Retour à Understanding and Visualizing Data with Python

4.7

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1,491 évaluations

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299 avis

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....

AT

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.

VV

Aug 03, 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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par 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.

par 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.

par 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.

par 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.

par Mahika R

•Jun 03, 2020

Never have I come across a course half as interactive as this and it was a much needed confidence booster for a beginner like me. I look forward to completing the specialization : )

par Filip G

•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.

par 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.

par 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

par 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.

par Nitish K N

•Sep 02, 2019

This is the foundation course every aspiring data scientist needs

par 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.

par Jan T

•Aug 07, 2019

More hands on assignments would be desirable.

par 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.

par 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

par Abhishek Y

•May 17, 2020

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

par Andrew T

•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.

par Vishnu S V

•Aug 03, 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 :)

par Darien M

•Nov 26, 2019

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.

par S M S S

•Jun 28, 2020

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.

par 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.

par 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.

par Antonello P

•Jul 22, 2020

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.

par 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).

par Tarit G

•Jul 02, 2020

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.

par Vinicius 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!

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