Excelent

love it

Amazing

Thanks!

brutal

체계적인 학

awssem

good!

Nice.

Nice!

good

fun!

Good

good

good

nice

nice

GOOD

gud

.

There are two main fields of study in this course which forms the foundation for the specialization: statistical theory, and programming with python data analysis packages. I learned so much about statistics and visualization that would have taken months to learn in university, I gained a lot of experience and knowledge from this course. I have a decent background in Jupyter notebook from university yet I still learned many new things and got an excellent chance to practice programming in the python packages. The course offered excellent optional practices and gave us several extremely insightful and educational analysis reports done in JN that were related to the module of the week for us to download.

I recommend you have a datacamp subscription to have access to some extra notes regarding programming in the packages particularly Pandas to get the most out of this course by attempting all the optional programming practices.

I loved several things, first that gives you an overview, useful, clear and fun of several basic statistical concepts such as measures of central tendency, different forms of graphic representation, and one of the most important at least for me (already that neither in school nor I would have ever thought about) the types of sampling that exist, because in school there is usually something called simple random sampling and we develop statistical techniques for it, almost completely ignoring the other types of sampling that are really common in real life and that when we face them we don't panic, I know that this is an easy level and I appreciate that in some way, but I would have expected a more difficult course that would have made the concepts really stay in me because I would be thinking about them continuously and how to apply them to the tasks that are presented week by week

I am happy to share that I have just finished the course Understanding and Visualizing Data with Python from the University of Michigan.It is a great and useful course for my career as a Data Analyst. Specifically, through the course, we can explore some interesting topics like where data come from, study design, data management, and exploring and visualizing data. Besides, the lecturers also have a python lab exercise that we can practice coding after the theory session. Most of the data set is from NHANES (National Health and Nutrition Exam Survey), therefore the statistical results are quite clear and understandable.P/S: I am free to share more about this course and another course that I have finished. Please do not hesitate to connect me. Have a nice day !!!!

I think the content here is great and gives you a good overview for understanding and visualizing data without getting into the mathematics. Week 4 is absolutely great in terms of how the information is conveyed by Mr. West who is an excellent teacher in my opinion. I do think, however, that the quizzes and notebook assignments could be a little bit more challenging and I would have loved to have answers to the "more practice" notebooks. I think it would have been great for those notebooks to have been part of the assignments, adding to the difficulty of the course.

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.