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.

I am doing this course as part of the IBM Data Analyst Certificate and even it was the 7th course I take I don't feel it was well explained. The videos pass very fast and the explanations are insufficient to understand what happen in the labs. I think there is place for improvement.

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.

The final assignment is terrible. I've spent a long time setting up the environment because the online notebook is not available. And some questions are hard to find what they are really aimming for. And instruction is actually bad, at least compared to the course.

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.

The course is great, however it seems to assume knowledge of things that are not listed as prerequisite knowledge, mainly Data Visualization methods in Python and Regression models. I would also have loved if there was a recommended reading section on these parts.

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.

Good course for beginners. Some inconsistencies with the code in the slides so may be confusing to follow if you're unfamiliar with writing code. Also, labs for week 5 and 6 don't work. Had to run jupyter lab files locally to get completed.

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