R ProgrammingData Visualization (DataViz)Ggplot2tidyverseObject-Oriented Programming (OOP)Data ManipulationRegular Expression (REGEX)RstudioLogic ProgrammingFunctional ProgrammingProgramming ToolGithub
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Чему вы научитесь
Build R packages
Custom data visualization and graphics
Data manipulation and wrangling
Produce and scale data science products
Приобретаемые навыки
Специализация: общие сведения
Недавно просмотрено: 13,234
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.
Проект прикладного обучения
In each of the courses, learners will deploy their newly acquired advanced R language skills to manipulate complex datasets, write powerful functions, create a new R package, and develop new visualization tools for building custom data graphics. These projects will result in a portfolio of R code that can be reused and built upon for deployment in the real world.
Карьерные результаты учащихся
46%
Начали новую карьеру, пройдя этот продукт (Специализация).
13%
Стали больше зарабатывать или получили повышение.
Карьерные результаты учащихся
46%
Начали новую карьеру, пройдя этот продукт (Специализация).
13%
Стали больше зарабатывать или получили повышение.
Только онлайн-курсы
Начните сейчас и учитесь по собственному графику.
Гибкий график
Установите гибкие сроки сдачи заданий.
Начальный уровень
Опыт не требуется.
Прибл. 3 месяца на выполнение
Около 7 ч/неделю
Английский
Субтитры: Английский, Французский, Русский, Китайский (упрощенное письмо), Арабский, Вьетнамский, Немецкий, Грузинский, Эстонский, Тайский, Японский, Непальский
О специализации
Пройти курсы
Специализация Coursera — это серия курсов, помогающих в совершенстве овладеть определенным навыком. Можно сразу записаться на специализацию или просмотреть курсы, из которых она состоит и выбрать тот, с которого вы хотите начать. Подписываясь на курс, который входит в специализацию, вы автоматически подписываетесь на всю специализацию. Можно завершить всего один курс, а потом сделать паузу в обучении или в любой момент отменить подписку. Отслеживайте свои курсы и прогресс на панели управления учащегося.
Практический проект
В каждой специализации есть практический проект, который нужно успешно выполнить, чтобы завершить специализацию и получить сертификат. Если для практического проекта в специализации предусмотрен отдельный курс, прежде чем начать его, необходимо завершить все остальные курсы.
Получите сертификат
Когда вы пройдете все курсы и завершите практический проект, вы получите сертификат, которым можно поделиться с потенциальными работодателями и коллегами.
Специализация включает несколько курсов: 5
The R Programming Environment
Оценки: 935
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.
Advanced R Programming
Оценки: 451
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.
Building R Packages
Оценки: 185
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.
Building Data Visualization Tools
Оценки: 134
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.
от партнера
Университет Джонса Хопкинса
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Рецензии
4.2
Лучшие отзывы о курсе MASTERING SOFTWARE DEVELOPMENT IN R
от партнера KMJun 3, 2019
Fantastic course... Unfortunately, not too many people registered, it's tough to get your assignments graded. The program is the great continuation to the 10 course R data science specialization...
от партнера EOAug 25, 2017
Great introduction to the world of R !!! Also a great refresher course after taking the Data Science Specialization.
от партнера BJOct 14, 2017
Some good functional R training but really missed the videos like in the Data Science specialization
от партнера MVDec 25, 2018
Very Very Rigorous Course for a beginner on R language and because of its nature, after completing just one course, I feel like I have gained a lot of knowledge and also familiarity with R language.
от партнера KMJun 3, 2019
Fantastic course... Unfortunately, not too many people registered, it's tough to get your assignments graded. The program is the great continuation to the 10 course R data science specialization...
от партнера KVJun 17, 2019
A very good course to read and get the valuable content of R language. This is for the students who want to learn and practice the basic and some intermediate concepts of data manipulation.
от партнера NSJun 6, 2018
Good to learn the possibilities in the R environment. In the end you learn most by applying it to your own projects (with a lot of help in available documentation or via internet sea).
от партнера AGNov 16, 2018
Great course! You'll learn a lot about the graphic capabilities of R. However, I think there are some things that need to be explained before one goes on to complete the final project.
Часто задаваемые вопросы
Какие правила возврата средств?
Можно ли зарегистрироваться только на один курс?
Да! Чтобы начать, нажмите карточку интересующего вас курса и зарегистрируйтесь. Зарегистрировавшись, вы можете пройти курс и получить сертификат, ссылкой на который можно делиться с другими людьми. Просто ознакомиться с содержанием курса можно бесплатно. При подписке на курс, входящий в специализацию, вы автоматически подписываетесь на всю специализацию. Ход учебы можно отслеживать в панели управления учащегося.
Можно ли получить финансовую помощь?
Могу ли я пройти курс бесплатно?
Действительно ли это полностью дистанционный курс? Нужно ли мне посещать какие-либо занятия лично?
Это полностью дистанционный курс, потому вам не нужно ничего посещать. Все лекции, материалы для самостоятельного изучения и задания доступны всегда и везде по Интернету и с мобильных устройств.
Сколько времени занимает получение специализации?
Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 3-6 months.
What background knowledge is necessary?
Some programming experience (in any language) is recommended. We also suggest a working knowledge of mathematics up to algebra (neither calculus or linear algebra are required).
Do I need to take the courses in a specific order?
We strongly recommend that you take the courses in order.
Получу ли я зачеты в университете за прохождение специализации?
Coursera courses and certificates don't carry university credit, though some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.
What will I be able to do upon completing the Specialization?
You will be able to use R to create new data science tools as part of a team or a community of developers. You will be able to build R packages, develop custom visualizations, and apply modern software development tools to create reusable code for solving data science problems.
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