In this course, learners will be introduced to the field of statistics, including where data come from, study design, data management, and exploring and visualizing data. Learners will identify different types of data, and learn how to visualize, analyze, and interpret summaries for both univariate and multivariate data. Learners will also be introduced to the differences between probability and non–probability sampling from larger populations, the idea of how sample estimates vary, and how inferences can be made about larger populations based on probability sampling. At the end of each week, learners will apply the statistical concepts they’ve learned using Python within the course environment. During these lab–based sessions, learners will discover the different uses of Python as a tool, including the Numpy, Pandas, Statsmodels, Matplotlib, and Seaborn libraries. Tutorial videos are provided to walk learners through the creation of visualizations and data management, all within Python. This course utilizes the Jupyter Notebook environment within Coursera. The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
Instructor Details
Courses : 3
Specification: Understanding and Visualizing Data with Python

59 reviews for Understanding and Visualizing Data with Python
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Christine B –
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.
Jan T –
More hands on assignments would be desirable.
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).
Aaron O –
Easy material to understand, and simple enough to actually apply the concepts in Python. Good beginner course.
Maria G –
It was an excellent course, the explanations were very clear and the examples given really helped to ilustrate the concepts.
Divyam A –
Can have more adavanced excercisesm and scope of data visualizations can be improved(since those are not that advanced) but for a begineer this is a great course, the links in the courses are great to extend the knowledge.
Nitish K N –
This is the foundation course every aspiring data scientist needs
Thomas C –
Video lectures and suplemental material were put together very well. I enjoued the class and learned a few new things in the process. If I had to make a sugestion it would be to include more practice opportunites however there were enough to be effective.
Marina P –
It was ok. To be honest, I expected somehow more from this course, but I cant say that something was bad. I would like to have more practice in python, because the exercises are made more for watching or just repeating, not really for practising and applying new skills.
John S W –
Excellent course. Wish I had had more feedback, or someway to interact with an instructor.
Kevin K –
Good Course to take, to get to know basic application of python to do statistics
Satrio T S –
Excellent
Hyun W C –
Love the content and structure of the course.
Samuel B –
Really enjoyed this course. Looking forward to the next part of the specialization. I thought the quality of the lectures was excellent and made the topic interesting and digestible
Marcelo E D C –
Nice course with multiple challenging opportunities for practicing the learned concepts with code.
Luis D R T –
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
Beatriz J F –
Excellent!
Hossein P –
This course started well, but unfortunately, I think they should add more extra example and focus on the topics more in depth, I can say in each quiz I spend around 3 hours to find related topics in the internet and learn them to answer to the questions and I think it should be cover by the course itself.
Andria –
Very nice.
KIRUBAKARAN B –
The course gives a strong foundation on sampling theory, distribution and inferences
Vignesh R –
Python in week 2 is largely unexplained, also course could have dived deeper into statistics
Matthew –
Pretty good introduction for using Python for statistics. Some of the lectures were a little dry, and the test material could have been more challenging.
harshit s –
Just a tad slow for my pace, but the understanding and course material are on point.
Frederick A P –
Very high level introduction to pandas and visualization. It is a good resource for finding different pandas functions.
Gabriel G B –
This course is GREAT!!! I highly recommend it, lecturers and professors are soooo good. Nice course, loved it.
Darien M –
Overall a poorly designed course. If you know a little bit of stats and are hoping to expand your Python skill set, then don’t even bother wasting your time with this class. The programming instruction is extremely weak. This is basically an intro level college stats course but the instruction is completely lecture based and quite poor (much of the instruction is left to TAs). The quizzes and programming exercises are not challenging. This course gets two starts from me because the practice programming exercises are actually great, but no answers are provided so it is hard to check your understanding of these problems.
Miguel L –
More statistics than Python but very good course
Ritesh J –
Excellent course. Gives you basic understanding on visualization in Python
gregorio –
Excellent content and clear explanations.
Manoj B –
Beginner level of plotting and sampling
Yu J K –
phyton part is shit
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.
Nitin –
The course is very good as it has basic univariate and multivariate visualization techniques.
asher b –
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.
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.
Neil B –
Now feeling a bit more confident about statistics. My anxiety in statistics is fading away. Thanks a ton for this amazing course.
Wenlei Y –
The teachers are all enthusiastic and very professional. Videos are great! But I cannot give a five star since the assignments do not seem very effective, and there are some old codes that are only applicable to python 2 (rather than python 3).
Erick M A –
A really good introduction to statistics with python
Aleksandr P –
That is an amazing course for someone, who has at least a little bit of experience with Python under the belt! Definitely recommend it to anyone, who would like to refresh statistical knowledge, learn how to apply it in real life.
Praveen J –
Got pretty neat intro towards the Statistical approach towards the Data. it will a good prerequisite for data Science
Nick S –
Great course to review basic statistical concepts. The added component of python was a practical way to learn python and apply it to real situations.
DILEEP Y –
I felt pretty good by taking this course. It delves into details of statistics with good hands on python programming. Acts as good foundation course and Will be helpful further gaining knowledge on in depth courses.
mohamed h –
some of the materials in this course was not very clear but I think there will be more explanation in the upcoming courses in this specialization
Eric M M –
I particularly liked the light introduction of new concepts like methods behind confidence intervals and hypothesis tests. These were then well emphasized via numerous Jupyter Notebooks of varying levels of difficulty. Highly recommendable.
Felipe N N B –
Pretty interesting and well paced course.
Liu M –
great course
Rakesh D –
Lectures are boring and very long it should be more practical ,but yes I’ve gain certain statistical insights.
Christopher W C –
This course is really a general overview. The focus seems to be laying a foundation which I hope we build on in a more technical way in upcoming stats courses.
Gabriel A A C –
Excelent
Shukrit K –
Python exercises can be more interactive and the examples for sampling could be explained by taking a small data set for getting a realistic idea
Sumit M –
It’s a very good course to learn statistics for data analysis and data science. The instructors were great and the information they gave was just precious.
Gabriel R G –
I loved it, it’s very useful if you want learn python focused in database
Nguyen T H T –
very useful for me to start with Python
snehil –
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.
Qiyu Z –
Thank you very much.
Giuliano M –
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).
Abhijeet G –
Very good course to get started with Visualizing data with Python. Also, a very good starting point on Statistics.
Sumit M –
It’s a very good course to learn statistics for data analysis and data science. The instructors were great and the information they gave was just precious.
Kalvin K –
I really enjoyed taking this course. All the teachers did a great job in explaining the information in clear and understandable ways. The layout of the course was also organized which made the whole process easy. I would recommend.