I'm very excited to start R Programming and I hope you are too. This is the second course in the Data Science Specialization and it focuses on the nuts and bolts of using R as a programming language. As of now the course web site on Coursera is open and you are free to start watching lecture videos, take the quizzes, and look at the first programming assignment.
The recommended background for this course is the course The Data Scientist's Toolbox. It is possible to take this class concurrently with that class but you may have to read ahead in the prerequisite class to get the relevant background for this class. The good news is that we will offer all courses in the Specialization every month, so if you don't feel ready to take on R Programming this month it will start again in May! For a complete set of course dependencies in the Data Science Specialization please see the course dependency chart.
As you browse the course web site, please make sure to read through the syllabus which contains important information about the grading policy for quizzes and programming assignments as well as the course schedule.
The primary way to interact with me and the other students in this course is through the discussion forums. Here, you can start new threads by asking questions or you can respond to other people's questions. If you have a question about any aspect of the course, I strongly suggest that you search through the discussion boards first to see if anyone as already asked that question. If you see something similar to what you want to ask, you should up-vote that question using the up-arrow button rather than asking your question separately. The more votes a question or comment gets, the more likely it is that I will see it and be able to respond quickly. Of course, if you don't see a question similar to the one you want to ask, then you should definitely start a new thread on the appropriate forum.
This week will cover the basics to get you started up with R. There are videos demonstrating how to install R on Windows and Mac. The Week 1 videos will cover the history of R and S, go over the basic data types in R, and describe the functions for reading and writing data. I recommend that you watch the videos in the order that they are listed on the web page, but watching the videos out of order isn't going to ruin the story. For each lecture video you can download a separate PDF document of the slides (the demonstration videos don't have slides associated with them).
Watching the videos on the Coursera web site is the best way to watch the lectures. However, there are alternative ways to view the lectures if that suits you. You can download the lecture video files and watch them locally on your computer.
I hope you enjoy the class. I anticipate a fun four weeks!
Roger Peng and the Data Science Team
For Week 1 we have an experimental feature called Statistics with Interactive R Learning, or SWIRL. This is an R package designed to help you learn R by walking you through a series of interactive lessons. The lessons take place right on the R console and so you can learn and use R at the same time in the proper environment.
You can find the instructions for how to install and use SWIRL in the Programming Assignments section of the course under "Week 1". I encourage you to try the SWIRL modules as I think it's a fun way to learn R, especially if you've never seen it before. If you complete a module, the SWIRL package will (optionally) notify the Coursera web site to give you extra credit! You can receive 1 point for each completed module.
Please note, however, that the SWIRL modules are completely optional and you can get full marks in the class without completing them.
Roger Peng and the Data Science Team
Today marks the beginning of Week 2 of R Programming. This week we take the gloves off and the lectures cover key topics like control structures and functions. We also introduce the first programming assignment for the course, which is due at the end of the week.
A few notes about the Programming Assignment:
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Each part of the Assignment can be submitted an infinite number of times—there is no limit on the number of submissions.
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For each part, we take your maximum score over all of your submissions.
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There is a submission script that you will have to download to submit your assignment.
Roger Peng and the Data Science Team
We have now entered the third week of R Programming which also marks the halfway point. The lectures this week cover loop functions and the debugging tools in R. These aspects of R make R useful for both interactive work and writing longer code, and so they are commonly used in practice.
The Programming Assignment is challenging and so I encourage you to start early if you have the chance. It requires you to explore some of the more interesting aspects of the R language, including taking advantage of the scoping rules to implement state preservation in R objects.
Note that the programming assignment this week is implemented as a Peer Assessment so you will not see it listed under the Programming Assignments section. Please go to the Peer Assessment section of the course to find the assignment instructions. Also, for this assignment, you will need to setup your GitHub account if you have not yet done so.
Best of luck!
Roger Peng and the Data Science Team
Welcome to Week 4 of R Programming! In this final week there is a new Programming Assignment and we will focus on peer grading of last week's Programming Assignment.
Participating in peer grading is an amazing learning opportunity. It gives you a chance to learn things from your fellow students, pick up tips for explaining key ideas, and helping others to learn as well. We have focused our effort on making the rubric as objective and straightforward to implement as possible. If you have any issues please report them in the forums as described in the syllabus.
This week's Programming Assignment uses data on hospital mortality rates in the United States from the Center for Medicare and Medicaid Services. This a large database that is used to evaluate hospital quality in the U.S. We will be scratching the surface of this dataset to explore hospital ranking.
Thanks again for all of your efforts in the course, we are in the last stretch. Good luck and have a great week!
Roger Peng and the Data Science Team
Congratulations on finishing R Programming!
We have set the grading and released the Statements of Accomplishment for the Course. It might take a few days for the statements to be disbursed to accounts.
A couple of other notes:
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This course is the second course in the Data Science Specialization. The next course is Getting and Cleaning Data which focuses on how to obtain and clean data from the web and through APIs.
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The course will begin again immediately starting in a couple of days. If you are still interested in keeping in touch with your fellow learners, please enroll in the new course and keep the conversation going. You may also be an invaluable resource for new course takers!
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Keep your eye on Johns Hopkins offerings from Coursera. All announcements about future offerings will be posted at: https://twitter.com/jhubiostat and http://simplystatistics.org/, http://twitter.com/simplystats.
Thanks again for all of your efforts during the course of the class and best of luck in your career!
Roger Peng and the Data Science Team