Informações sobre o curso
4.3
316 classificações
80 avaliações
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....
Globe

cursos 100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Calendar

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Intermediate Level

Nível intermediário

Clock

Approx. 11 hours to complete

Sugerido: 4 hours/week...
Comment Dots

English

Legendas: English, Chinese (Simplified)...

Habilidades que você terá

Logic ProgrammingR ProgrammingObject-Oriented Programming (OOP)Functional Programming
Globe

cursos 100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Calendar

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Intermediate Level

Nível intermediário

Clock

Approx. 11 hours to complete

Sugerido: 4 hours/week...
Comment Dots

English

Legendas: English, Chinese (Simplified)...

Programa - O que você aprenderá com este curso

Week
1
Clock
8 minutos para concluir

Welcome to Advanced R Programming

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....
Reading
1 vídeo (Total de 1 min), 3 leituras
Reading3 leituras
Syllabus1min
Course Textbook: Mastering Software Development in R1min
swirl Assignments5min
Clock
3 horas para concluir

Functions

This module begins with control structures in R for controlling the logical flow of an R program. We then move on to functions, their role in R programming, and some guidelines for writing good functions....
Reading
17 leituras, 1 teste
Reading17 leituras
Control Structures Overview2min
if-else10min
for Loops10min
Nested for loops10min
next, break10min
Summary2min
Functions Overview2min
Code10min
Function interface10min
Default values10min
Re-factoring code10min
Dependency Checking10min
Vectorization10min
Argument Checking10min
R package10min
When Should I Write a Function?10min
Summary2min
Quiz1 exercício prático
Swirl Lessonmin
Week
2
Clock
4 horas para concluir

Functional Programming

Functional programming is a key aspect of R and is one of R's differentiating factors as a data analysis language. Understanding the concepts of functional programming will help you to become a better data science software developer. In addition, we cover error and exception handling in R for writing robust code....
Reading
19 leituras, 1 teste
Reading19 leituras
What is Functional Programming?10min
Core Functional Programming Functions10min
Map10min
Reduce10min
Search10min
Filter10min
Compose10min
Partial Application10min
Side Effects10min
Recursion10min
Summary2min
Expressions10min
Environments10min
Execution Environments10min
What is an error?10min
Generating Errors10min
When to generate errors or warnings10min
How should errors be handled?10min
Summary2min
Quiz1 exercício prático
Swirl Lesson30min
Week
3
Clock
2 horas para concluir

Debugging and Profiling

Debugging tools are useful for analyzing your code when it exhibits unexpected behavior. We go through the various debugging tools in R and how they can be used to identify problems in code. Profiling tools allow you to see where your code spends its time and to optimize your code for maximum efficiency....
Reading
15 leituras, 1 teste
Reading15 leituras
Debugging Overview2min
traceback()10min
Browsing a Function Environment10min
Tracing Functions10min
Using debug() and debugonce()10min
recover()10min
Final Thoughts on Debugging10min
Summary2min
Profiling Overview2min
microbenchmark10min
profvis10min
Find out more10min
Summary2min
Non-standard evaluation10min
Summary2min
Quiz1 exercício prático
Debugging and Profiling30min
Week
4
Clock
5 horas para concluir

Object-Oriented Programming

Object oriented programming allows you to define custom data types or classes and a set of functions for handling that data type in a way that you define. R has a three different methods for implementing object oriented programming and we will cover them in this section....
Reading
11 leituras, 1 teste
Reading11 leituras
OOP Overview2min
Object Oriented Principles10min
S310min
S410min
Reference Classes10min
Summary2min
Overview2min
Reuse existing data structures10min
Compose simple functions with the pipe10min
Embrace functional programming10min
Design for humans10min
4.3

Melhores avaliações

por FZJun 7th 2017

Very useful, I considered myself quite an advanced R user, but this class raised the level, especially with the R as OOB part. Good investment if you are not a beginner.

por JYMay 8th 2017

It is a good course that forced me to understand the s3 and s4 object of R and have gained an appreciation of "methods belonging to functions not belonging to objects".

Instrutores

Roger D. Peng, PhD

Associate Professor, Biostatistics
Bloomberg School of Public Health

Brooke Anderson

Assistant Professor, Environmental & Radiological Health Sciences
Colorado State University

Sobre Johns Hopkins University

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....

Sobre o Programa de cursos integrados Mastering Software Development in R

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 useful data science results and products. You will obtain rigorous training in the R language, including the skills for handling complex data, building R packages and developing custom data visualizations. You will 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....
Mastering Software Development in R

Perguntas Frequentes – FAQ

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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