Lectures are great but there's little practice material and the quizzes are terrible. The quizzes are actually super easy but they don't cover much material from the course and sometimes introduce concepts and terms that were nowhere in the course materials.

If you want a good intro to stats without any actual testing, the lectures get pretty in-depth and the explanations are excellent! But if you're looking for lots of practice with stats in Python, you won't get much here.

This can be a quite helpful course for beginners. I really liked the course because it thoroughly introduced me into Seaborn (visualization library) which I was unaware of. Also, some of the practical exercises truly help you develop your pandas skills. I really enjoyed week 1-3, which truly challenged me and introduced me to new concepts with a good balance between practical and theoretical. However, week 4 felt a bit off. The contents could've been split into two weeks. The practical tasks are minimal compared to readings and videos. And the final quiz covers like 15% of all that was taught in the week. Concepts like CDF were never taught but employed at the end when talking about the empirical rule.

Overall a poorly designed course. If you know a little bit of stats and are hoping to expand your Python skill set, then don't even bother wasting your time with this class. The programming instruction is extremely weak. This is basically an intro level college stats course but the instruction is completely lecture based and quite poor (much of the instruction is left to TAs). The quizzes and programming exercises are not challenging.

This course gets two starts from me because the practice programming exercises are actually great, but no answers are provided so it is hard to check your understanding of these problems.

This course still has spelling mistakes in its quizzes, which in a programming focused course are big, and the instructors don't seem interested in fixing them. The result is you have to guess through their mistakes if code is suppose to not work in a quiz because of the error or the error is not supposed to be there in the first place and the code is valid.

The course contents are good to an introduction or refreshing in statistics but the assigments are not really well prepared, and contains many unrepaired errors. This drops down the level an educational potential of this course (and the entire specialization) and converts it in a poor educational resource and a waste of time, in my opinion

I must say that this is a must take course for ones who are aspiring a career in Data Science. All the concepts were laid out so beautifully and it was explained very clearly with visualisations of each real-life-examples. I enrolled in this specialisation before starting my Machine Learning so that I have all the necessary fundamentals of Statistics. Brady Sir & Brendra Ma'am are simply phenomenal, the way they explain the concepts are incredible. The concepts gets etched in one's memory. The most exciting part of the course is Brenda Ma'am performing a cartwheel !! For all the ones who are enrolled, don't forget to watch it out.

I don't understand what is the point of the video lectures. The lecturers are just reading the PowerPoint slides. Their focus is not on how to help the students understand the concepts but on how to read the slides as soon as possible without misreading words.

The contents are good, but while I was listening to the lectures, I just realized that it would be exactly the same as reading the slides on my own.

Well organized material. The Discussion forum was the best one I've experienced in my Coursera education. All my questions were answered within one day. The best statistics class I've taken yet!

Never have I come across a course half as interactive as this and it was a much needed confidence booster for a beginner like me. I look forward to completing the specialization : )

this course is well below my expectations. there are none real life examples or detailed visualizations, except a few simple plots. There is no step by step coding lectures. There are some youtube videos which are much better than this. Dont waste your time if your goal is to learn python, other than getting some certification.

Excellent course materials, especially the videos, with content that is thoughtfully composed and carefully edited. Very good python training, great instructors, and overall great learning experience.

Very clearly explained each and every topic. Though understanding all the concepts at first is not possible if you got through the videos twice or thrice than you definitely get the concepts

Excellent introductory course to statistics. Great use of NHANES dataset to demonstrate techniques on real dataset. I would appreciate a more demanding project at the course end.

I strongly recommend this course to those who want to begin python programming applied to statistics. It launches a very sound foundation for statistical inference theory.

I strongly recommend this course to those who want to begin python programming applied to statistics. It launches a very sound foundation for statistical inference theory

I love the U of M courses! I get so much out of them. Thank you again for helping me to advance my knowledge of Python and deepen my understanding of statistics.

This is the foundation course every aspiring data scientist needs

This course is definitely a beginner level course in both python and stats, but it is very well done, and there is plenty of content.

More hands on assignments would be desirable.

Great course to learn the basics! The supplementary material in Jupyter notebooks is extremely valuable. Really appreciate the PhD students who took the time to explain even the simplest of codes :)