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Programa especializado: Mastering Software Development in R
Build better data science tools.. Learn to design software for data tooling, distribute R packages, and build custom visualizations.
ofrecido por
Programa especializado: Mastering Software Development in RUniversidad Johns HopkinsQué aprenderás
Build R packages
Custom data visualization and graphics
Data manipulation and wrangling
Produce and scale data science products
Habilidades que obtendrás
Acerca de este Programa Especializado
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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.
Proyecto de aprendizaje aplicado
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.
Resultados profesionales del estudiante
45%
Comenzaste una nueva carrera profesional después de completar este programa especializado.
12%
Conseguiste un aumento de sueldo o ascenso.
Certificado para compartir
Obtén un certificado al finalizar
Cursos 100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Cronograma flexible
Establece y mantén fechas de entrega flexibles.
Nivel principiante
No se requiere experiencia previa.
Aprox. 6 meses para completar
Sugerido 4 horas/semana
Inglés (English)
Subtítulos: Inglés (English), Francés (French), Portugués (de Brasil), Ruso (Russian), Español (Spanish), Árabe (Arabic), Chino (simplificado), Italiano, Vietnamita, Coreano, Alemán (German), Turco (Turkish), Chino (Chinese), Georgiano, Estonio, Tailandés, Japonés, Nepalés
Resultados profesionales del estudiante
45%
Comenzaste una nueva carrera profesional después de completar este programa especializado.
12%
Conseguiste un aumento de sueldo o ascenso.
Certificado para compartir
Obtén un certificado al finalizar
Cursos 100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Cronograma flexible
Establece y mantén fechas de entrega flexibles.
Nivel principiante
No se requiere experiencia previa.
Aprox. 6 meses para completar
Sugerido 4 horas/semana
Inglés (English)
Subtítulos: Inglés (English), Francés (French), Portugués (de Brasil), Ruso (Russian), Español (Spanish), Árabe (Arabic), Chino (simplificado), Italiano, Vietnamita, Coreano, Alemán (German), Turco (Turkish), Chino (Chinese), Georgiano, Estonio, Tailandés, Japonés, Nepalés
Hay 5 cursos en este Programa Especializado
The R Programming Environment
1,053 calificaciones
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
516 calificaciones
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
203 calificaciones
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
146 calificaciones
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.
ofrecido por
Universidad Johns Hopkins
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.
Preguntas Frecuentes
¿Recibiré crédito universitario por completar el programa especializado?
Can I just enroll in a single course?
¿Puedo inscribirme en un solo curso?
Can I take the course for free?
¿Puedo tomar este curso de manera gratuita?
¿Este curso es 100 % en línea? ¿Necesito asistir a alguna clase en persona?
¿Cuánto tiempo se necesita para completar un programa especializado?
Do I need to take the courses in a specific order?
Will I earn university credit for completing the Specialization?
¿Recibiré crédito universitario por completar el programa especializado?
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