The virtual course "Fitting statistical models to data with Python - Virtual Course - Coursera" is a course with different contents and offers video classes from . Explore its essential features, and click the orange button for detailed information on the Coursera e-Learning platform.
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 Statistical Inference (Course 2) to emphasize the importance of connecting research questions with our data analysis methods. We will also focus on various modeling goals, including inferring 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 to Course 1, Understanding and Visualizing Data with Python). During these lab-based sessions, students will work through tutorials that focus on specific case studies to help solidify the week's statistical concepts, which will include more deep dives into Python libraries, including Statsmodels, Pandas, and Seaborn. . This course uses the Jupyter Notebook environment within Coursera. Students will work through tutorials focused on specific case studies to help solidify the week's statistical concepts, which will include more deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course uses the Jupyter Notebook environment within Coursera. Students will work through tutorials focused on specific case studies to help solidify the week's statistical concepts, which will include more deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course uses the Jupyter Notebook environment within Coursera.
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