.. When you want to learn some new, don't search only the applied.Becouse everybody can to know the applied, but there are few people who really learn how things work in real life.With this course can learned and review Quantitative and Qualitative variables, Categorical Data, Histrograms, Boxplot, Scatterplot, Pearson Correlations, and more, All applied with Python, was wonderful.

Very good introduction to the concepts and corresponding techniques to implement/visualize these sophisticated and somewhat obscure theories providing a systematic view on the fundamentals. Great job! However, wish Prof Brady could go further in the detail on the non probability modeling and how to handle missing data. Maybe in the later courses in this specialization?

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.

I think it's a nice introduction to statistics. The course provides loads of interesting materials to study which I think it motivates me to know more about its relevant topic. Also, being able to use Python to visualise idea learnt from the course and the case study are really nice part since it helps you visualise the theoretical concepts.

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!

A very good training. I have learned a lot about statistics and data visualization with Python. This is a training that I recommend for those who want to develop their skills in statistics and data visualization with Python. Thank you to the entire team for this wonderful training. Hats off to the University of Michigan.

Out of all the Python courses I can find, this course from U of Michigan is the most fun and interactive lesson I ever seen on Coursera! I would highly recommend University of Michigan to anyone. Easy to understand, yet challenging enough for critical thinking. Thank you Professor and Associates staff for your hard work!

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 🤩😮🤩🤩

The course is very informative about summary statistics and distributions, Pandas, Matplotlib and Seaborn library. The forum is 5 stars, like the whole course.

Only the last week could be improved by adding more quizzes and practice.

"Univercity of Michigan", you are the best! Thank you very much for this 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.

Probably the best course on internet to learn Statistics, understand why you are learning it and also getting the mathematical essence. Visualizing the data solves half of understanding problems and this course has a lot of it. Thank you for creating such a wonderful course.

I did like the course very much and highly recommend it to anyone who can have a basic understanding of statistics and it's representation using Python. The course provide a clear understanding of the main principles and the basic tools. Thanks for the amazing course!

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.