The course was not detailed enough. I think the instructor assumed that people taking the course would know a lot about Regression, Correlation and some other statistical functions, that it was hard to understand or follow at times. Maybe consider 'dumbing' down down the statistical functions so that newbies can also follow.

Overall interesting course. Thank you.

First 2 weeks were fine for beginners, but after week 3 where all new different syntax and concepts like seaborn, visualization, Regression models etc etc were thrown in, it got way too advanced for beginners especially when there insufficient and effective practices available to hone the knowledge. Have to spent most of the time self-learning on other websites.

This is an excellent course for beginners in the data analysis and data science fields as it explains deep technical concepts in layman terms along with the Python code for the same. However, not a perfect course for someone wanting to go into conceptual depth or wanting to expand their knowledge of analysis in Python beyond use of standard packages.

Needs to rely less on the cognitive class platform, just host the ipynb files externally as the labs are inaccessible alot of the time. Course only covers regression models, I would've liked to see SVM, KNN and other algorithms. However the course excels in explaining the relevant maths related to regression and regression evaluation

It was educational, but when you pass a quiz, there should be an option to get answers to the questions you got wrong. And the practice exercises were filled with mistakes, particularly week 5. And the instructor was not responding to students' questions for week 5, which was one of the most challenging weeks. That was annoying.

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.

The labs in the first few weeks didn't work. It was frustrating and took a LOT of extra time to find a way to finally complete them. The concepts in later weeks were not adequately explained for someone who isn't as versed in statistics and required supplemental YouTube videos to (try to) understand.

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.

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.