学生对 约翰霍普金斯大学 提供的 数据科学速成课 的评价和反馈

G

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".

Good, simple, straightforward, and applied. It starts to introduce you to the language and platforms of data science, but it is most definitely not a standalone course if you want to be conversant in the field. Really just a "taster" to get you into the specialization.

More lectures but each lecture should be shorter. More examples for each topic would be good.

However it is a crash course, so it is more of an overall description of Data Science, which also gives you suggestions to enhance your knowledge in other courses

This course is pretty basic so don't expect much. Basically this course is more reading than understanding but the articles suggested are good. If you are serious regarding data science then this course may be of not much use to you.

Not all lectures in the course are well done. The one on the data scientist toolbox is good and could have more details. The one separating data science from statistics is too confusing. May need to redo the video on that one.

This course leaves you frustrated, as valuable information is only ad the end and at the beginning. It doesn't really answer questions of what are data science, big data, machine learning, how they interact and how to use them.

Okay content, very mix of level of information. Could state better the terms used in the DS world. The quiz part is not well formed questions, more there to mislead than actuelly verify the knowledge

The course material was good and the presentation was clear, however the Quizzes were very frustrating to figure out when I got them wrong. I could not clearly tell why the answers were incorrect.

I am trying to work out whether or not to get into data science, I thought this would help but still undecided.

I liked the grounding of principles, tools and methods required for the discipline.

The course can use more visuals instead of videos of the face of the instructor. It can also use more interactive examples as this is a more executive view instead of having scholar examples.

Was actually expecting more.

Some of the lectures seemed just to theoretical and distant.

Several of the linked resources do not exist anymore or are accessible only with paid subscriptions.

Great course for somebody who does not know anything about data science. When doing this specialisation there should be credit for those that did the other data science specialisation