学生对 密歇根大学 提供的 Understanding and Visualizing Data with Python 的评价和反馈

If you are searching for a course who could either teach you all about the world of statistics - ranging from statistical analysis with awsome examples and explanation with demosntrations of statistical methods - and at the same time force you trough programming, this is the right course.

I'm very grateful by the efforts of course's team in undertaken such work! I'm now more prepared to advance in my carrer, thanks to it!

Excellent course for an introduction to python statistics. Keep in mind that this is not an usual statistics course, the fact that it covers python changes it a lot. I had almost no prior knowledge about programming so I had to learn in order to keep up with the lectures. I recommend to come here after being familiar with it and maybe having checked info about numpy, pandas and matplotlib.

An interesting teaching style, full of life. Also, the quality and quantity of content is extremely well. Peer Reviewed "Data Memorandum" for a company is an excellent touch to the course. I would recommend this course just for that it self. Otherwise also, this course can be recommended to any beginner who wants to try Data Science from the Maths angle.

All the best.

You need to have atleast a couple months of coding experience to do this course. Stats concets are explained nicely. I liked their approach of teaching new concepts. They made their own data sets to teach us and give us a good hands on experience with manipulating and crunching data. This course would be a good start for your journey towards data science/analytics.

A very nice manner of teaching where lecturers used a variety of real-world examples which made hard things easier to understand.

I have learned basics of python language including data types and syntax, core features of pandas, seaborn and numpy libraries. Recalled for myself statistical principles and approaches.

Besides all of this, there are a lot of fun :)

Excellent course. Although I do believe it should have more hands-on experience so that we, as students, can improve their python abilities and can feel more comfortable when using python for statistic analysis. In addition, I believe tests were too easy so it could definitely use a bit more difficulty. Yet, the course materials and the lectures were great!

I love the depth and breadth of the content. It provides in-depth knowledge of statistics and wide range of context information and supplementary reference learning materials. I also appreciate that each lesson is accompanied by hands-on activities using Jupyter notebook which definitely has helped me gain a deeper and clearer understanding of the content.

The course gave a very good understanding to type of data (quantitative, categorical) , histogram, correlations, standard terms used in statistics, how sample plan needs to be created . The peer review exercise was very nice. I enjoyed doing it. The exercises in python looked basic. Overall a very good course and I enjoyed learning through this.

Very nice experience to join this course, which help me to understand and visualize the data using python. I recommend this course to everyone and too friends, as all the instructors clarify all the concepts so nicely. I Thanks to everyone involved in this course to gave me opportunity. Thanks to Coursera for giving such platform.

This first course in the specialization was very helpful and outstanding in the way it created the concepts of statistical programming and data visualization along with statistics theory. All instructors were very helpful and my special thanks to Brady T. West and Brenda Gunderson who were splendid in their teaching methodology.

I really love this course! This has been my best learning experience since I use Coursera! I really appreciate Brian to answer our questions in the forum, even though some of my question is really silly, but he is also very patient. The content of this course is very nice, I learn a lot. Thanks for the efforts of every staff!

The course content is GOLD! Seriously, several of the things that were taught in this course are already known to me but after taking this course, it gives me the real insight and physical significance of those things. After this course I understand how to actually use those things practically! A must do course 🤩😮🤩🤩

A very gentle introduction to data visualisation with great effort from teachers and students to make the course refreshing.

The course will not be very mathematical or coding heavy.

Most of the quizzes are fairly simple and motivate the student to gain more insight by opting for further courses in the specialization.

This course gives a solid understanding of core statistical principles, sampling, approach to making inferences, plus some experience with data manipulation using Pandas and data visualization using Matplotlib and Seaborn libraries, as well as some experience with the Numpy library (all in Python)

The course is really good, videos and materials presented are good, there are lots of recommendations for additional readings and web tools, it is also interesting the change of presenter, it helps to keep attention. But I think it is not for somebody who has never heard of Statistics before.

Excellent course to better grasp fundamental parts of statistics within the data analysis space and how to create some basic visualizations. The course is not Python heavy, although some experience working with Pandas, Numpy and understanding of basic loops and list comprehensions will help.

This course is excellent and very well thought out. It covers the fundamentals of sampling methods and data analysis as well as their practical applications with Python. I would recommend it to anyone willing to learn statistics (but you should already have some basic Python knowledge).

I feel 100% more confident in my job now. We just started using Python for analysis and I am probably now ahead of many of my coworkers in a super short amount of time. The class got me over the hump in the learning curve so I can progress much faster than trying to learn on my own.

Sometimes, the lines in Jupyter notebooks are kinda hard to understand. Yet, there are a lot of materials out there online for us to explore; for this, I also learn how to solve programming problems by myself. In general, I like the courses and the instructors a lot.

Great refresher course for those who have taken courses in statistics previously, or a really good introduction for anyone new to the subject. Also a great introduction to using Python for statistical analysis. Everything is clearly laid out and easy to follow.

Great course. The materials are thoughtfully put together and paced well, and I achieved my learning objectives: a second pass through undergraduate level basic statistics and a basic idea of how to use python to do math an evaluate statistical and other data.

An excellent combination of lectures and labs. The material is extremely well taught. The mixture of lecturer styles helps to maintain interest. There is good additional content available for the deep dive topics. I found the content fresh and relevant.

This course is such a great course for beginners in Python like me. It has very helpful reading materials to aid you and great tutorials for Python using Jupyter Notebook. This made me excited to explore Python for statistical analysis in my research works.

This course provided a valuable introduction to data handling and visualizing with python. I very much valued the mix of videos, readings, and Jupiter notebook work, as well as all the pointers to additional resources for deeper dives. And the cartwheels!

A well paced stats refresher which covered the core material well and skillfully introduced current research. The fourth week was a solid introduction to sampling methodologies and inference. Looking forward to the next course in the sequence.