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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/fitting-statistical-models-data-python
课程评论：没有评论
课程名称：使用Python将统计模型拟合到数据 概述：本课程将扩展对统计推断技术的探索，重点关注将统计模型拟合到数据的科学与艺术。我们将基于统计推断课程（课程2）中的概念，强调将研究问题与数据分析方法相连接的重要性。同时，我们将关注多种建模目标，包括对变量之间关系的推断和对未来观察结果的预测。 本课程将介绍和探讨多种统计建模技术，包括线性回归、逻辑回归、广义线性模型、层次和混合效应（或多层）模型，以及贝叶斯推断技术。所有技术都将通过各种真实数据集进行说明，并强调根据数据的研究设计选择不同的建模方法（回顾课程1：使用Python理解和可视化数据）。 在这些以实验室为基础的课程中，学习者将通过关注特定案例研究的教程来巩固每周的统计概念，内容将深入探讨Python库，包括Statsmodels、Pandas和Seaborn。本课程利用Coursera中的Jupyter Notebook环境。 课程大纲： **第一周 - 统计建模的概述与考虑** 本周，我们将介绍“将统计模型拟合到数据”所涉及的关键模型拟合概念，包括依赖变量和独立变量之间的区别、在拟合模型时如何考虑研究设计、模型拟合质量的评估、不同类型变量在统计建模中的处理方式，以及模型拟合目标的明确定义。 **第二周 - 将模型拟合到独立数据** 在第二周，我们将介绍线性回归和逻辑回归这两种回归类型的基础知识。学习者将思考如何拟合模型、如何评估模型的拟合程度，以及如何在数据的背景下解释这些模型。此外，学习者还将学习如何在Python中实现这些模型。 **第三周 - 将模型拟合到依赖数据** 本周将基于第二周讨论的建模概念，主要讨论多层次和边际模型。这些模型使研究者能够考虑研究设计引入的变量间的依赖关系。我们将讨论何时以及为何拟合这些替代模型，似然比检验，以及固定效应及其解释。 **第四周 - 特殊主题** 在最后一周，我们将介绍扩展前几周和课程内容的特殊主题。将涵盖多种依赖变量类型、探讨抽样方法及在拟合模型时是否使用调查权重的考虑，以及利用贝叶斯技术从数据中获得见解的深入案例研究。学习者还将有机会在Python中应用贝叶斯技术。
Name:WEEK 1 - OVERVIEW & CONSIDERATIONS FOR STATISTICAL MODELING
Description: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.
Name:WEEK 2 - FITTING MODELS TO INDEPENDENT DATA
Description: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.
Name:WEEK 3 - FITTING MODELS TO DEPENDENT DATA
Description: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.
Name:WEEK 4: Special Topics
Description: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.
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.
