Understanding and Visualizing Data with Python

所在平台: Coursera

课程主页: https://www.coursera.org/learn/understanding-visualization-data

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课程简介

课程名称：使用Python理解和可视化数据 课程概述： 在本课程中，学习者将被介绍统计学领域，包括数据来源、研究设计、数据管理以及数据的探索和可视化。学习者将识别不同类型的数据，并学习如何可视化、分析和解释单变量和多变量数据的摘要。此外，学习者还将学习概率抽样与非概率抽样之间的差异，以及如何通过概率抽样对更大的人群做出推断。在每周的末尾，学习者将使用Python应用所学的统计概念。在基于实验室的课程中，学习者将探索Python作为工具的不同用途，包括Numpy、Pandas、Statsmodels、Matplotlib和Seaborn库。课程中提供了教程视频，指导学习者创建可视化和进行数据管理，所有内容均在Python环境中完成。该课程利用Coursera中的Jupyter Notebook环境进行教学。 课程大纲： 第1周 - 数据简介 在本周，学习者将回顾课程大纲，了解未来几周要掌握的各种概念和目标。将介绍统计学领域，探讨其多种视角，识别日常生活中存在的各种数据类型，并深入了解Python的基本功能及Jupyter Notebook的使用。 第2周 - 单变量数据 在本周，课程将关注于一变量（单变量数据）的图形和数值解释。学习者将创建并分析直方图、箱形图及数据的数值摘要，为量化数据提供分析基础，以及用于分类数据的条形图和饼图。课程将探讨数值摘要的关键解释，如均值、四分位间距和标准差，并在周末进行一次关于数值摘要及其解释的评估。 第3周 - 多变量数据 在本周，课程将介绍研究问题所需的多个变量的关键概念。将通过数值和视觉的方式考察不同变量之间的相互作用，强调如果不正确考虑相互作用，摘要可能会出现误导的情况，并讨论定量变量与分类变量的差异。本周的作业将包括一篇写作任务，并审查同伴的作业。 第4周 - 总体与样本 本周学习者将深入思考数据的来源。高质量的数据统计分析总是会结合数据生成过程中使用的信息或数据收集设计的特征。学习者将接触到从更大总体中抽样的关键概念，包括概率抽样和非概率抽样，以及如何通过良好设计的样本对更大总体作出推断。将学习到抽样分布的概念，以及该分布方差估计在对总体做出陈述中的重要角色。最后，学习者还将了解阅读数据集文档的重要性，这是查看数据的重要步骤。

课程大纲

Name:WEEK 1 - INTRODUCTION TO DATA

Description:In the first week of the course, we will review a course outline and discover the various concepts and objectives to be mastered in the weeks to come. You will get an introduction to the field of statistics and explore a variety of perspectives the field has to offer. We will identify numerous types of data that exist and observe where they can be found in everyday life. You will delve into basic Python functionality, along with an introduction to Jupyter Notebook. 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 - UNIVARIATE DATA

Description:In the second week of this course, we will be looking at graphical and numerical interpretations for one variable (univariate data). In particular, we will be creating and analyzing histograms, box plots, and numerical summaries of our data in order to give a basis of analysis for quantitative data and bar charts and pie charts for categorical data. A few key interpretations will be made about our numerical summaries such as mean, IQR, and standard deviation. An assessment is included at the end of the week concerning numerical summaries and interpretations of these summaries.

Name:WEEK 3 - MULTIVARIATE DATA

Description:In the third week of this course on looking at data, we’ll introduce key ideas for examining research questions that require looking at more than one variable. In particular, we will consider both numerically and visually how different variables interact, how summaries can appear deceiving if you don’t properly account for interactions, and differences between quantitative and categorical variables. This week’s assignment will consist of a writing assignment along with reviewing those of your peers.

Name:WEEK 4 - POPULATIONS AND SAMPLES

Description:In this week, you’ll spend more time thinking about where data come from. The highest-quality statistical analyses of data will always incorporate information about the process used to generate the data, or features of the data collection design. You’ll be exposed to important concepts related to sampling from larger populations, including probability and non-probability sampling, and how we can make inferences about larger populations based on well-designed samples. You’ll also learn about the concept of a sampling distribution, and how estimation of the variance of that distribution plays a critical role in making statements about populations. Finally, you’ll learn about the importance of reading the documentation for a given data set; a key step in looking at data is also looking at the available documentation for that data set, which describes how the data were generated.

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