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Learner Reviews & Feedback for Understanding and Visualizing Data with Python by University of Michigan

4.7
stars
2,589 ratings

About the Course

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....

Top reviews

AT

May 21, 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.

VV

Aug 2, 2020

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 :)

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451 - 475 of 551 Reviews for Understanding and Visualizing Data with Python

By divyam

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Sep 1, 2019

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.

By Aidan A

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May 25, 2023

Decent course for learning some beginner statistics concepts and general information about python libraries. You should try to have some prior coding experience or be good at reading documentation before this course.

By Pankaj R

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Sep 4, 2020

Very helpful course for newcomer in data science studies. Great in clearing fundamentals for descriptive statistics, use of python to get these insights,plotting. Overall provide good learning curve.

By Matthieu C D

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Apr 19, 2020

Excellent high level introduction, would have like the assessment to be more challenging. The additional materials are just amazing for most of them. The notebooks to practice are also excellent.

By Vu M D

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Mar 7, 2022

Good content but I dont like some assigment/assessment, especially the one asking to write a memorandum, which is totally not related to "Understanding and Visualizing Data with Python"

By Samuel B

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Oct 11, 2019

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

By Rohit K

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May 15, 2022

Great course to start with Statistics. Methods of data collection and their implications are explained in good detail. Good start with coding in Python visualizing data as well.

By Pradeep S B

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May 10, 2020

The course was slightly harder and longer than I had expected, but the statistics concepts were well explained. However, the coding was not as hands-on as I would have liked.

By Ragib S A

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Jul 11, 2020

To pursue your dream career in Data Science statistics is a must. And this course is one of the fun and easy to understand statistical courses I have found on Coursera.

By Christopher B

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Oct 4, 2020

Great course with a great deal of meaningful content. Coursera failure on the Jupyter notebooks made wk 3 a bit more challenging, but overall its worth your while.

By Ricardo W E

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Jun 8, 2020

It is very complete course, you need to have a good background of Statistics, for the week for there is a lot of text but not enough practice or hand on projects!

By Christopher W C

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Jan 30, 2020

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.

By Braulio C C D A R

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May 2, 2021

Good course if you have learned the foundations of statistics already and want to review it while learning how to use Python for your statistical analysis.

By Hemant K C

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Mar 3, 2021

I would have preferred lesser time in Week 4 on Sampling. Those lessons could have been moved to the next course as they don't strictly relate to Python.

By RAKESH J

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Feb 2, 2021

Content is great. Would be a great learning experience for those who have some idea of stats and python. Definitely, not for entry-level python and stats.

By Shujie Y

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Dec 10, 2023

Generally, it is great! It would be better if examples were provided to illustrate the concepts of confidence intervals and hypothesis testing in week 4.

By Luis A S G

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Dec 5, 2020

It is a good course to start with python and statistics, the part of sampling distribution I think should improve, personally I don´t like large speeches

By Matthew

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Nov 10, 2019

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.

By Kevin D

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Mar 13, 2022

A lot of thoery. It was a challenge to grasp all of the statistical terms. Not really much of a python or practicum course. I guess that comes later.

By Rory B

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Aug 7, 2020

Generally useful, not entirely sure what the peer review assignment was, and could have done with more python assignments to help with consolidation

By Qamar R

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Sep 8, 2021

great course - the labs were very helpful. Only suggestion, more practical / relatable examples when explaining certain areas, especially Inference

By Essig Z

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Aug 17, 2020

The high-level theory was well explained. It would've been awesome if there would be more hands-on labs, and if the theory would go more in depth.

By ANUBHAV C

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Jul 15, 2020

The assignments could be made more interactive,like assigning us problems on which would have to visualize the in jupyter by us to get the answer.

By Shukrit K

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Feb 13, 2020

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

By mohamed h

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Jan 7, 2020

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