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I want to give 3.5 stars but there is only option for 3 or 4.

Given that it's geared more towards wanna be managers in this field. I would have expected lot of case studies, and links to additional material for individual reading plus a mini write up or a project at the end.

For MOOCs to compete with a degree program or an onsite instruction few of the things I think that are needed are

Interviews with industry practitioners which is a big plus.

interviews/postings from people with similar background as the 90% of the MOOC students of this program who made it... this motivates the students from dropping from the middle of the course/specilization programs

links to additional articles that the students can read at leisure

writeup/project as assignments that the students can pursue and publish (not necessarily for grade but as a mind jogger)

One more thing that I think should help (not specific to this course) is that the student with the best grade should be offered one free course and think this will motivate the students to complete the courses.

I wanted to like this course but It felt entirely too academic in terms of both the subject matter and the way the specifics of "data science" are presented. The course content reminded me of my time a few years ago when I was enrolled in a Ph.D. program at a major Tier 1 research university. The professors were great, but the content was esoteric and incredibly focused on research based "data science" and less on business analytics or more practical applications of data science for the working professional. I felt myself re-watching the videos to try and understand the content because there were many instances where the professors' spoke about some fundamental aspect of data science but failed to provide some real world context. I liked the course but I would not recommend it as a crash course for the working professional, more like a crash course for someone who already has a degree in statistics/math/engineering who is looking to further their studies in an academic setting where research is the main goal.

I felt the explanation were in between data science for beginners and someone who already had taken a statistics course. I feel that it was important at the beginning to give more real life examples of the usage of data that were compelling. And then take those real life examples and break it down for us using a single inspired project. That would have helped me better understand some of the principles that seemed a bit abstract for me.

I liked mostly Mr. Jeff Leek and Mr. Roger D. Peng's clear and well-composed presentations. But I am disappointed with the certificate format because there is a big note at the bottom which was printed not once but twice and therefore some part of went out of border. After all the hard-works to get such a certificate doesn't feel good at all. Same experience with the "Psychological First Aid" course certificate.

I think the course structure could be improved to give a better balance between the reading materials and spoken content. Though I understand it is a crash course better correlation between real life examples could help augment the course. For the amount of time spent the course does give a quick overview, but if you are looking for more business specific knowledge you will have to take additional courses.

The first week gave me a good insight into the data science process. I now understand the situations in which Data Science can best be applied. However, I still do not fully understand the statistics aspect. It would help me if the instructors would provide examples in detail of supervised vs unsupervised learning. I seem to understand the theory behind it but I am not sure how valuable it will be for me.

I personally found it difficult to understand some of the language used, which I think could be simplified better. I personally learn better, when I'm not being overloaded with loads of facts & info, however I did like that we could do some reading at our own pace, then get shown a video. I like the videos, and the quizzes.

Good starting point, maybe too short. Hope next courses on the specialization become more extense in content. I've always have issues to follow Peng's line of thinking, it's like kinda in some way he is doing some sort of improvisation or so, I love his books, but his lectures are very hard to follow, even the short ones.

This course is very basic and completely theoretical. The grading of questions is not proper. The passing mark is 80% but there are only 4 questions. Answering 3 questions correctly gives you 75% and answering 4 questions gives you 100%, then where is the passing mark question.

But the content is good.

Was expecting more of the how to's and a bit of programming or at least concepts of the programming/statistics, but I guess there are different interpretations of the idea of a crash course.

Definitely geared for people who work with data scientists but not in the data science field.

Brian Caffo's lectures were rambling and confusing, with excessive use of jargon without proper explanation. His graphs were overwhelming with information, and little effort was made to explain the graph. Otherwise, the other lectures were very good, concise, and clear. Thank you.

A good introductory overview of data science. Grounds you on what you can & cannot do with data science. I find defining the question really impactful. Teachers were enthusiastic and the brevity of the course gives it good appeal for beginners who want to "get their feet wet".

VERY high-level intro for complete beginners who want to understand the meaning of some key terms and important concepts relating to data science projects. Almost certainly more training will be required if the participant decides to run or commission a data science project.