学生对 IBM 提供的 使用 Python 进行数据分析 的评价和反馈

A lot of un addresses subjects. Many mistakes both in the videos and in the labs.

Overall after viewing all the videos again and summarizing for my self everything, I felt a lot better with the material but I think the course is not organized. I also think that it should get into some mathematical subjects more thoroughly.

A lot of errors make the course confusing. Also, the assigments and labs are "too easy"... it is clearly shown in the videos that there is much more to be done, but the course only demands you do about 50% of what is taught. How are we supposed to really learn without practice?? Give us real and demanding projects!

The content was good, but there were numerous mistakes and inconsistencies (i.e. a chart would show a red line as a training set but the write-up would say the red line was a testing set). Also, I would have preferred to have shorter and more lab activities. The lab activities were too few and each was too long.

Grammatical mistakes, low quality videos, low quality slides and videos. Labs are okay, though no in-depth clarifications and explanations are given. Like "to do this you write this". Options? Explanations? What for? It's too much. Just remember how we wrote these lines and copy-paste them in you code later.

Course is a bit too short and way too fast paced for what it is trying to convey! Of course people will be able to complete the course without problems but, have to re-visit and brush knowledge on these a lot more. Anyways, it is a bit of confidence booster. You feel like you learnt a new course.

The reason I am giving a three to this course because compared to rest it was a bit fast-paced. Also, I feel we need a prerequisite of statistics before starting this course which was not mentioned anywhere.

Guess it is time for a lot of practice. Wish there were more assignments as well.

Highly technical and complex in nature. Difficult for people just starting out with data science. The hands-on labs are more useful than the videos themselves. The quizzes in between videos felt a bit too easy and mostly comprised of examples (as questions) in the videos themselves.

This course is definitely very helpful to people who are passionate about Data science and have basic to intermediate understanding of Python but this course can be much better if it includes coding assignments rather than quiz submission. It was a great experience.

I'm not convinced that this is a great way to learn, I just feel there needs to be a better way of learning this than the approach this course takes, I kind of learned the python commands but I'm not sure I understand how to apply them in the real world. We'll see

Decent videos on Data Analysis techniques.

But the labs are poorly constructed: typos, inconstant question and solution, un-commented code and under-explained lab result.

It's a shame since the labs in other courses in this series are very high-quality.

It would be great course for beginner to have idea about different steps involve in data science job. I would recommend to go with this course. I just took 3 days to complete this course and you can do in 2 days also. Depending on your speed.

There are some mistakes in the course (wrong transcryptions, missing cells in LAB).

The material is quite difficult and more explanation / exercises would be needed.

There is no assignment at the end of the course which I consider as minus.

Good introductory course on Data Analysus with Python. Since the course is short, the functions and concepts are explained very quickly. There are also many mistakes in the slides, notebooks and even in the final assignment.

The content of the course is very interesting. There are lots of typos in the lab workbooks though. Additionally, i found having to use Watson Studio for the assignment / labs as opposed to plain Jupyter a little annoying.

It would be great if we go in a little more details of when to use which metrics for evaluation. Instead of running through a bunch of concepts you could have spent a little more time in each of them.

There were serious problems with this course, not in the instructional material but in the execution. There were multiple typos in the code. The especially grievous ones being in the dictionary names.

Ran into some roadblocks during the peer assignment. It would have been nice to have had access to someone to discuss the roadblocks and assist me with understanding how I went wrong.

This is the 6th course of the specialization and I gave 5 stars to the previous courses. But this course have many typos in videos and codes. It makes harder to understand some points.

The work in this course was incredibly interesting. However, there are many errors and the forums went for over a week without response to questions...It seems hastily put together.

Videos are very fast and dont go into details. Assignment is very easy, it could have been more challenging which can test and make learner to think using several concepts learned.

Typos are very unprofessional and spoil impressions of the course. Tests and labs are super-easy and do not make you think, and you only need to repeat commands from the lectures.

So many errors in materials. It's unacceptable for course of such level. Even though people mentioned these errors in discussion forumns noone seems to bother about correct em.

Some of the codes shown in the videos had minor errors. Also, a bit more explanation for function (in statistics terms) would have helped in having a better understanding.

This course covered all the topics and overall it's a good one. I wish there were more examples, as it was hard to understand the details in depth with just one example .

This is a good course for beginners with Python. The content is explained in a very direct and comprehensible way, but more programming exercises and tasks are required.