A fun and easy course for someone who wants to start knowing what it is about to analyse and visualize data, but it did not provide enough in detail, to the extent I would like to learn, about the visualization packages of python (i.e. matlibplot and seaborn).

Great starting point for getting into statistics. Covers some good fundamental areas which other courses may skip over. Designed well for the online delivery. Assessment sections were a little varied e.g. some were great, others felt far too easy or limited.

Video lectures and suplemental material were put together very well. I enjoued the class and learned a few new things in the process. If I had to make a sugestion it would be to include more practice opportunites however there were enough to be effective.

the overall structure of the course is good and right to the point but I gave only four stars as the statistical concepts are confusing without understanding the jargon, in my personal opinion, I feel a video explaining the jargon would be very helpful.

The teachers are all enthusiastic and very professional. Videos are great! But I cannot give a five-star since the assignments do not seem very effective, and there are some old codes that are only applicable to python 2 (rather than python 3).

The classes and content are great. It has fixation exercises, which helps a lot. I would suggest as an improvement to put more examples in unit 4, because there were times when the concept was not very clear, especially complex samples

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.

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.

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

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.

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!

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.

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.

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.

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

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.

The assignments could be made more interactive,like assigning us problems on which would have to visualize the in jupyter by us to get the answer.

Python exercises can be more interactive and the examples for sampling could be explained by taking a small data set for getting a realistic idea

some of the materials in this course was not very clear but I think there will be more explanation in the upcoming courses in this specialization

Nice course, there are very good explanations, but I thought there was going to be some mathematical background, I think it is important too.

It is really good course for professional data scientist as mostly explains how to visualize data and graph and make an conclusion out of it.