Excellent course materials, especially the videos, with content that is thoughtfully composed and carefully edited. Very good python training, great instructors, and overall great learning experience.
Great course to learn the basics! The supplementary material in Jupyter notebooks is extremely valuable. Really appreciate the PhD students who took the time to explain even the simplest of codes :)
por Jung W G•
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.
por Bhanu P P•
Well taught, it will be hard for beginners with python.
por Kaiquan M•
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.
por Kylie A•
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!
por shahriyer p•
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.
por Pankaj B•
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.
por Minas-Marios V•
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.
por Antonello P•
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.
por David B•
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.
por ILYA N•
They cover basics like normal distribution, z-scores, and plotting data with scatterplots/histograms. In week 4, they give a fairly detailed overview of distribution sampling, and hammer home that you need to be cognizant of bias in your data. To me the most useful aspect of the course were links to third-party articles and web-sites that I would not have discovered otherwise (such as the app from Brown where you can play with different distributions).
por Tarit G•
Excellent course to learn different statistical ways of understanding and visualizing datasets. Also, it was taught how to gather data. What I like about this course is, besides explaining every topic clearly, the instructors have commented on when to use that and when not to and drawbacks of that concept. The instructors were great at explaining things. I am very thankful to the instructors, team and the University of Michigan.
por Vinicius d O•
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!
por RODRIGUEZ G C A•
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.
por Pierre A G Y•
.. 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.
por YuanYuan O•
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?
por Arpita G•
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.
por Tirth B•
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.
por Denys M•
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 :)
por Daniel J Z G•
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!
por JIANG X•
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.
por Geetha A•
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.
por Punam P•
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.
por Amelia M•
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!
por Wei O•
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!