope / derivation of simple polynomials have been sufficient for basic ML courses.
• Statistics: Currently filling this in, see below. High-school understanding about means/ standard deviations / deviances / Gaussian distribution have been enough to accompany basic ML courses this far, but I wouldn't assume it stays this easy.

🔘 Plan

Degrees in Consideration

• CU Boulder MS-CS (+ AI certificate)
• UT Austin MS-CS
• UT Austin MS-AI
• Georgia Tech OMSCS - Not considering due to rigorous reference letter needs

Shortlist Criteria

• None of them require serious reference letters. This is the big one for me. I don't work in software, so I have no manager who can vouch for software work. My academic contacts are effectively gone as professors I know are retired. My direct managers could put in good words about how I work in general, but those letters would say nothing about CS, and collecting them is real effort. Thus the performance-based admission from CU Boulder is very attractive.
• UT Austin is cheaper in tuition, with even better ranking + some Reddit commenter mentioning that the AI information is even more cutting edge, which caught my attention. But if I apply, I can only start in a year <-vs-> CU Boulder = Start now.
• I work in Europe and deliberately did not want a European degree. I'd like to broaden my perspective beyond a Europe-centric one.
• (Sorry for the ignorance in advance - Where I work, US universities all sound fairly similar in brand except for the super-famous ones like Stanford or MIT. Thus my normal audience likely can't tell the difference between UT Austin or CU Boulder. With that I'm not sure if stretching the study time by going to UT Austin will be worth the investment.)

Current Plan

• Do CU Boulder courses on Coursera Plus first, to make sure I have enough time to learn. Take the for-credit version later in bigger bulk, maybe close to capped allowable credits per term.
• Target finishing within 1.5–3 years. (See question 1 on whether that's realistic.)
• Potentially staying fully with CU Boulder, but also considering to use CU Boulder course certificates as parallel application material for UT Austin. Still undecided because with CU Boulder I could start literally now, instead of starting in a year. This potentially drags the study time longer, leaving me less time for other things in life.
• I am in a luxurious situation where I have lots of time - No partner / kids, with a 9–5 job that doesn't bother me outside of working hour. Might as well use this life phase to finish the study quickly, before something else in life becomes more interesting for my attention.
• To test my discipline, I tested for a month the schedule of Monday/Wednesday/Friday 19:30–21:00 as minimum study time, plus at least 4 hours on each weekend day. The routine held and I liked it, but it turned out too rigorous for my energy level after a workday, so adjustments are still ongoing.

🔘Courses & Helpful Tips

Courses done so far as preparation are below.
All non-credit, collected starting from 2026 spring.

✅ edX / Georgia Tech — Introduction to Python Programming certificate: 3 of 4 courses
✅ DeepLearning.AI — Supervised Machine Learning
✅ DeepLearning.AI — Advanced Learning Algorithms
✅ DeepLearning.AI — Unsupervised Learning
🟢University of London — Probability and Statistics (Done in a few days)
🟤CU Boulder — Introduction to Machine Learning: 30%, mainly to check difficulty level & course style
🟤CU Boulder — AI Ethics and Society's Future: 15%, same reason as above

Tips and tricks I tested to be helpful for me:

• Swap doomscrolling for Coursera and I suddenly have a lot of time. Waiting in queue, waiting for bus, waiting for friends, before bed, etc. It helped a lot that whenever I wanted to open Instagram, I open Coursera instead.

• Use commute for high-quality rest, not study. My commute is 2 hours round-trip. Trying to study on it drained me and didn't work anyway, since there's no desk and no way to take proper notes. Treating it as pure rest worked far better: Real eyes-closed nap, or play video games on Switch full-heartedly. I recommend offline, high-quality games over phone apps which are built to hog your attention and pull you back at set times. With this, I come home rested, making my evening time at a desk high-quality study time. Much better than trying to cram on a train.

• Download content offline before you travel. I had long-haul flights delayed by hours, once by a full day, and having lectures to watch made me the calmest traveler in the world. It's not only good for your progress, but also good for your mental stability. (Be warned though that Coursera's offline sync is buggy)

• Deeplearning.ai is very, very recommendable as preparation. Its courses have been super helpful in building intuitions, using explanations that even high-school students without calculus knowledge can understand. It made me feel all other courses are explaining simple things in an unnecessarily complex way, bombarding you with formulas before even an intuition can be solidly formed. If you don't care about certificates, I think you can access all their courses for free on their website.

------

🔘 Uncertain Points (Feedback welcomed)

1. Pacing: Plan vs. reality?
Six terms a year at 1–2 credits puts 30 credits at 2–3 years. Is that how it goes with a full-time job? I'd like to hear what you estimated at the start versus what actually happened, and what caused the gap. I'm aiming to finish it in 1.5-3 years, preferably under 2 years, but possibly too rigorous.

2. Audit first, credit later - Potential issue with this approach?
Has anyone done the Coursera-Plus first, for-credit-in-bulk-later route? Did the study-in-advance save you real time on the credit pass?

3. Term availability?
Does a course being live on Coursera mean it runs every term? What's the reliable way to check, and how far ahead can you see it?

4. Advices for community-feeling during the degree?
I would welcome the potential chances of making friends through the degree. The positive side of a self-paced degree is that you are very flexible, but the downside is that you would have to make effort to engage with other classmates. Any tips on not just shutting off in your own cave and studying more-or-less alone?
I'm playing with the idea of doing a self-initiated capstone project with a few others and crowdfund an experienced ML practitioner to review the project. If anyone's interested later down the road, gladly reach out.

------

I hope this helps also other people with similar profiles considering the degree. Happy to report back as I go.

(Disclaimer: AI used to hone wording and post structure)