_:nc7d332d27fec4707bc00f37f99916640xb3
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"The whole specialization is a bit of a mixed bag... Many of the courses rely too heavily on teaching R programming and not sufficiently on data science concepts (such statistics or machine learning). The instructors (specially Peng) spent way too much time detailing R syntax that could have been picked up by the students on their own from other resources available on the web...\nThe regression models and statistical inference courses are exceptions though: Together with the machine learning course, these are probably the most useful from the whole specialization.\n<text />The materials in this capstone project are way sloppier than materials in other courses by the way. They lack structure and feel confusing. I'm not even sure if the instructors tried to implement the proposed project themselves to have a base of reference.  Feels like they were already growing tired of the whole thing and put the capstone project together in a hurry without much thought or care.\n<text />The theme of the project is indeed interesting (text-mining and NLP), but I think that would have been more productive for me to take a NLP course instead. You are going to use very little from what you have learned from the other courses in the specialization (for the most part the data product course) and you will need to learn text-mining and NLP from scratch on your own to complete the capstone (no videos nor materials available in the course on these subjects).\n<text />Also, if I was going to implement the same app on my own these days, I would probably use RNNs, not Katz Back-off and Markov Transition Matrices as in the capstone and I would probably use SparkR. Heck, I might not even use R, probably Scala or Python with Spark instead. In short, data science moves fast and this course already feels very outdated...\n<text />The instructors seem quite experienced in statistical analysis, so it's a shame that they decided to focus so heavily on R programming instead... That would have made the specialization more resilient to technological innovations in the field...\n<text />The specialization surely could be improved and these issues corrected, but all courses seem pretty much abandoned by the instructors. Most of the courses still have active \"mentors\" (volunteers not associated with Coursera nor Johns Hopkins) , but \"mentors\" seem to have lost contact with the instructors: For example, a couple of assignments require data that is no longer available (dead links) and \"mentors\" have provided this data in the discussion fora. I reckon that if \"mentors\" could contact instructors, the dead links would have been fixed in the materials by now...\n<text />The peer-grading doesn't work so well... Most of the submissions I graded were painful to review (extremely low quality). Not surprisingly, the graders were also pretty low-skilled. They can't even understand the requirements (and I suspect not even the English language) and they will take points from correct submissions.\n \nI urge any employers to look at the actual code for this capstone from candidates given the general incompetence and poor skills of the students I graded. The grading criteria is pretty relaxed, so even though I would like to fail them, I still had to give them a passing grade. Such a weak grading criteria is detrimental to all people who actually have the skills and put hard work on their submissions. Many undeserving people will, unfortunately, pass and receive a certificate.\n<text /><text /><text /><text /><text /><text /><text /><text /><text />"
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