This course is a good begin for statistic learning, especially sampling and visualization ideas. The content is quite long, there are so many lectures as well as reading material.

It is better if the summary reading is provided.

Very thorough introductory course to data understanding with a clear presentation of probability and non-probability sampling, probability of selection, and sampling distribution. Wish I had this course when I was in college!

Can have more adavanced excercisesm and scope of data visualizations can be improved(since those are not that advanced) but for a begineer this is a great course, the links in the courses are great to extend the knowledge.

Decent course for learning some beginner statistics concepts and general information about python libraries. You should try to have some prior coding experience or be good at reading documentation before this course.

Very helpful course for newcomer in data science studies. Great in clearing fundamentals for descriptive statistics, use of python to get these insights,plotting. Overall provide good learning curve.

Excellent high level introduction, would have like the assessment to be more challenging. The additional materials are just amazing for most of them. The notebooks to practice are also excellent.

Good content but I dont like some assigment/assessment, especially the one asking to write a memorandum, which is totally not related to "Understanding and Visualizing Data with Python"

Really enjoyed this course. Looking forward to the next part of the specialization. I thought the quality of the lectures was excellent and made the topic interesting and digestible

Great course to start with Statistics. Methods of data collection and their implications are explained in good detail. Good start with coding in Python visualizing data as well.

The course was slightly harder and longer than I had expected, but the statistics concepts were well explained. However, the coding was not as hands-on as I would have liked.

To pursue your dream career in Data Science statistics is a must. And this course is one of the fun and easy to understand statistical courses I have found on Coursera.

Great course with a great deal of meaningful content. Coursera failure on the Jupyter notebooks made wk 3 a bit more challenging, but overall its worth your while.

It is very complete course, you need to have a good background of Statistics, for the week for there is a lot of text but not enough practice or hand on projects!

Concepts are very well explained, using interesting data sets and examples. Only area for improvement would be additional videos to accompany more of the labs.

This course is really a general overview. The focus seems to be laying a foundation which I hope we build on in a more technical way in upcoming stats courses.

Good course if you have learned the foundations of statistics already and want to review it while learning how to use Python for your statistical analysis.

I would have preferred lesser time in Week 4 on Sampling. Those lessons could have been moved to the next course as they don't strictly relate to Python.

Content is great. Would be a great learning experience for those who have some idea of stats and python. Definitely, not for entry-level python and stats.

Generally, it is great! It would be better if examples were provided to illustrate the concepts of confidence intervals and hypothesis testing in week 4.

It is a good course to start with python and statistics, the part of sampling distribution I think should improve, personally I don´t like large speeches

Pretty good introduction for using Python for statistics. Some of the lectures were a little dry, and the test material could have been more challenging.

A lot of thoery. It was a challenge to grasp all of the statistical terms. Not really much of a python or practicum course. I guess that comes later.

Generally useful, not entirely sure what the peer review assignment was, and could have done with more python assignments to help with consolidation

great course - the labs were very helpful. Only suggestion, more practical / relatable examples when explaining certain areas, especially Inference

The high-level theory was well explained. It would've been awesome if there would be more hands-on labs, and if the theory would go more in depth.