Help me decide which Coursera course to pursue next!

[rachel83az]

I really wanted to do one of the IBM data certification certificates but they are not included in Plus/Learning Programs. I may pay for one later but I'm undecided right now. I've narrowed things down to:

https://www.coursera.org/specializations/deep-learning - AI/machine learning is interesting but do I really need to know this right now? It does say that a knowledge of linear algebra will help. I haven't done linear algebra yet. Supposed to take 4 months (80 hours) to complete.

https://www.coursera.org/professional-ce...-analytics - Pros: SAS is definitely a thing in data analytics. This is reported to be a beginner-level course that should take 2 months (40 hours) to complete. Cons: I have no idea if I'll even need to know SAS in the future.

https://www.coursera.org/specializations...nce-python - Data science with Python, looks good. 5 courses in this specialization. Pros: Python. Cons: It says it's intermediate level and will take about 5 months (140 hours) to complete. 

https://www.coursera.org/specializations...ndations-r - Data science with R. Pros: John Hopkins University! Beginner level. Leads into https://www.coursera.org/specializations...e-learning Cons: Says it takes about 5 months (160 hours) to complete. 

I'm leaning towards the John Hopkins specialization(s) but I'm not sure. Maybe both John Hopkins & the Michigan one. Or John Hopkins and SAS because SAS should be done quickly? I'm very torn right now.

[bjcheung77]

All I have to say is, take the one you want, I would lean towards the SAS and then do the John Hopkins one... but then, that's just me...
On a side note, this post reminds me of a oldie song from Natalie Imbruglia - Torn! https://youtu.be/VV1XWJN3nJo

[rachel83az]

All I have to say is, take the one you want,

That's the problem! I want to do them all but I only have a limited amount of time available in each day.

[Merlin]

I really wanted to do one of the IBM data certification certificates but they are not included in Plus/Learning Programs. I may pay for one later but I'm undecided right now. I've narrowed things down to:

https://www.coursera.org/specializations/deep-learning - AI/machine learning is interesting but do I really need to know this right now? It does say that a knowledge of linear algebra will help. I haven't done linear algebra yet. Supposed to take 4 months (80 hours) to complete.

If you haven't already, I recommend taking Andrew Ng's introductory course first "AI for Everyone" before starting on this. This will help prepare you for his "Machine Learning" course and the "Deep Learning" specialization.

It isn't a bad idea to complete the "Mathematics for Machine Learning" specialization from Imperial College London prior to starting either of those. It covers all the foundational maths required for the basic-intermediate ML courses you may want to consider.

Along those same lines, I have also heard good things about the "Advanced Machine Learning" course from HSE. It goes way deeper into topics like deep learning, natural language processing, and vision processing.

https://www.coursera.org/specializations...nce-python - Data science with Python, looks good. 5 courses in this specialization. Pros: Python. Cons: It says it's intermediate level and will take about 5 months (140 hours) to complete. 

I have this specialization in my list of courses to complete, but I haven't gotten around to starting it yet. I believe that it assumes you have fairly strong Python skills coming in. The "Python for Everyone" specialization is a good place to start if needs be. I took it as a refresher course since I don't do much in the way of Python coding these days.

[rachel83az]

I did notice later that one is supposed to take Andre Ng's course before the Deep Learning specialization. I guess that takes that particular specialization out of the running, at least for now. I hadn't seen the HSE specialization but that one is "advanced" and will definitely need to be later. 

I'm actually about halfway through Py4E.com since that's supposed to go more in-depth than the Coursera and edX course(s). Also, just finished the Python segment of Programming in the ASU course (now we're doing Java). So I should hopefully be good on the Python front...

Maybe I'll do SAS and John Hopkins and then add the Michigan one after I finish SAS. I want to squeeze as much out of the free Coursera stuff as I possibly can!

[Merlin]

I did notice later that one is supposed to take Andre Ng's course before the Deep Learning specialization. I guess that takes that particular specialization out of the running, at least for now. I hadn't seen the HSE specialization but that one is "advanced" and will definitely need to be later. 

As a FYI, Andrew Ng is the instructor for all of those courses, including the Deep Learning specialization. I think the intended order is:

1. AI for Everyone (course)
   
2. Machine Learning (course)
   
3. Deep Learning (specialization)
   

I'm actually about halfway through Py4E.com since that's supposed to go more in-depth than the Coursera and edX course(s). Also, just finished the Python segment of Programming in the ASU course (now we're doing Java). So I should hopefully be good on the Python front...

That's good. You'll want to be up to speed on Python for most of the ML and Data Science courses since many of the worked examples will focus on Python using SciKit-Learn, Pandas, and MatPlotLib. Some of them also use TensorFlow.

Also, if you like the "Applied Data Science with Python" from U of Michigan, you might also consider their "Machine Learning" specialization (4 courses) as I believe it is taught by the same instructors. It isn't a replacement for the Andrew Ng course though.