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Understanding and Visualizing Data with Python

Statistics with Python,

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.

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Rating 4.4 based on 103 ratings
Length 5 weeks
Effort 4 weeks of study, 4-6 hours/week
Starts Dec 7 (14 weeks ago)
Cost $49
From University of Michigan via Coursera
Instructors Brenda Gunderson, Brady T. West, Kerby Shedden
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Data Analysis Probability And Statistics

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What people are saying

very good course

Very good course instructors !

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

Overall a very good course and I enjoyed learning through this.

Beginner level of plotting and sampling phyton part is shit More statistics than Python but very good course Excellent course.

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very clear

All the concepts were laid out so beautifully and it was explained very clearly with visualisations of each real-life-examples.

Instructors and presentations are excellent, very clear.

It was an excellent course, the explanations were very clear and the examples given really helped to ilustrate the concepts.

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real life

there are none real life examples or detailed visualizations, except a few simple plots.

Definitely recommend it to anyone, who would like to refresh statistical knowledge, learn how to apply it in real life.

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data analysis

Excellent balance of basics of statistics and python programming oriented towards data analysis.

The content is very comprehensive, provides an introduction about all the useful things necessary to do statistical data analysis with Python.

for beginners

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

Great class for beginners.

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basic statistical

Good introduction to basic statistical methods with an emphasis on working with surveys, and a good introduction to basic statistical techniques with core Python, numpy, matplotlib, seaborn and statsmodels.

Great course to review basic statistical concepts.

experience with

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 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) Excelent This course is really a general overview.

That is an amazing course for someone, who has at least a little bit of experience with Python under the belt!

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Rating 4.4 based on 103 ratings
Length 5 weeks
Effort 4 weeks of study, 4-6 hours/week
Starts Dec 7 (14 weeks ago)
Cost $49
From University of Michigan via Coursera
Instructors Brenda Gunderson, Brady T. West, Kerby Shedden
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Data Analysis Probability And Statistics

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