Back to Understanding and Visualizing Data with Python

4.7

stars

935 ratings

•

170 reviews

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....

May 22, 2020

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.

Apr 04, 2019

Excellent introductory course to statistics. Great use of NHANES dataset to demonstrate techniques on real dataset. I would appreciate a more demanding project at the course end.

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By Daniel R

•Feb 22, 2019

Lectures are great but there's little practice material and the quizzes are terrible. The quizzes are actually super easy but they don't cover much material from the course and sometimes introduce concepts and terms that were nowhere in the course materials.

If you want a good intro to stats without any actual testing, the lectures get pretty in-depth and the explanations are excellent! But if you're looking for lots of practice with stats in Python, you won't get much here.

By Hugo I V R

•May 09, 2019

This can be a quite helpful course for beginners. I really liked the course because it thoroughly introduced me into Seaborn (visualization library) which I was unaware of. Also, some of the practical exercises truly help you develop your pandas skills. I really enjoyed week 1-3, which truly challenged me and introduced me to new concepts with a good balance between practical and theoretical. However, week 4 felt a bit off. The contents could've been split into two weeks. The practical tasks are minimal compared to readings and videos. And the final quiz covers like 15% of all that was taught in the week. Concepts like CDF were never taught but employed at the end when talking about the empirical rule.

By Kristoffer H

•Jan 10, 2019

This course still has spelling mistakes in its quizzes, which in a programming focused course are big, and the instructors don't seem interested in fixing them. The result is you have to guess through their mistakes if code is suppose to not work in a quiz because of the error or the error is not supposed to be there in the first place and the code is valid.

By Jadson P A d S

•Jan 24, 2019

I strongly recommend this course to those who want to begin python programming applied to statistics. It launches a very sound foundation for statistical inference theory.

By David W

•Apr 14, 2019

I love the U of M courses! I get so much out of them. Thank you again for helping me to advance my knowledge of Python and deepen my understanding of statistics.

By tuncay d

•Jan 31, 2019

this course is well below my expectations. there are none real life examples or detailed visualizations, except a few simple plots. There is no step by step coding lectures. There are some youtube videos which are much better than this. Dont waste your time if your goal is to learn python, other than getting some certification.

By José A G P

•Apr 16, 2019

The course contents are good to an introduction or refreshing in statistics but the assigments are not really well prepared, and contains many unrepaired errors. This drops down the level an educational potential of this course (and the entire specialization) and converts it in a poor educational resource and a waste of time, in my opinion

By Aayush G

•Apr 15, 2019

I must say that this is a must take course for ones who are aspiring a career in Data Science. All the concepts were laid out so beautifully and it was explained very clearly with visualisations of each real-life-examples. I enrolled in this specialisation before starting my Machine Learning so that I have all the necessary fundamentals of Statistics. Brady Sir & Brendra Ma'am are simply phenomenal, the way they explain the concepts are incredible. The concepts gets etched in one's memory. The most exciting part of the course is Brenda Ma'am performing a cartwheel !! For all the ones who are enrolled, don't forget to watch it out.

By Nirmal M

•Apr 19, 2019

I strongly recommend this course to those who want to begin python programming applied to statistics. It launches a very sound foundation for statistical inference theory

By Nitish K N

•Sep 02, 2019

This is the foundation course every aspiring data scientist needs

By Bart T C

•Dec 31, 2018

This course is definitely a beginner level course in both python and stats, but it is very well done, and there is plenty of content.

By Abhishek Y

•May 17, 2020

Great course but python programming part is bit confusing, can be done on IDLE instead.

By Jan T

•Aug 07, 2019

More hands on assignments would be desirable.

By Pankaj B

•Dec 13, 2019

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.

By Minas-Marios V

•Apr 23, 2020

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.

By ILYA N

•Aug 16, 2019

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).

By Vinícius G d O

•May 12, 2019

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!

By JIANG X

•Jun 11, 2019

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.

By Geetha A

•Dec 05, 2019

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.

By PATIL P R

•Apr 05, 2020

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.

By snehil

•Mar 24, 2020

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.

By Shekhar N

•Apr 14, 2020

A very gentle introduction to data visualisation with great effort from teachers and students to make the course refreshing.

The course will not be very mathematical or coding heavy.

Most of the quizzes are fairly simple and motivate the student to gain more insight by opting for further courses in the specialization.

By Maksim M

•Feb 11, 2020

This course gives a solid understanding of core statistical principles, sampling, approach to making inferences, plus some experience with data manipulation using Pandas and data visualization using Matplotlib and Seaborn libraries, as well as some experience with the Numpy library (all in Python)

By Giuliano M

•Mar 26, 2020

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).

By Christine B

•Jul 19, 2019

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.

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