Mastering Software Development in R Specialization

开始时间: 06/21/2022 持续时间: Approximately 6 months to complete Suggested pace of 4 hours/week

所在平台: Coursera专项课程 课程类别: 计算机科学 大学或机构: CourseraNew

课程主页: https://www.coursera.org/specializations/r

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课程详情

R is a programming language and a free software environment for statistical computing and graphics, widely used by data analysts, data scientists and statisticians. This Specialization covers R software development for building data science tools. As the field of data science evolves, it has become clear that software development skills are essential for producing and scaling useful data science results and products. This Specialization will give you rigorous training in the R language, including the skills for handling complex data, building R packages, and developing custom data visualizations. You’ll be introduced to indispensable R libraries for data manipulation, like tidyverse, and data visualization and graphics, like ggplot2. You’ll learn modern software development practices to build tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers. This Specialization is designed to serve both data analysts, who may want to gain more familiarity with hands-on, fundamental software skills for their everyday work, as well as data mining experts and data scientists, who may want to use R to scale their developing and programming skills, and further their careers as data science experts.

掌握R中的软件开发专业知识：R是一种用于统计计算和图形的编程语言和免费软件环境，被数据分析师，数据科学家和统计人员广泛使用。本专业涵盖用于构建数据科学工具的R软件开发。随着数据科学领域的发展，很明显，软件开发技能对于产生和扩展有用的数据科学结果和产品至关重要。 本专业知识将为您提供R语言的严格培训，包括处理复杂数据，构建R包和开发自定义数据可视化的技能。将向您介绍必不可少的R库，用于数据处理（如tidyverse）以及数据可视化和图形（如ggplot2）。您将学习现代软件开发实践，以构建高度可重用，模块化的工具，并适合在基于团队的环境或开发人员社区中使用。 该专业旨在服务于可能希望在日常工作中更加熟悉动手基础软件技能的数据分析师，以及可能希望使用R来扩展其开发能力的数据挖掘专家和数据科学家。和编程技能，并进一步发展他们作为数据科学专家的职业。

课程大纲

Course: 1

Course Link: https://www.coursera.org/learn/r-programming-environment?specialization=r

Title:The R Programming Environment

Description:This course provides a rigorous introduction to the R programming language, with a particular focus on using R for software development in a data science setting. Whether you are part of a data science team or working individually within a community of developers, this course will give you the knowledge of R needed to make useful contributions in those settings. As the first course in the Specialization, the course provides the essential foundation of R needed for the following courses. We cover basic R concepts and language fundamentals, key concepts like tidy data and related "tidyverse" tools, processing and manipulation of complex and large datasets, handling textual data, and basic data science tasks. Upon completing this course, learners will have fluency at the R console and will be able to create tidy datasets from a wide range of possible data sources.

Course: 2

Course Link: https://www.coursera.org/learn/advanced-r?specialization=r

Title:Advanced R Programming

Description:This course covers advanced topics in R programming that are necessary for developing powerful, robust, and reusable data science tools. Topics covered include functional programming in R, robust error handling, object oriented programming, profiling and benchmarking, debugging, and proper design of functions. Upon completing this course you will be able to identify and abstract common data analysis tasks and to encapsulate them in user-facing functions. Because every data science environment encounters unique data challenges, there is always a need to develop custom software specific to your organization’s mission. You will also be able to define new data types in R and to develop a universe of functionality specific to those data types to enable cleaner execution of data science tasks and stronger reusability within a team.

Course: 3

Course Link: https://www.coursera.org/learn/r-packages?specialization=r

Title:Building R Packages

Description:Writing good code for data science is only part of the job. In order to maximizing the usefulness and reusability of data science software, code must be organized and distributed in a manner that adheres to community-based standards and provides a good user experience. This course covers the primary means by which R software is organized and distributed to others. We cover R package development, writing good documentation and vignettes, writing robust software, cross-platform development, continuous integration tools, and distributing packages via CRAN and GitHub. Learners will produce R packages that satisfy the criteria for submission to CRAN.

Course: 4

Course Link: https://www.coursera.org/learn/r-data-visualization?specialization=r

Title:Building Data Visualization Tools

Description:The data science revolution has produced reams of new data from a wide variety of new sources. These new datasets are being used to answer new questions in way never before conceived. Visualization remains one of the most powerful ways draw conclusions from data, but the influx of new data types requires the development of new visualization techniques and building blocks. This course provides you with the skills for creating those new visualization building blocks. We focus on the ggplot2 framework and describe how to use and extend the system to suit the specific needs of your organization or team. Upon completing this course, learners will be able to build the tools needed to visualize a wide variety of data types and will have the fundamentals needed to address new data types as they come about.

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