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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/inferential-statistical-analysis-python
课程评论：没有评论
课程摘要：推断统计分析与Python 本课程将探讨利用数据进行估计和评估理论的基本原则。我们将分析分类数据和定量数据，从单一人群技术开始，然后扩展到处理两个群体的比较。学习内容包括构建置信区间以及使用样本数据评估关于参数值的理论是否与数据相符。课程将重点强调如何正确解读推断结果。 每周结束时，学习者将在课程环境中使用Python应用所学内容。实验课将围绕特定案例研究进行，帮助巩固统计概念，并深入 Python 库，包括 Statsmodels、Pandas 和 Seaborn。该课程利用Jupyter Notebook环境。 课程大纲： 第1周 - 概述与推断程序：回顾课程大纲，介绍推断方法及相关的研究问题，并建立基于数据的决策框架，以及评估可能出现的错误。 第2周 - 置信区间：学习通过置信区间估计总体参数，包括五种不同的总体参数及其计算所需的假设，并在Python中创建置信区间。 第3周 - 假设检验：学习如何使用前一周涵盖的五种分析方法测试各种假设，讨论假设检验中的重要因素与假设，并学会解读结果，了解不同程序的选择。 第4周 - 学习者应用：通过案例研究和实例展示之前讨论的推断程序的应用，学习者将提出与已讨论的研究设计和数据集相关的良好研究问题，并通过置信区间估计和正式的假设检验回应这些问题。
Name:WEEK 1 - OVERVIEW & INFERENCE PROCEDURES
Description:In this first week, we’ll review the course syllabus and discover the various concepts and objectives to be mastered in weeks to come. You’ll be introduced to inference methods and some of the research questions we’ll discuss in the course, as well as an overall framework for making decisions using data, considerations for how you make those decisions, and evaluating errors that you may have made. On the Python side, we’ll review some high level concepts from the first course in this series, Python’s statistics landscape, and walk through intermediate level Python concepts. All of the course information on grading, prerequisites, and expectations are on the course syllabus and you can find more information on our Course Resources page.
Name:WEEK 2 - CONFIDENCE INTERVALS
Description:In this second week, we will learn about estimating population parameters via confidence intervals. You will be introduced to five different types of population parameters, assumptions needed to calculate a confidence interval for each of these five parameters, and how to calculate confidence intervals. Quizzes will appear throughout the week to test your understanding. In addition, you’ll learn how to create confidence intervals in Python.
Name:WEEK 3 - HYPOTHESIS TESTING
Description:In week three, we’ll learn how to test various hypotheses - using the five different analysis methods covered in the previous week. We’ll discuss the importance of various factors and assumptions with hypothesis testing and learn to interpret our results. We will also review how to distinguish which procedure is appropriate for the research question at hand. Quizzes and a peer assessment will appear throughout the week to test your understanding.
Name:WEEK 4 - LEARNER APPLICATION
Description:In the final week of this course, we will walk through several examples and case studies that illustrate applications of the inferential procedures discussed in prior weeks. Learners will see examples of well-formulated research questions related to the study designs and data sets that we have discussed thus far, and via both confidence interval estimation and formal hypothesis testing, we will formulate inferential responses to those questions.
In this course, we will explore basic principles behind using data for estimation and for assessing theories. We will analyze both categorical data and quantitative data, starting with one population techniques and expanding to handle comparisons of two populations. We will learn how to construct confidence intervals. We will also use sample data to assess whether or not a theory about the value of a parameter is consistent with the data. A major focus will be on interpreting inferential results appropriately. At the end of each week, learners will apply what they’ve learned using Python within the course environment. 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.
