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Excelent

love it

Amazing

Thanks!

brutal

체계적인 학

awssem

good!

Nice.

Nice!

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fun!

Good

good

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nice

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gud

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Good course but definitely wish the practice material was a little stronger or more challenging. I quite like the lectures and the professors and teaching staff definitely know their stuff being UMich's Stats department of course the content itself is great. The lectures are great, the solution sets they give are great but how exactly they did those solutions... well let's just say I personally wouldn't just rely on week 1s coverage of the basics to get to there. I would strongly recommend people have at least a passing understanding of Python like through the Python 3 Specialization from UMich or Py4E from UMich. AND I would say this shouldn't be the first time you use numpy, Pandas or seaborn. I would suggest going through the Numpy Tutorial on the numpy site, the Pandas tutorial on the Pandas site and follow up with Kaggle's micro courses on Pandas, seaborn and data cleaning. This course, true to its name of the stats specialization is really an application of basic descriptive statistics like for Exploratory Data Analysis done with python. Which is what I was looking for so this is exactly what I wanted. Again lectures solid and the solution to the exercise notebooks are GREAT. They don't explain in great detail besides linking documentation how they got there so knowing Pandas indexing, shallow/deep copy, the pandas stats functions, Pandas pivots like melt and stack etc. This really takes someone who knows the basics of Pandas, teaches them the very basics of stats like stuff from high school early college, and applies it to a real dataset as you would in an everyday EDA setting. And it is EXACTLY what I wanted to teach that. Just wish there was more practice on this stuff. Youtube tutorials don't go as indepth imo.

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 !!!!