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Spécialisation Mastering Software Development in R
Build better data science tools.. Learn to design software for data tooling, distribute R packages, and build custom visualizations.
Offert par
Spécialisation Mastering Software Development in RUniversité Johns-HopkinsCe que vous allez apprendre
Build R packages
Custom data visualization and graphics
Data manipulation and wrangling
Produce and scale data science products
Compétences que vous acquerrez
À propos de ce Spécialisation
5,867 consultations récentes
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.
Projet d'apprentissage appliqué
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.
Résultats de carrière des étudiants
45 %
ont commencé une nouvelle carrière après avoir terminé ce spécialisation.
12 %
ont obtenu une augmentation de salaire ou une promotion.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
Cours en ligne à 100 %
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Planning flexible
Définissez et respectez des dates limites flexibles.
Niveau débutant
Aucune connaissance prérequise.
Approx. 6 mois pour terminer
4 heures/semaine recommandées
Anglais
Sous-titres : Anglais, Arabe, Français, Portugais (européen), Chinois (simplifié), Italien, Vietnamien, Coréen, Allemand, Russe, Turc, Espagnol, Géorgien, Estonien, Thaï, Japonais, Népalais
Résultats de carrière des étudiants
45 %
ont commencé une nouvelle carrière après avoir terminé ce spécialisation.
12 %
ont obtenu une augmentation de salaire ou une promotion.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
Cours en ligne à 100 %
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Planning flexible
Définissez et respectez des dates limites flexibles.
Niveau débutant
Aucune connaissance prérequise.
Approx. 6 mois pour terminer
4 heures/semaine recommandées
Anglais
Sous-titres : Anglais, Arabe, Français, Portugais (européen), Chinois (simplifié), Italien, Vietnamien, Coréen, Allemand, Russe, Turc, Espagnol, Géorgien, Estonien, Thaï, Japonais, Népalais
Cette Spécialisation compte 5 cours
The R Programming Environment
1,071 évaluations
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
522 évaluations
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
204 évaluations
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 évaluations
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.
Offert par
Université 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.
Foire Aux Questions
Puis-je obtenir des crédits universitaires si je réussis la Spécialisation ?
Can I just enroll in a single course?
Puis-je m'inscrire à un seul cours ?
Can I take the course for free?
Puis-je suivre le cours gratuitement ?
Ce cours est-il vraiment accessible en ligne à 100 % ? Dois-je assister à certaines activités en personne ?
Quelle est la durée nécessaire pour terminer la Spécialisation ?
Do I need to take the courses in a specific order?
Will I earn university credit for completing the Specialization?
Puis-je obtenir des crédits universitaires si je réussis la Spécialisation ?
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