Imperial MSc Machine Learning and Data Science (Online via Coursera)

Thank you @borbs_15, that's very kind. Likewise! (assuming that you also applied)

I already got into UIUC's Online Master of Computer Science in Data Science. I am still debating which one to choose if I am accepted to ICL. At least I have a good alternative, so I won't be too disappointed in case of a rejection 😅

UPDATE: I received an offer on Monday (17 April) from ICL!! I've been thinking this week and am leaning towards ICL over UIUC. ICL's programme is more costly and takes longer (more modules + a research project) than UIUC's, but I feel like the curriculum at ICL is more extensive and will better prepare me for a job transition into ML engineering.

Has anyone else received an offer and is debating between ICL and another uni? I'd love to hear your thoughts.

Hi, I got an offer also last Monday.
Just need to finalise some missing document linked to my MSc being from abroad.
I am planning to accept the offer and looking forward to start the programme.
It was my first and only choice and didn’t apply to another programme as was only interested with this.
Same for me, i am working on a job transition.

I'm currently in the final term working on the summer project.
This post is just a brief summary of some pros and cons about the degree, might add to it later.

++ Deep Learning, Big Data, Bayes were exceptionally good.

+ UDA, SL, EDAV, AM and LA were not bad.

+ Over two years you can build a portfolio of projects.

+ I've gone from being vaguely competent with simple stats models like linear regression, to now being firmly comfortable having a discussion about more or less all of the common ML algorithms and architectures, their mathematical assumptions, challenges and opportunities. E.g., random forests, deep learning, convolutional neural networks, RNNs+LSTMs, transformers, normalising flows, bayesian stats+models, distributed computing for big data, NLP, reinforcement learning. I'm not an expert in any of these areas but I would be able to follow a conversation and have a fair idea of what is being discussed. In most situations I'm able to evaluate when and where ML algorithms are best suited.

+ I feel reasonably prepared to pursue a PhD in my areas of interest.

- Ethics and Programming modules were pretty terrible. Hardly any educational value.

- Dedicating 50% of the masters to R was a waste for me personally. Looking at current job postings, Python is far more in demand. This isn't to say R is completely useless. There are industries where R is popular and use cases where it performs well. However I wish the course had been purely focused on Python, with a splash of SQL and maybe Scala/Java or C++.

- Work load fluctuates a lot and is seriously demanding in the peaks. 21 hours per week is definitely an understatement. Some weeks you'll have multiple overlapping deadlines and no flexibility whatsoever. Some weeks went up to 30 or 40 hours of work. This was just to keep up with the lectures, notes, labs and assessments. I rarely had time to do all of the recommended readings.

- Instructor feedback on assignments is high variance. Sometimes excellent, sometimes minimal. There were times when we would spend 10 or 20 hours on an assessment project and get back 20 words of feedback. Uninformative comments like "Good report, 8/10." However some lecturers did really care about providing good feedback.

- There's very little emphasis on the technical engineering side of machine learning and does not cover SQL or databases in any capacity.

- £30k is pretty steep for what is offered. I can't say whether it would be worth the money since it would depend on your personal financial situation. (However this ended up being a worthwhile investment for me as it enabled me to secure a promotion to an ML focused role at work, YMMV).

All in all, this course would not by any means prepare a layperson for a role in software engineering, data engineering or ML engineering. However this would be a suitable course for laypersons entering data science roles or software engineers entering ML eng roles.

I'm currently in the final term working on the summer project.
This post is just a brief summary of some pros and cons about the degree, might add to it later.

++ Deep Learning, Big Data, Bayes were exceptionally good.

+ UDA, SL, EDAV, AM and LA were not bad.

+ Over two years you can build a portfolio of projects.

+ I've gone from being vaguely competent with simple stats models like linear regression, to now being firmly comfortable having a discussion about more or less all of the common ML algorithms and architectures, their mathematical assumptions, challenges and opportunities. E.g., random forests, deep learning, convolutional neural networks, RNNs+LSTMs, transformers, normalising flows, bayesian stats+models, distributed computing for big data, NLP, reinforcement learning. I'm not an expert in any of these areas but I would be able to follow a conversation and have a fair idea of what is being discussed. In most situations I'm able to evaluate when and where ML algorithms are best suited.

+ I feel reasonably prepared to pursue a PhD in my areas of interest.

- Ethics and Programming modules were pretty terrible. Hardly any educational value.

- Dedicating 50% of the masters to R was a waste for me personally. Looking at current job postings, Python is far more in demand. This isn't to say R is completely useless. There are industries where R is popular and use cases where it performs well. However I wish the course had been purely focused on Python, with a splash of SQL and maybe Scala/Java or C++.

- Work load fluctuates a lot and is seriously demanding in the peaks. 21 hours per week is definitely an understatement. Some weeks you'll have multiple overlapping deadlines and no flexibility whatsoever. Some weeks went up to 30 or 40 hours of work. This was just to keep up with the lectures, notes, labs and assessments. I rarely had time to do all of the recommended readings.

- Instructor feedback on assignments is high variance. Sometimes excellent, sometimes minimal. There were times when we would spend 10 or 20 hours on an assessment project and get back 20 words of feedback. Uninformative comments like "Good report, 8/10." However some lecturers did really care about providing good feedback.

- There's very little emphasis on the technical engineering side of machine learning and does not cover SQL or databases in any capacity.

- £30k is pretty steep for what is offered. I can't say whether it would be worth the money since it would depend on your personal financial situation. (However this ended up being a worthwhile investment for me as it enabled me to secure a promotion to an ML focused role at work, YMMV).

All in all, this course would not by any means prepare a layperson for a role in software engineering, data engineering or ML engineering. However this would be a suitable course for laypersons entering data science roles or software engineers entering ML eng roles.

hey, I've just got an offer for this course and have to choose between this and another course and was wondering if you could answer a few questions?
1. Do you get any tutor in this course and how have the tutorials been?
2. For your final project, again, do you have a supervisor of sorts to work on feedback etc and discuss project ideas?
3. And how do you think this course would fit someone with a computer science degree? (i.e - do you think it's aimed at someone getting into data science rather than improving on current knowledge?)

I apologize if I'm bombarding you with questions here. I haven't seen many people comment on this course, and I'm having trouble making a choice. :/

Does anyone know if the certificate will say that it was delivered online? A lot of alternative masters programmes like this make it a point to mention that there will be no distinction between the online and on-campus programmes.

I am aware that this programme is online-only, but would be great if they did not mention that on the certificate.

1. You do get a personal tutor who will arrange meetings for your tutor group once per semester. This is more of a pastoral session to check you're still alive. Each module lecturer will host two live sessions per week for their module, one live lecture and the other is an office hour, so there's ample opportunity for contact time.

2. For the final research project, students ranked project topics by preference and then were assigned groups. E.g. time series, bayesian stats, deep learning, etc. Once you are in the group you all meet once per week with a lecturer to discuss ideas. There's a lot of opportunity to play around with some ideas and see what others are planning, but ultimately it's quite open for you to do whatever you want within the relevant scope of the group theme. Unfortunately there were no projects explicitly in reinforcement learning, computer vision, natural language processing or generative modelling. Although depending on how nice your project leader is, you can 'massage' the scope of your topic to cover other areas.

3. This would be a good course for compsci grads, although bare in mind at times it is very maths heavy. I would encourage you to brush up on key areas and general notation before the start of the course. The Applicable Maths module in the first semester is a good recap, but obviously topics like linear algebra, probability, stats and calculus aren't things you can confidently learn from scratch in a week. Would definitely recommend putting in some extra prep beforehand. This is important because later modules like Bayesian stats will go completely over your head otherwise. Conversely, the programming module was exceedingly simple. If you already have a CS degree I doubt you would learn anything new from that module whatsoever, aside from some R syntax. (However I am aware the programming module was newly refactored this year, so would be keen to hear from new cohort what their experience was). The big data module did cover some theory on distributed computing but presumably not anything new to a CS grad.

I did learn a lot from this masters and I wouldn't be able to say whether it's better than your other offer without knowing exactly where else you are considering.

I think this is a suitable course for someone looking to flesh out the gaps in their theoretical knowledge, become more confident with the data science workflow and gain a deeper understanding of the maths and stats behind ML algos.

I wouldn't pursue this course if you are already confident with ML+maths+stats and looking to improve your technical skills.

I'm pretty sure the certificate won't mention that the course was delivered online.

Usually unis that deliver online degrees are quick to advertise this fact but Imperial does not say anywhere what the certificate will state. Wondering if anyone her knows what the exact title awarded is? "MSc Machine Learning and Data Science" ?

1. You do get a personal tutor who will arrange meetings for your tutor group once per semester. This is more of a pastoral session to check you're still alive. Each module lecturer will host two live sessions per week for their module, one live lecture and the other is an office hour, so there's ample opportunity for contact time.

2. For the final research project, students ranked project topics by preference and then were assigned groups. E.g. time series, bayesian stats, deep learning, etc. Once you are in the group you all meet once per week with a lecturer to discuss ideas. There's a lot of opportunity to play around with some ideas and see what others are planning, but ultimately it's quite open for you to do whatever you want within the relevant scope of the group theme. Unfortunately there were no projects explicitly in reinforcement learning, computer vision, natural language processing or generative modelling. Although depending on how nice your project leader is, you can 'massage' the scope of your topic to cover other areas.

3. This would be a good course for compsci grads, although bare in mind at times it is very maths heavy. I would encourage you to brush up on key areas and general notation before the start of the course. The Applicable Maths module in the first semester is a good recap, but obviously topics like linear algebra, probability, stats and calculus aren't things you can confidently learn from scratch in a week. Would definitely recommend putting in some extra prep beforehand. This is important because later modules like Bayesian stats will go completely over your head otherwise. Conversely, the programming module was exceedingly simple. If you already have a CS degree I doubt you would learn anything new from that module whatsoever, aside from some R syntax. (However I am aware the programming module was newly refactored this year, so would be keen to hear from new cohort what their experience was). The big data module did cover some theory on distributed computing but presumably not anything new to a CS grad.

I did learn a lot from this masters and I wouldn't be able to say whether it's better than your other offer without knowing exactly where else you are considering.

I think this is a suitable course for someone looking to flesh out the gaps in their theoretical knowledge, become more confident with the data science workflow and gain a deeper understanding of the maths and stats behind ML algos.

I wouldn't pursue this course if you are already confident with ML+maths+stats and looking to improve your technical skills.

I want to ask you about the support you got after graduation. do they recommend you to employers like they do to their on-campus students? do you get career support? can you attend the graduation ceremony if you can go to the UK?