Learn statistics with python

Специализация Statistics with Python

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Специализация — серия из нескольких курсов (3)

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Чему вы научитесь

Create and interpret data visualizations using the Python programming language and associated packages & libraries

Apply statistical modeling techniques to data (ie. linear and logistic regression, linear models, multilevel models, Bayesian inference techniques)

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Доступен на таких языках: Английский

Субтитры: Английский, Арабский, Французский, Португальский (Европа), Китайский (упрощенное письмо), Итальянский, Португальский (бразильский), Вьетнамский, Корейский, Немецкий, Русский, Тайский, Индонезийский , Турецкий, Испанский

Специализация — серия из нескольких курсов (3)

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Специализация — серия из 3 курсов

This specialization is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will learn where data come from, what types of data can be collected, study data design, data management, and how to effectively carry out data exploration and visualization. They will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. Finally, they will learn the importance of and be able to connect research questions to the statistical and data analysis methods taught to them.

Проект прикладного обучения

The courses in this specialization feature a variety of assignments that will test the learner’s knowledge and ability to apply content through concept checks, written analyses, and Python programming assessments. These assignments are conducted through quizzes, submission of written assignments, and the Jupyter Notebook environment.

Understanding and Visualizing Data with Python

Чему вы научитесь

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Inferential Statistical Analysis with Python

Чему вы научитесь

Determine assumptions needed to calculate confidence intervals for their respective population parameters.

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Fitting Statistical Models to Data with Python

Чему вы научитесь

In this course, we will expand our exploration of statistical inference techniques by focusing on the science and art of fitting statistical models to data. We will build on the concepts presented in the Statistical Inference course (Course 2) to emphasize the importance of connecting research questions to our data analysis methods. We will also focus on various modeling objectives, including making inference about relationships between variables and generating predictions for future observations.

This course will introduce and explore various statistical modeling techniques, including linear regression, logistic regression, generalized linear models, hierarchical and mixed effects (or multilevel) models, and Bayesian inference techniques. All techniques will be illustrated using a variety of real data sets, and the course will emphasize different modeling approaches for different types of data sets, depending on the study design underlying the data (referring back to Course 1, Understanding and Visualizing Data with Python). During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera.

Источник

Learning Statistics with Python#

I am a huge fan of Danielle Navarro’s book Learning Statistics with R. It is the most accessible statistics book I know of. My students love it. I love it. It’s free, and it comes in not only R, but also JASP and JAMOVI flavors. The only problem is, I need to teach intro stats using Python, not R. What to do? Translate the book, obviously!

Since Danielle has been so kind as to open-source the book, I have gone to work translating the R bits to Python, and am learning a lot along the way. To start with, I’m concentrating on translating the code, and putting off editing the textual references to R and R-specific functions for later. Having started with just the code, I have now realized that a better approach is to go through the text line-by-line, and do the job properly the first time. It’s a bit slower this way, but ultimately better, I think.

It’s hard to say how far I’ll get, but for now I’m having fun, and am excited that the students in my course won’t have to forego this fantastic book, just because they need to do their stats in Python.

Update: having by now gotten as far as figuring out how to use Python to overlay the probability density for an F-distribution on top of a distribution created by taking the ratio of scaled random samples from two chi-square distributions, I think I’m committed to seeing this thing through.

Thanks very much to Danielle for the encouragement, and to Emily Kothe for the bookdown adaptation of LSR, which has been extremely helpful in creating this Python version.

Learning Statistics with Python by Danielle Navarro and Ethan Weed is licensed under CC BY-SA 4.0

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