Back to Understanding and Visualizing Data with Python

4.6

225 ratings

•

51 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....

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.

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.

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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 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 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 Md I

•Apr 19, 2019

This is the best course in this website in entry level

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 Jan T

•Aug 07, 2019

More hands on assignments would be desirable.

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 Marko P

•Jan 09, 2019

Very good course which covers both statistical concepts and python application.

By EDILSON S S O J

•Jan 01, 2019

Amazing course!

By Frank S Y R

•Jan 17, 2019

Nice!

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 Jin S

•Feb 06, 2019

Really enjoyed the different (yet all wonderful) teaching styles of the large instructor team!

By Rajesh R

•Feb 21, 2019

Very good course instructors ! Excellent balance of basics of statistics and python programming oriented towards data analysis. Thoroughly enjoyed the course material.

By cameron g

•Mar 21, 2019

Great introduction

By Varga I K

•Mar 20, 2019

It was great setup for statistical analysis in python.

By Meghana K

•Mar 26, 2019

Thank you so much for this course. I loved it! :)

By Patricia C G

•Mar 19, 2019

I loved this course! Thank you for sharing all your knowledge with us!

By Yaron K

•Jan 26, 2019

A good introduction to visualizing data using the Python seaborn library

By Moid H

•Feb 09, 2019

A good introduction on Statistics.

By Jorge A

•Feb 10, 2019

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By Saleem R

•Apr 03, 2019

Excellent course for beginners from any subject related to engineering or science and who want to do research.

By 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.

By Sangbaek P

•Apr 11, 2019

Really helpful to build a foundation for the basic Python and improve the understanding on basic but key concepts on statistics and visualizing techniques. Awesome!

By Arijit K G

•Apr 14, 2019

Provides deep and systematic insight to the tits and bits of statistics using python.

By Richard R

•Apr 15, 2019

A well paced stats refresher which covered the core material well and skillfully introduced current research. The fourth week was a solid introduction to sampling methodologies and inference. Looking forward to the next course in the sequence.

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