Computing for Data Analysis
Roger D. Peng
This course is about learning the fundamental computing skills necessary for effective data analysis. You will learn to program in R and to use R for reading data, writing functions, making informative graphs, and applying modern statistical methods.
Announcements
Week 2 Lectures Released
I'm excited that many of you have decided to tackle the programming assignment head on and it seems that many of you have already made substantial progress. I realize that the lectures for Week 1 don't completely cover the concepts you'll need to write the programming assignment (which is due at the end of Week 2), most notably the topic of functions. I was originally going to cover that in the second week but I've released the videos for that topic today so that they may be of use for the programming assignment.
Thank you all for the feedback so far. Please keep the discussions going in the forums.
Thank you all for the feedback so far. Please keep the discussions going in the forums.
Wed 26 Sep 2012 7:51:00 AM PDT
Computing for Data Analysis now Open
I'm very excited to start Computing for Data Analysis and I hope you are too. As of now the course web site on Coursera is open and you are free to start watching lecture videos, take the Week 1 quiz, and look at the first programming assignment.
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 teaching assistant 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 or the TA 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 and there are a few optional videos showing some more advanced aspects in case you are interested. 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 demo 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 MP4 files and watch them locally on your computer. Also, I have created a YouTube playlist for the Week 1 lectures athttp://goo.gl/8HBAS in case it is easier for you to watch the videos there.
I hope you enjoy the class. I anticipate a fun four weeks!
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 teaching assistant 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 or the TA 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 and there are a few optional videos showing some more advanced aspects in case you are interested. 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 demo 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 MP4 files and watch them locally on your computer. Also, I have created a YouTube playlist for the Week 1 lectures athttp://goo.gl/8HBAS in case it is easier for you to watch the videos there.
I hope you enjoy the class. I anticipate a fun four weeks!
Sun 23 Sep 2012 11:11:00 AM PDT
Welcome!
I want to welcome everyone to Computing for Data Analysis. I am delighted that so many people have taken an interest in learning statistical computing and R and am looking forward to working with everyone in the class.
Of course, this course is about the statistical programming language R and so you will need to install R on your computer if have not done so already. The main R web page is at http://www.r-project.org and it contains a lot of useful information. To download R and install it on your computer, you can get it at the Comprehensive R Archive Network (http://cran.r-project.org). There are videos in this week's set of lectures that explain how to install R on Windows and Mac machines (as well as how to build it from source).
One option that you may want to explore is RStudio (http://rstudio.org) which is a very nice front-end to R and works on all platforms. It is not required for the course, but it's a nice piece of software that some people may enjoy using.
You will need a text editor to edit R code and write your programming assignments. The Windows and Mac versions of R both come with a text editor (as does RStudio). They will be sufficient for the course. However, if you have a favorite text editor, you are welcome to use that too.
Of course, this course is about the statistical programming language R and so you will need to install R on your computer if have not done so already. The main R web page is at http://www.r-project.org and it contains a lot of useful information. To download R and install it on your computer, you can get it at the Comprehensive R Archive Network (http://cran.r-project.org). There are videos in this week's set of lectures that explain how to install R on Windows and Mac machines (as well as how to build it from source).
One option that you may want to explore is RStudio (http://rstudio.org) which is a very nice front-end to R and works on all platforms. It is not required for the course, but it's a nice piece of software that some people may enjoy using.
You will need a text editor to edit R code and write your programming assignments. The Windows and Mac versions of R both come with a text editor (as does RStudio). They will be sufficient for the course. However, if you have a favorite text editor, you are welcome to use that too.
Thu 20 Sep 2012 1:57:00 PM PDT
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