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.
Fitting Statistical Models to Data with Python
This course is part of Statistics with Python Specialization
Instructors: Brenda Gunderson
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What you'll learn
Deepen your understanding of statistical inference techniques by mastering the art of fitting statistical models to data.
Connect research questions with data analysis methods, emphasizing objectives, relationships between variables, and making predictions.
Explore various statistical modeling techniques like linear regression, logistic regression, and Bayesian inference using real data sets.
Work through hands-on case studies in Python with libraries like Statsmodels, Pandas, and Seaborn in the Jupyter Notebook environment.
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There are 4 modules in this course
We begin this third course of the Statistics with Python specialization with an overview of what is meant by “fitting statistical models to data.” In this first week, we will introduce key model fitting concepts, including the distinction between dependent and independent variables, how to account for study designs when fitting models, assessing the quality of model fit, exploring how different types of variables are handled in statistical modeling, and clearly defining the objectives of fitting models.
What's included
8 videos6 readings1 assignment2 ungraded labs
In this second week, we’ll introduce you to the basics of two types of regression: linear regression and logistic regression. You’ll get the chance to think about how to fit models, how to assess how well those models fit, and to consider how to interpret those models in the context of the data. You’ll also learn how to implement those models within Python.
What's included
5 videos4 readings3 assignments3 ungraded labs
In the third week of this course, we will be building upon the modeling concepts discussed in Week 2. Multilevel and marginal models will be our main topic of discussion, as these models enable researchers to account for dependencies in variables of interest introduced by study designs. We’ll be covering why and when we fit these alternative models, likelihood ratio tests, as well as fixed effects and their interpretations.
What's included
7 videos3 readings2 assignments4 ungraded labs
In this final week, we introduce special topics that extend the curriculum from previous weeks and courses further. We will cover a broad range of topics such as various types of dependent variables, exploring sampling methods and whether or not to use survey weights when fitting models, and in-depth case studies utilizing Bayesian techniques to derive insights from data. You’ll also have the opportunity to apply Bayesian techniques in Python.
What's included
6 videos4 readings1 assignment1 discussion prompt1 ungraded lab
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Recommended if you're interested in Probability and Statistics
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Reviewed on Jan 23, 2021
Week 3 starts to get unreasonably difficult and hard to understand. Apart from that, the course is still worthwhile to take.
Reviewed on Jul 11, 2021
Just like the other courses in the specialization, very well thought out and planned! Up to date, great professors . . . couldn't ask for more!
Reviewed on Jun 19, 2020
The course was wonderful however, sometimes I felt that a little bit more details could be provided when python code was being explained for week 2.
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