A very well explained and well-structered course. I highly recommend to those who want learn statistics along with python programming. This course majorly focuses on the visualization aspect.

The course is very well structured. Teaching and links to related articles help us understand the concepts better. Jupyter notebook based python learning is very comfortable and easy to use.

Very thorough and comprehensive. The Python labs are a great complement, but introductory knowledge of Python is strongly recommended...

la ultima semana debería ser divida en dos , y tener mejor interacción entre la aplicación de python y el contenido explicado, ya que al ser tantas horas de video puede llegar a ser extenuante y aburrido.

A very basic but good introduction to understanding data. An introduction to data visualization. Not a good introduction to Python, but does show how to use Python functions to present data.

The title of the course is a bit misleading. The focus is really on some basic Statistics, with Python notebooks thrown in to demonstrate some of those concepts. However, you won't get much help understanding Python. Even the workbooks involved use some interesting methods/libraries, but not much detail in the course about them, other than the particular use they come up in. It's a 4 week course, but can easily be completed in about a week, possibly less. If you already have a fair foundation in Stats, this course probably won't add much value. I did enjoy the instructors and they were trying to keep things interesting.

Good stats course. Needs more Python. Much of the Python is just watching or clicking run. Would appreciated more opportunity to walk through the coding with hints and hidden solutions to gain some proficiency.

There is incorrect course title for this course as in reality it’s Statistics AND partial illustration of it using Python. There is no consіstent exposition on Python libraries and frameworks.

I think the order of lab components should be rearranged. Introduction of core python mechanics should come before the module in which each code is implemented.

Well taught, it will be hard for beginners with python.

Do not waste your time and don't take this course. You won't develop any tangible skill from this course.

The course is mostly a narrative of statistics and has nothing to do with the real statistics with is a branch of mathematics. Virtually zero formula is shown by the time you complete the course.

Some of the instructors are good but the majority of course is narrated by Brady which is good enough to put you to sleep. He reads from a screen and following his line of sight is jsut as distracting as the monotone narrative.

Also what they advertise for the length of the course is an absolute misrepresentation, the course takes at least twice as long as they show in the title page. There are much better courses out there, don't waste your time and money.

The theory taught in the course in the videos is very basic, but the lab modules expect an advanced understanding of Python and statistical concepts. It would help to have some videos to explain how to engage with the lab modules and also some videos and resources that explain the various commands and concepts used in the lab modules.

The courses is supposed to help students learn how to use Python to understand and visualize data. However, the course lacks focus on the subject as well as tasks for practicing Python code. Lack of practice. The peer-reviewed tasks are hilarious - you will be asked to describe how you'd visualize metrics in (Python you would think? No!) words. This is so easy to turn this task into something actually useful: create a notebook with preloaded data and ask students to come up with metrics and visualize them. No-one came here to practice English writing skills, and this shows in the tasks of the students. The quizzes are easy, the final quiz has all answers in hints which are not even hidden. That's actually a pretty good representation of the course creators' confidence in the students' knowledge after the course - we know you didn't learn anything, so we will just give you all the answers. Concentration on the course goal. The course is too short for trying to pack all the information in it. The last week was interesting, but if I wanted to learn about study design, I'd take a course on Study design. A lot of topics can be described as 'Understanding and Visualizing Data', and the difference between a well-designed course with thought-through structure and this course is that the good course is focused around the narrow subject (e.g. using Python for understanding the data) and delves as deep as possible instead of throw in different topics that are related to 'understanding data' in such a short course. And one last thing I would like to bring up is the students teaching in the course. I understand that it was probably the project they got credits for, and the professors thought that it's be a great practice for them. This is a great initiative, but the Coursera students actually pay for this course, and, I am sorry, but the students lectures were bad for the most part - the explanations are not coherent, the repetitions, the 'we are not going discuss that' (then please structure the lection the way the you don't use the function you don't want to explain). While it's understandable that students need more practice in teaching (they are students after all), the question arises as to why one should pay to listen to their 'end-of-the-course project'.

The statistics material is extremely superficial and naive to anyone with high school level of statistics. On the other hand, the Python lessons are extremely difficult, going directly to complex tasks with no explanation of the intermediate skills required to understand what is being taught. This is the case even if the course description says only a basic level of Python knowledge would suffice to follow the course. I don't see how this course could be useful to anyone.

The statistics concepts are thoroughly explained, however the course lacks in explaining how to work with Python. In the labs you can see a bunch of code which is not explained in any way.

The videos are rather long. The presenter talks a lot. He should be precise, non-repetetive.

This "Understanding and Visualising Data with Python" training offers: 1. lecture videos teaching you concepts 2. graded quizzes 3. a graded assignment where you have to create a survey design 4. Jupyter notebooks with exercises for you to explore statistical concepts in Python 5. walkthrough videos on Jupyter notebook exercises if you need some help to unblock yourself or when you want to understand why certain things were done The training was alittle lengthy but well worth the time. At times, because concepts can be explained in long sentences, you may need to rewind and revisit certain parts of the videos to get the full meaning of what has been explained. Overall, this training refreshed my understanding of: 1. basic statistical concepts - statistical measures, population, sampling 2. using numpy, matplotlib, seaborn, scipy packages in Jupyter notebooks (which was good because I currently dont code in Python at work) This training also explained practical ideas such as: 1. stratifying, clustering, why these concepts are important when sampling 2. issues with certain sampling approaches 3. useful ways to turn a non-probability sample into a probability sample, so that the analysis/claims you present would be grounded in a more solid basis. Points 2 and 3 in the list above were neither covered in school nor statistics texts in the past. So like me, you may get the chance to learn something new to apply to your work.

THIS! This is a very well thought out and planned course! It is up to date and doesn't use expired packages or expect you to program WAY beyond the level they teach. The instructors/lecturers are awesome and easy to follow (although the ones who do python speak a little fast!). THIS is what I was looking for in a specialization/ class. I do recommend doing codecademy's python training if you know absolutely 0 python (like me), but even with zero prior knowledge this course walks you through it very nicely! THANK YOU soooo much! I greatly appreciate the thought that went into designing this and the following courses and will definitely take a closer look at UM when I apply for a master's program!

This excellent course provides a good introduction to methods used to collect data and draw meaningful inferences and to use python for this purpose. The course also shows how to write simple python scripts that, through random simulation, illustrate and test the theory behind the statistical methods.

You will find this course much easier if you have a basic understanding of python and numpy (arrays), pandas (dataframes), matplotlib (scatter plots, histograms). and seaborn (histograms, violin plots, box plots). You don't need to be an expert on any of these, just a few tutorials. Then you will be ready to learn a lot about statistics and python from people who know quite a lot about both.

From my point of view, this course was very fundamental for learning statistics with python . I have learnt a lot about different statistical model with how to describe by visualizing them. I have also studied uni-variate , multi-variate data analysis and introduced to a practical NHANES model which was implemented on python code to get different visualization of data analysis. Finally also learnt about using sampling distribution , sampling variance and probability and non-probability sample. This course will definitely boost up confidence for statistical analysis with python.

The content is very comprehensive, provides an introduction about all the useful things necessary to do statistical data analysis with Python. However, some of the quiz questions are ambiguous and its not clear to me why the chosen answer was the correct one. I submitted feedback on one of these quizzes but I didn't receive any response. Other than that, I felt the instructors did a great job of explaining the fundamental concepts in statistics and the basic tools in Python, and I am glad at having taken this course.

This course introduces basic but crucial statistical concepts that any data analyst should be aware of, and offers detailed explanations of the steps that one should follow when desinging an observational survey. I have had several courses online and on campus, but none have done such a great job at explaining study design as this one. Note, however, that knowledge of basic Python programming is a must-have before attending this course, and I would also recommending getting one or two tutorials on numpy and pandas.

Very good course for people that don't have any knowledge of statistics, like me. The material is detailed, the concepts are explained clearly in the lectures and the instructors make it easy to follow.

I don't understand why people complain about the programming assignments being difficult. Normally they cover things that are shown in the lectures. When that is not the case, links to the relevant documentation pages are presented. If anything the assignments are too easy and there should be more.

This was a fantastic course! It did a wonderful balancing act of getting students to use jupyter notebooks/python for data analysis and visualization with a very good introduction to the different types of sampling methods used in research studies. I really enjoyed the assignment where we needed to create a memo to a pizza company - it really was a clever exercise that didn't hold your hand. Overall, a really great course that made me eager to continue on with the specialization.

It's a great course as usual from the University of Michigan, lots of contents but i feel that i'm not much the target audience for this course, it seemed to me something more aligned for someone who wants to work on the collect data side, someone who wants to work at a survey organization something like that. For now im looking for courses in coursera with data science/analytics focus, hands-on projects and stuff that will aggregate more on my in-progress university course.