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所在平台: Coursera专项课程 |
课程主页: https://www.coursera.org/specializations/tidyverse-data-science-r
课程评论：没有评论
课程名称：R语言中的Tidyverse技能与数据科学 课程概述： 本课程旨在教授有一定R语言基础的数据科学家如何使用Tidyverse包进行数据科学工作。通过五个课程模块，学习者将学习如何导入、整理、可视化和建模数据。Tidyverse提供了一种简单而强大的数据科学方式，从基本分析到大规模数据处理均可应用。本课程覆盖数据科学项目的整个生命周期，并为每个阶段提供具体的工具。 学习内容包括： - 组织数据科学项目 - 从常见的电子表格、数据库和网络格式导入数据 - 整理和处理杂乱数据，构建整洁的数据集 - 构建高质量的数据图形 技能获得： - 数据科学 - 预测建模 - R编程 - 数据可视化（DataViz） - 数据分析软件 - 数据管理 - 整理数据 应用学习项目： 每个课程结束时，学习者将参与一个项目。从头开始组织数据科学项目，导入并处理多种格式的数据，将非整洁数据整理为整洁数据，使用ggplot2可视化数据，并构建机器学习预测模型。 课程信息： - 完全在线课程，学习者可以根据自己的时间安排学习。 - 灵活的课程进度，学生可以自行设定和维持灵活的截止日期。 - 预计完成时间约需7个月，每周建议学习3小时。 - 课程以初学者为级别，需对R编程有一定了解。 - 完成课程后可获得可分享的证书。 如需了解更多信息，请访问课程链接：[Tidyverse技能与数据科学](https://www.coursera.org/learn/tidyverse)。
Course Link: https://www.coursera.org/learn/tidyverse
Name:Introduction to the Tidyverse
Description:Offered by Johns Hopkins University. This course introduces a powerful set of data science tools known as the Tidyverse. The Tidyverse has ... Enroll for free.
Course Link: https://www.coursera.org/learn/tidyverse-importing-data
Name:Importing Data in the Tidyverse
Description:Offered by Johns Hopkins University. Getting data into your statistical analysis system can be one of the most challenging parts of any data ... Enroll for free.
Course Link: https://www.coursera.org/learn/tidyverse-data-wrangling
Name:Wrangling Data in the Tidyverse
Description:Offered by Johns Hopkins University. Data never arrive in the condition that you need them in order to do effective data analysis. Data need ... Enroll for free.
Course Link: https://www.coursera.org/learn/tidyverse-visualize-data
Name:Visualizing Data in the Tidyverse
Description:Offered by Johns Hopkins University. Data visualization is a critical part of any data science project. Once data have been imported and ... Enroll for free.
Course Link: https://www.coursera.org/learn/tidyverse-modelling-data
Name:Modeling Data in the Tidyverse
Description:Offered by Johns Hopkins University. Developing insights about your organization, business, or research project depends on effective ... Enroll for free.
What you will learn
Organize a data science project
Import data from common spreadsheet, database, and web-based formats
Wrangle and manipulate messy data and build tidy datasets
Build presentation quality data graphics
Skills you will gain
Data Science
Predictive Modelling
R Programming
Data Visualization (DataViz)
Data Analysis Software
Data Management
tidying data
About this Specialization
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This Specialization is intended for data scientists with some familiarity with the R programming language who are seeking to do data science using the Tidyverse family of packages. Through 5 courses, you will cover importing, wrangling, visualizing, and modeling data using the powerful Tidyverse framework. The Tidyverse packages provide a simple but powerful approach to data science which scales from the most basic analyses to massive data deployments. This course covers the entire life cycle of a data science project and presents specific tidy tools for each stage.
Applied Learning Project
Learners will engage in a project at the end of each course. Through each project, learners will build an organize a data science project from scratch, import and manipulate data from a variety of data formats, wrangle non-tidy data into tidy data, visualize data with ggplot2, and build machine learning prediction models.
Shareable Certificate
Shareable Certificate
Earn a Certificate upon completion
100% online courses
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Flexible Schedule
Set and maintain flexible deadlines.
Beginner Level
Beginner Level
Familiarity with the R programming language.
Hours to complete
Approximately 7 months to complete
Suggested pace of 3 hours/week
Available languages
English
Subtitles: English
Shareable Certificate
Shareable Certificate
Earn a Certificate upon completion
100% online courses
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Flexible Schedule
Set and maintain flexible deadlines.
Beginner Level
Beginner Level
Familiarity with the R programming language.
Hours to complete
Approximately 7 months to complete
Suggested pace of 3 hours/week
Available languages
English
Subtitles: English
