The videos, readings, and labs were not sufficient for me to feel prepared for the assessments. I ended up using outside resources just to understand what was being presented here. There was really no explanation of why you would use certain tools or the underlying statistics principles; the course assumes a lot of the learner (both in statistics and Python) considering it's aimed at beginners. I believe this is a newer course, so hopefully it will continue to be revised, but I was disappointed in the content compared to other IBM courses I've taken through Coursera.

At first, I find this course to be somewhat challenging at first since I don't have any prior knowledge in statistic, but after a few lecture and some self study later, I have gain a pretty good understanding of statistics and its application in Data Science.

I especially like the final assignment as it give me a feel for what being a Data Scientist is like. It also make me go through all of the previous lab for reference. By doing so, I have a chance to review the things I have learnt and get a deeper understanding of the material. I can't speak for everyone but if you are completely new to statistic like me and planning to break into Data Science field, I think this course might be a good starting point for you.

I enjoyed taking this course and found it was well explained. Having been out of school for a long time and not using stats in my daily job, I found that I had to listen to the videos over and over again to fully understand the concepts introduced. I also struggled initially with python as it was a new concept for me. I recommend it for others, take it slowly and try to revisit the videos and readings and ensure you follow and thoroughly complete the lab exercises as this will help with the project.

This course was seamlessly easy to understand and follow. During my undergraduate studies, I struggled with statistics which made me a bit worried taking the course.

I am glad I pushed passed my fear and took the course , as it has sparked my interest to learn statistics, how it applies to data and making business decisions.

Thanks Aije and Murtaza - I look forward to taking more courses from you both on here.

I really enjoyed taking this course. It was really easy to follow and I absolutely loved how the course was put together. I will recommend anyone looking to use Python for Data Science to take this course.

Amazing course . Very easy to follow . Definitely improved on my python skills . Would 100% recommend .

Challenging for non statiticians

There are mistakes in examples, in assignments, and final project! Creators never respond in Help section.

I really enjoyed the course. However, one has to keep in mind that the concepts presented are very basic.

for example, regression is a highly complicated topic and interpretation of the results needs more in depth analysis, however, for an introductory look into the topic the course is very well structured

Excellent course to help clear doubts for the level of statistics needed for data science. It a great experience. well done IBM!

Being a Mechanical Engineer, I already knew Normal Distribution but don't know the T-test, ANOVA, etc. This course covers pretty much about doing statistics using Python but you should know statistics before doing this course. The course is great it also help you learn a bit of Seaborn visualization library.

I would recommend to do some course on Statistics first then do this course for implementing statistics in Python. The Quiz, Assignment and Lectures were good. Only thing which was bad was it didn't explain the value we got in the summary of results of statistical tests we performed. Also the explaination was very brief.

Good course, many subjects are covered. But be careful if you're totally new to statistics and hypothesis testing, this course is rather fit as a refresher.

Unfortunately the lecture slides are not available for download and some of the transcripts need serious amendments. In all Jupyter labs the kernel did not connect for a long time and attempts to export notebooks as pdf threw internal server errors. Such things are disturbing and could be prevented with proper monitoring and proper technical setup. The peer review in week 6 must be performed without having the approved solutions; this is not very professional.

Overall, the course gave me a brief but informative look at the basics of statistics with Python. Once again, the many practical exercises were very nice. However, the speed of the p-value and regression was a bit too ambitious for me. Would have appreciated some more details there or a good link to somewhat short and informative. But as said, overall, another very informative course.

The videos, readings, and labs were not sufficient for me to feel prepared for the assessments. I ended up using outside resources just to understand what was being presented here.

The course felt disjointed at times and there was a lack of clear explanations. The expectations for the final project (formatting, etc.) could have been stated more clearly to reflect the marking rubric. The final project was otherwise nice and quite summative.

Not the greatest course on this platform. The structure of the course is somehow confusing and it's got a bit old, should be updated and offer better knowledge.

It was an excellent course with fundamentals of statistics. It taught me why of the python library models. The quizes and final assignment is very helpful for learning. And, the final assignment really helped me to turn information into my own skill. Thank you very much for designing this amazing course!

Enjoyed course the most from teh IBM Data Science Modules! Being less technical, it was easier to understand with minimal knowledge on the subject and the excersixes and final project were very practical and helpful in understanding

Its a good cource. I learned about the basics of satistics and how to apply it in python and data that we have. I walked through many cources but in Courcera, this is the best basic cource for the DataScience enthusiast.

Excellent Course! Clear and didactical explanations, objectives exercices and very oriented subjects! For those who are interested in data analytics, this the trainning you should take!

The final assignment is very well designed, I was able to review the entire course material and consolidate the learning. I have now a good understanding of hypothesis testing.

A well structured course, simple and direct to the point, with a little of exercising you'll come out with a huge understanding of the statistical concepts.

Great introduction to basic statistics for data science. Python specialization suits those with no experience in the language.

It is few of the Data Science courses in my learning series. This is one of the Best in Series. Thanks to the team.

Good albeit very general presentation of useful libraries and Python programming language for Data Science.