|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-visualization-and-descriptive-statistics-with-python-3/
课程评论：没有评论
课程名称：Python 3的数据可视化与描述性统计 课程概述：该课程旨在教授分析师、数据科学感兴趣的学生、统计学家和数据科学家如何通过创建专业的图表和使用Python 3中的数值描述性统计技术来分析现实世界的数据。您将学习如何使用Python 3中的图表库来分析与腐败感知、婴儿死亡率、预期寿命、埃博拉病毒、酒精和肝脏疾病数据、全球识字率、美国暴力犯罪、足球世界杯、移民死亡等相关的真实世界数据。此外，您还将学习如何有效使用Python 3中的各种统计库，如numpy、scipy.stats、pandas和statistics，来创建进行现实世界数据分析所必需的所有描述性统计摘要。 在本课程中，您将理解每个库如何处理缺失值，并学习如何在数据中存在缺失值时正确计算各种统计数据。课程将指导您掌握使用Python 3分析现实世界数据所需的所有知识。您将能够使用seaborn、matplotlib或pandas库适当地创建可视化图表。 使用各种世界数据集，我们将利用这些工具在pandas、matplotlib和seaborn中分析每个数据集，包括： - 相关图 - 比较组分布的箱线图 - 时间序列和线图 - 并排比较的饼图 - 面积图 - 堆积柱状图 - 连续数据的直方图 - 柱状图 - 回归图 - 数据中心的统计量 - 数据扩散的统计量 - 数据相对位置的统计量 - 计算相关系数 - 数据的排名和相对位置 - 确定数据集中的异常值 - 将数据分箱为三分之一、四分之一、五分之一、十分之一等 本课程使用Anaconda Jupyter Notebook进行教学，以实现可重复的研究目标，利用markdown清晰文档代码，使其易于理解和共享。 学生评价： “我很喜欢你在课程各单元中分享的小贴士。这是一门很好的课程。” “我是一名数据科学家，有多年使用Python/大数据的经验。课程内容为有兴趣学习Python中数据可视化和描述性统计分析的学生提供了丰富的资源。我会推荐这门课程给任何希望进入这一领域的人。”
This course is designed to teach analysts, students interested in data science, statisticians, data scientists how to analyze real world data by creating professional looking charts and using numerical descriptive statistics techniques in Python 3. You will learn how to use charting libraries in Python 3 to analyze real-world data about corruption perception, infant mortality rate, life expectancy, the Ebola virus, alcohol and liver disease data, World literacy rate, violent crime in the USA, soccer World Cup,migrants deaths, etc. You will also learn how to effectively use the various statistical libraries in Python 3 such as numpy, scipy.stats, pandas and statistics to create all descriptive statistics summaries that are necessary for analyzing real world data.In this course, you will understand how each library handles missing values and you will learn how to compute the various statistics properly when missing values are present in the data.The course will teach you all that you need to know in order to analyze hands on real world data using Python 3. You will be able to appropriately create the visualizations using seaborn, matplotlib or pandas libraries in Python 3. Using a wide variety of world datasets, we will analyze each one of the data using these tools within pandas, matplotlib and seaborn:Correlation plotsBox-plots for comparing groups distributionsTime series and lines plotsSide by side comparative pie chartsAreas charts Stacked bar charts Histograms of continuous dataBar charts Regression plotsStatistical measures of the center of the dataStatistical measures of spread in the dataStatistical measures of relative standing in the dataCalculating Correlation coefficientsRanking and relative standing in dataDetermining outliers in datasetsBinning data in terciles, quartiles, quintiles, deciles, etc.The course is taught using Anaconda Jupyter notebook, in order to achieve a reproducible research goal, where we use markdowns to clearly document the codes in order to make them easily understandable and shareable.This is what some students are saying:"I really like the tips that you share in every unit in the course sections. This was a well delivered course.""I am a Data Scientist with many years using Python /Big Data. The content of this course provides a rich resource to students interested in learning hands on data visualization in Python and the analysis of descriptive statistics. I will recommend this course anyone trying to come into this domain."
