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.

Quality control is bad. There are lots of typos, slides with wrong animations, and mistakes in task descriptions. Overall, the look and feel is really inconsistent. My students would get bad grades if they submitted a quality like this.

Good topics, but video instructions not clear enought. I had to go search on Internet for the topics. Sorry.

Maybe they were too simplified for videos. The subjects needed more exploration.

Anyway, it was a good starting point.

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.

There is more value to this course if you use ChatGPT as a supplement. The course is really high level and uses some codes that can be confusing (and it doesn't break it down) if you don't come from a coding background.

it will be better if you can illustrate how to solve the problem step by step and explain what is the parameters that you put inside the function. Some videos are great but some videos seems a bit rush.

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.

Few mistakes in the questions made for this course. Also, more questions for quizzes are needed to test the learner's abilities better. Slightly harder coding assignments would also be a great idea.

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.

one must have prior knowledge about python and have little bit understanding of statistics. over all course was good but should be improved in terms of Data, ML terminologies, etc

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.

There are lots of typos in the labs and assignments, which can be frustrating. I expect better quality from IBM. Content is great and easy to understand nevertheless.

The staff helps answer questions, which is very good. but there are some bugs never got fixed, and few practice questions give wrong answer, which can be misleading.

This course is quite good. The bad thing is that the arguments of the last "lesson week" are treated very superficially, taking for granted some advanced knowledge.

A lot (too much maybe) is covered in this coarse

It really helps a lot when you know some statistics. Like linear regression,

Why gridsearch was covered I wonder.

It could be better. It provides a lot of coding, but it does not explain all the aspects of it. The tests are not a good representation of what has been done.