Retour à Fitting Statistical Models to Data with Python

3.9

22 notes

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7 avis

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....

par AF

•Mar 12, 2019

The course is actually pretty good, however the mix between basic subjects (like univariate linear regression) and relatively advanced topics (marginal models) may discourage some students.

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7 avis

par Varga Imre Károly

•Apr 14, 2019

Great review of machine learning used in statistics finished up with some overview on bayesian math.

Enjoyed very much and learnt even more.

par Alvaro Fuentes

•Mar 12, 2019

The course is actually pretty good, however the mix between basic subjects (like univariate linear regression) and relatively advanced topics (marginal models) may discourage some students.

par Tobias Roeschl

•Mar 10, 2019

The content itself is great but some notebooks were a bit unready. Otherwise great course!

par David Zhao

•Feb 10, 2019

Great lecture content, poor quiz design. Hard to apply any of the concepts that you learn.

par Harish Shetty

•Jan 27, 2019

Content of course was good. Some issue with quiz.

par Yaron Klein

•Jan 26, 2019

I had never given much thought to multilevel models and their implications (for example how clustering or the interviewer effected the results). So the course was definitely interesting. However the Python notebooks that are part of the course don't give enough detail to be able to apply the theoretic material to other models.

par Kristoffer Hess

•Jan 13, 2019

If you don't already understand the topic don't bother with this course, the lectures are 95% hand waving and showing formulas they don't explain how to make sense of and then the quizzes are answering questions on what they didn't bother to explain.

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