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Understanding and Visualizing Data with Python, Université du Michigan

4.6
102 notes
33 avis

À propos de ce cours

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....

Meilleurs avis

par FG

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 JS

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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34 avis

par Md Irshad

Apr 19, 2019

This is the best course in this website in entry level

par Rashbir Singh

Apr 16, 2019

This is a must. If you want to be a data scientist then this course is like a stepping stone. Trust me its totally worth it

par José Antonio González Prieto

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 VIVEK NEGI

Apr 15, 2019

Very helpful in understanding sampling stats...using python is like a cherry on top :)

par Aayush Gadia

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 Richard Riehle

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.

par David White

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 Arijit Kumar Gayen

Apr 14, 2019

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

par Sangbaek Park

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!

par Filip Gvardijan

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.