Good introduction to basic statistical methods with an emphasis on working with surveys, and a good introduction to basic statistical techniques with core Python, numpy, matplotlib, seaborn and statsmodels. Instructors and presentations are excellent, very clear. I would give it five stars if it were more interactive, i.e. with more in-video quizzes, and practice quizzes between videos. Also, I wish I had take this course before I did the Applied Data Science with Python specialization, also on Coursera, but, alas, it wasn't available then.

The course in general is great for overview of basic statistics and how to display the descriptive part using charts in Python. The latter part is something new I learnt from this course. However, I rated four because there are still rooms for improvement. First, week-3 course (probability vs non-probability sampling etc.) is a bit too long and repetitive. Second, while the peer-graded assessment is interesting, I don't like to wait for my work to be reviewed, hence delaying the overall completion of my participation.

The course could be improved with more quizzes to apply what lectures cover. A lot of useful information is presented, but there was not enough opportunity for us to apply it. Also, the course should present more examples of statistical concepts. At times, it felt as if I was just listening to an audiobook. Statistics can be better understood by applying concepts and visualizing. Week 4, in particular, felt very rushed. There was a lot of "this will be addressed later", which diminished the relevance.

It is a fairly good course for statistics introduction. However, the explanation on how to apply with python libraries is not well-organized. Learners must have a well understanding of numpy, pandas, Matplotlib and Seaborn on their own in advance, because the TAs just read through the code without explaining why in details.

The statistics concepts lecturer is very good, but the TA didn't describe python libraries or modules selection and application concept.

Best course for Statistic to understand from Datascience POV. If you're a software developer or from computer science, then this course is good to go into to understand the statistics for DataScience.

Deducting one start for :- 1. Week 4 was too much lectures and less assignment. Felt rushed.

2. The pythonic technical tutorial was not explained much technically and hence i had to spent a lot of time on different course to learn them.

I am so happy to learn about data visualization through python. This course gives you a good amount of insight on how to visualize data through python and helps you understand the graphs and statistics behind it. I am very thankful to the team who have made up this course as it has been very helpful to me and other students like me to be much more confident with our knowledge in Python and Statistics.

The course is pretty technical and there are some gaps in the information that is presented (that will probably be addressed in later courses in the specialization), but it certainly whets one's appetite to learn more about statistics. I also appreciated the opportunities provided to apply the code that was learned and to see some of the theories in action. A worthwhile take overall.

This course is an interesting course that shows how to use some tools in python to visualize data with python. Furthermore, it explains some of the essential aspects of data concerning statistics and probability. However, I consider that the focus and the form that the last week is managed is lacking. Moreover, some of the code is written in deprecated functions and should be updated.

Overall, this course is good to expand my horizon in CS and Statistics area. But some content may be a little bit vague, like those in the labs. though accompanying with explanatory videos, a lot of points regarding the lines of code are still not very intelligible. This is the only one thing that I'm not quite content with in this course, but other parts are really beneficial.

This is a tactfully curated course for getting your legs wet in statistics with python, I personally was not completely comfortable with the statistical explanation and had to refer to multiple sources but hey a splendid job in other crucial things such as working around a code!! 10/10 recommend!! I wish to connect with the tutors via Linkedin

Having no prior knowledge of stastics and it's terminology, i felt more number of practice question should have been given to understand the concepts in a better manner. The assignments with python could have more number of questions and bit more complex as well. One should refer extra material to understand the concepts well.

At times I felt as though the content could have been delivered on a longer timeline. Some of the Python was very dense if you aren't already skilled with Numpy and Pandas. Coming from Python Basics, they took more time to explain programming concepts. Overall I enjoyed the content and the instructors. Thank you!

Has a lot of theory, so any non statistics student can go along with it. Statistical theories are explained very well with a lot of references and cool website, articles and link. If anyone wants to get the most out of this course he/she has to spend time learning to code by himself.

It was ok. To be honest, I expected somehow more from this course, but I cant say that something was bad. I would like to have more practice in python, because the exercises are made more for watching or just repeating, not really for practising and applying new skills.

This is a good course for people who do not have a strong background in programming. The videos and lectures are extremely well planned, . However, there is not much practice you can do, the assignments are super easy and they do not cover much of what is taught.

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.

So far the best statistics course I have taken on Coursera! High quality lectures and wonderful lecturers. The only thing I didn't like is the order arrangement of the homework. It started too hard, but once you overcome that, the rest is pretty doable.

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).

It is a great course to learn statistics. The lectures and teachers are excellent. I think they should change the way they teach Python programming and should use other tools for students to practice, like the tool to learn R (R labs).

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

Great Course. Week 4 material is a bit theory heavy. Course Era Notebook has some inconsistent behaviour. I used colab.google.com. Overall , Great Experience!

I need the certicate for completion of the first out three courses!

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.