Save for later

Advanced R Programming

Mastering Software Development in R,

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.
Get Details and Enroll Now

OpenCourser is an affiliate partner of Coursera and may earn a commission when you buy through our links.

Get a Reminder

Send to:
Rating 3.9★ based on 105 ratings
Length 5 weeks
Starts Jul 3 (43 weeks ago)
Cost $49
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Brooke Anderson
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Data Analysis Software Development

Get a Reminder

Send to:

Similar Courses

What people are saying

object oriented programming

The Object oriented programming section did not provide an adequate amount of support for the assignment, compared to any of the other parts of the Course.

I did think there could more explanation given to object oriented programming R. Very useful, I considered myself quite an advanced R user, but this class raised the level, especially with the R as OOB part.

perfect The course has given me good insight into the functional and object oriented programming parts of R. Good Course!

Read more

complete the assignment

That meant it took a lot of time figuring out what exactly was needed to complete the assignment and how to do it.

Overall I'm satisfied but I would appreciate a bit more attention to detail in the learning materials there are many typos and general grammar issues that break the concentration and some times require the reader to stop and guess what is being said from context.I also believe the 4th week is lacking sufficient content for the learner to complete the assignment.

I find that some of the course materials are not sufficient for the learners to understand the concept in R programming and complete the assignment.

Read more

final project

The OOP portion of the final project was poorly constructed.

The final project was the only assignment in the course.

The final project is revised by the same students which in my opinion makes it more of a challenge.

Read more

too much

The assignments are poorly written and missing too much detail.

In fact, it is quite "too much" on talking, but lack of examples of codes and real problem solves.

I learned a lot, but I shouldn't have to do too much outside work to complete assignments for a class I paid for.

Read more

real world

I think a different type of real world object should be used, perhaps one that is not easily stored in a data frame.

This was really new for me and would love to have been able to see its application in real world examples to better cement the concepts.

Read more

peer review

the last peer review problem is much too hard for what I learned from the course material, if there is a more specific instruction for the assignment will be better.

The one thing that bothered me was the peer review system for assignments.

Read more

object-oriented programming

Object-oriented programming in R requires more than a single assignment to grasp- even at a superficial level.

A well-structured course on advanced and object-oriented programming.

The readings essentially say "Here is what object-oriented programming allows you to do [create/manipulate classes and objects]" but then goes back on itself by recommending that you do not use object-oriented programming to create custom classes or data structures because the R community already knows what data structures they like.

This did not help me by the time I had to complete the object-oriented programming portion of the final assignment.

Read more

Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

AD, Data Science $47k

Associate Data Science Supervisor $55k

Science writer / data analyst $63k

Genomic Data Science Programmer $75k

Volunteer Director of Data Science $78k

Expert Data Science Supervisor $79k

Supervisor 1 Data Science Supervisor $91k

Guest Director of Data Science $101k

Data Science Architect $105k

Head of Data Science $131k

Assistant Director 1 of Data Science $133k

Owner Director of Data Science $149k

Write a review

Your opinion matters. Tell us what you think.

Please login to leave a review
Login or register with your email instead.
Rating 3.9★ based on 105 ratings
Length 5 weeks
Starts Jul 3 (43 weeks ago)
Cost $49
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Brooke Anderson
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Data Analysis Software Development

Similar Courses

Sorted by relevance

Like this course?

Here's what to do next:

  • Save this course for later
  • Get more details from the course provider
  • Enroll in this course
Enroll Now

Special thanks to our sponsors