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Ricardo Vladimiro
Hi and thank you for the A2A.
I didn't do any of Udacity's courses so I have no base to compare quality. Assuming you are mostly interested in content, from reading Udacity's course description and from my experience with Coursera's, the differences I see are:
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Hi and thank you for the A2A.
I didn't do any of Udacity's courses so I have no base to compare quality. Assuming you are mostly interested in content, from reading Udacity's course description and from my experience with Coursera's, the differences I see are:
- Udacity assumes prior knowledge in statistical inference. One of Coursera's courses is of statistical inference. I would say that both assume knowledge of descriptive statistics. Udacity states it, Coursera doesn't if I recall correctly.
- Udacity assumes prior programming knowledge, especially Python. One of Coursera's course is R programming, the only language you will need for the specialisation.
- Coursera is, in my opinion, more academic. It distinguishes data science from big data and focuses on the science part of data science. For instance, it worries with reproducible research and the creation of data products that a data scientist can share and discuss. While that is very useful on a corporate setting, it is (again in my humble opinion) a bigger concern in research and academia context.
- Udacity approach is, in my opinion, more corporate. It exposes the student to things like MapReduce, MongoDB while not forgetting my dearest friend R. It has a visualisation part which is very important for professional. To give you some context, the knowledge I got from reproducible research is very useful to discuss internally at my work with other analysts. We can go through the data, the process and the mind set, apply new data sets and discuss it easily. However, for stakeholders a story telling visualisation is much more important than the ability to reproduce the research.
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